PLS 803: From Binary Models to Duration Models
Shahryar Minhas
2025-09-30
1 From Binary to Duration: Building the Bridge
Binary models tell us whether events occur. Duration models tell us when. Political scientists started caring about “when” in the 1990s—around the time we discovered email and stopped using typewriters. Box-Steffensmeier and Jones (2004) wrote the book political scientists actually read, though economists had been doing this since Lancaster (1979) and engineers since the 1950s (they call it reliability analysis, we call it survival analysis, tomato tomahto).
1.1 Why Duration Models Matter in Political Science
Political science studies change and stability. When do governments fall? How long do peace agreements last? When do voters abandon incumbents? These aren’t “whether” questions—they’re “when” questions. It’s the difference between knowing the Titanic sank and knowing it took 2 hours and 40 minutes.
Consider three foundational studies that shaped modern comparative politics:
Przeworski et al. (2000): Their famous finding (no democracy with GDP per capita above $6,055 has ever died) only makes sense with duration analysis. Using logit would tell you rich democracies rarely fail, but not that Argentina’s democracy lasted 7 years while Switzerland’s has lasted 175+. The “democratic consolidation” literature depends entirely on understanding time-to-failure.
Fearon & Laitin (2003): Civil wars aren’t just about onset. They’re about duration. Ethnic wars don’t necessarily start more often, but they last longer (median 6 years vs. 2 for non-ethnic). Missing this duration dimension led earlier scholars to overstate ethnicity’s role in conflict.
Box-Steffensmeier & Zorn (2001): Congressional careers aren’t just about winning elections. They’re about accumulating seniority. A member serving 20 years has different power than ten members serving 2 years each. Committee chairs, legislative effectiveness, and pork barrel politics all depend on duration, not just occurrence.
1.2 Why Standard Models Fail for Duration Data
You’ve mastered OLS and binary choice models. Why learn something new? Because duration data breaks every assumption these models make.
1.2.1 The Three Fatal Problems
1. OLS Assumes the Wrong World
OLS needs normally distributed errors and unbounded outcomes. Duration data is neither:
- Bounded at zero: Governments can’t last -5 years (though Italian coalitions try)
- Right-skewed: Most fail quickly; few last forever (Japan’s LDP: 38 years, Italy’s average: 1.14 years)
- Heteroskedastic: Variance increases with duration (older democracies have more uncertain futures)
Using OLS is like using a map of Kansas to navigate the Alps—the tool assumes flat when reality isn’t.
2. Binary Models Throw Away Information
Logit/probit/cloglog tell you whether an event occurred, not when:
- Lost variation: Treating Weimar Germany (14 years) and modern Germany (75+ years) as “democracy = 1” ignores major differences
- Lost mechanisms: Did economic crisis cause immediate collapse (Thailand 1991) or slow decay (Venezuela 2000s)?
- Lost predictions: “50% chance of failure” means nothing without timeframe—this year? this decade? this century?
It’s like studying income by only asking “are you rich?”—you lose the entire distribution.
3. Censoring Breaks Everything
This is the killer. When studying regime duration 1945-2020:
- Complete durations: Democracies that failed (we know exact duration)
- Censored durations: Democracies still alive in 2020 (we know minimum duration only)
Standard approaches fail spectacularly:
- OLS with censored values: Treats Canada’s 153+ years as exactly 153 years → underestimates true durations
- OLS dropping censored: Only analyzes failed democracies → selection bias (like studying marriage by only interviewing divorced people)
- Logit with censoring: Can’t handle “partial” outcomes—democracy didn’t fail yet but might tomorrow
Real data is typically 30-50% censored. Ignoring this is statistical malpractice.
1.2.2 The Censoring Problem
Imagine you’re studying democratic regime survival from 1945 to 2020. You collect data on all democracies during this period. Some democracies have experienced breakdown (think Chile in 1973 or Turkey in 1980), while others are still democratic in 2020 (like Canada, a democracy since 1867, or Costa Rica, democratic since 1949).
For Canada, we know the democracy has lasted at least 153 years by 2020, but we don’t know when (or if) it will eventually fail. This is right censoring—we know the duration exceeds some value, but not the exact duration. If we simply used OLS, we’d have to either drop these censored observations (losing perhaps half our data and creating selection bias toward failed democracies) or treat the censored values as if they were complete (systematically underestimating true durations).
# create visual representation of censoring using real democracy data
# some democracies failed (complete durations), others still exist (censored)
# this is what duration models need to handle
set.seed(6886)
n_regimes <- 20
regime_data <- data.frame(
country = c("Chile", "Turkey", "Greece", "Argentina", "Brazil",
"Spain", "Portugal", "Uruguay", "Peru", "Ecuador",
"Canada", "Costa Rica", "UK", "Sweden", "Norway",
"Netherlands", "Denmark", "Switzerland", "Australia", "New Zealand"),
start_year = c(1970, 1973, 1974, 1983, 1985,
1978, 1976, 1985, 1980, 1979,
1867, 1949, 1945, 1945, 1945,
1945, 1945, 1945, 1945, 1945),
end_year = c(1973, 1980, 1974, 1987, 1993,
2020, 2020, 2020, 1992, 2000,
2020, 2020, 2020, 2020, 2020,
2020, 2020, 2020, 2020, 2020),
failed = c(rep(1, 10), rep(0, 10))
)
regime_data$duration <- regime_data$end_year - regime_data$start_year
# Visualization showing the censoring problem
ggplot(regime_data, aes(y = reorder(country, -start_year))) +
geom_segment(aes(x = start_year, xend = end_year, yend = country,
color = factor(failed)), size = 3) +
geom_point(aes(x = end_year, shape = factor(failed)), size = 4) +
scale_shape_manual(values = c(17, 16), labels = c("Censored", "Failed")) +
scale_color_manual(values = c("blue", "red"), labels = c("Still Democratic", "Failed")) +
labs(title = "The Censoring Problem in Democratic Survival Studies",
subtitle = "We observe when democracies fail, but ongoing democracies are 'censored' observations",
x = "Year", y = "",
color = "Status", shape = "Observation Type") +
theme_minimal() +
geom_vline(xintercept = 2020, linetype = "dashed", alpha = 0.5) +
annotate("text", x = 2020, y = 1, label = "Study ends\n(2020)", hjust = -0.1, size = 3) +
theme(legend.position = "bottom")
cli_h3("Censoring Problem Summary")
cli_li("Total democracies: {nrow(regime_data)}")
cli_li("Failed democracies: {sum(regime_data$failed)}")
cli_li("Censored (still democratic): {sum(1-regime_data$failed)}")
cli_li("Percentage censored: {round(mean(1-regime_data$failed)*100, 1)}%")This censoring problem is ubiquitous in political science. When studying war duration, ongoing conflicts are censored. When analyzing leader tenure, leaders still in power are censored. When examining the durability of international agreements, treaties still in force are censored. Duration models handle this censoring naturally, while OLS cannot.
1.2.3 The Distribution Problem: Why Normality Fails
Duration data breaks OLS assumptions. Durations can’t be negative (no government lasts -5 years, though watching Question Time might feel that way). The distribution is right-skewed—most governments fail quickly while a few hang on like Windows XP in government offices. The Japanese LDP governed for 38 straight years; the average Italian government lasts 1.14 years. Try fitting that with a normal distribution.
# Simulate typical political duration data
set.seed(42)
# War durations tend to follow Weibull distribution
war_durations <- rweibull(1000, shape = 1.5, scale = 10)
# Government durations might be log-normal
gov_durations <- rlnorm(1000, meanlog = 2, sdlog = 1)
# What OLS assumes vs. reality
# create data for all four comparisons
duration_comparison <- data.frame(
duration = c(war_durations,
rnorm(1000, mean = mean(war_durations), sd = sd(war_durations)),
gov_durations,
rnorm(1000, mean = mean(gov_durations), sd = sd(gov_durations))),
type = factor(c(rep("Actual War Durations", 1000),
rep("OLS Assumption (War)", 1000),
rep("Actual Government Durations", 1000),
rep("OLS Assumption (Government)", 1000)),
levels = c("Actual War Durations", "OLS Assumption (War)",
"Actual Government Durations", "OLS Assumption (Government)")),
category = rep(c("War", "War", "Government", "Government"), each = 1000)
)
# create comparison plot
ggplot(duration_comparison, aes(x = duration)) +
geom_histogram(bins = 30, alpha = 0.7) +
geom_vline(xintercept = 0, color = "red", linewidth = 1.2) +
facet_wrap(~type, ncol = 2, scales = "free") +
labs(x = "Duration", y = "Count",
title = "Why OLS Fails for Duration Data") +
theme_minimal()
OLS assumes normally distributed errors, which duration data violates. Log-transforming duration doesn’t solve the censoring problem and makes interpretation harder than explaining MySpace to Gen Z.
1.3 The Connection to Binary Models
Duration models connect directly to the binary models we’ve studied. Think of duration analysis as a sequence of binary choices:
- At time 1: Does the event occur? (\(Y_1 = 0\) or \(1\))
- At time 2 (if no event yet): Does the event occur? (\(Y_2 = 0\) or \(1\))
- At time 3 (if no event yet): Does the event occur? (\(Y_3 = 0\) or \(1\))
- … and so on
This perspective shows why the complementary log-log (cloglog) model from our binary models discussion matters—it arises when we observe a continuous-time process at discrete intervals.
1.4 What Makes Duration Data Special?
Duration data has unique features that require special treatment:
1.4.1 1. Censoring
We often don’t observe complete durations for all units:
- Right censoring: The event hasn’t occurred by the end of our observation period
- Example: Some democracies haven’t failed by 2024
- Left censoring: The process started before we began observing
- Example: We start observing an alliance that already existed
- Interval censoring: We know the event occurred within a time interval but not exactly when
- Example: A regime transition occurred sometime between two annual surveys
1.4.2 2. Time-Varying Covariates
Unlike binary models where covariates are typically fixed, duration models often incorporate variables that change over time:
- Economic growth rates change yearly
- International tensions fluctuate
- Leader characteristics change with succession
1.4.3 3. Duration Dependence
Duration dependence captures how risk evolves over time. The probability of an event often depends on how long the unit has already survived, reflecting underlying political processes.
New democracies illustrate negative duration dependence—they face highest breakdown risk immediately after transition when institutions remain untested. As democratic norms consolidate and actors adapt to new rules, failure probability decreases. Przeworski et al. (2000) showed this pattern empirically: democracies that survive their first decade rarely fail thereafter.
Conversely, interstate rivalries may show positive duration dependence. Initial disputes might be manageable, but accumulated grievances, arms races, and domestic pressures for action increase war probability over time. The hazard rises with duration.
Constant hazard means risk never changes—a 10-year-old democracy faces the same breakdown probability as a 1-year-old democracy. This almost never happens in politics. Even chronically unstable states show duration patterns. Thailand had 12 coups between 1932-1991, but they clustered in time, not randomly distributed like radioactive decay.
2 Core Concepts in Duration Analysis
2.1 The Survival Function: Who’s Still Standing?
The survival function \(S(t)\) answers the basic question: what fraction of units make it past time \(t\)? Formally:
\[S(t) = P(T > t)\]
where T is the random variable representing duration.
Think of the survival function as answering a simple question at each moment: “What proportion of units are still ‘alive’ (haven’t experienced the event yet)?” For democratic regimes, \(S(10) = 0.7\) means that 70% of democracies make it past their 10th birthday. \(S(50) = 0.2\) means only 20% of democracies survive beyond 50 years—a sobering reminder of democratic fragility.
The survival function has intuitive properties that match our understanding of political processes. It always starts at \(S(0) = 1\) because everyone begins “alive”: all governments start governing, all wars start ongoing, all leaders start in power. It eventually approaches \(S(\infty) = 0\) because theoretically, nothing lasts forever (though some Swiss cantons might disagree!). And it’s non-increasing because units can only fail once. You can’t “un-fail” and become more survived over time.
2.2 The Hazard Rate: The Risk at Any Moment
While the survival function tells us who’s still standing, the hazard rate \(h(t)\) tells us about immediate risk of failure for those who survived so far. This is the concept that pays the bills in duration analysis—it captures how political risk changes over time.
The hazard rate represents the instantaneous probability of failure at time t, conditional on having survived to that point:
\[h(t) = \lim_{\Delta t \to 0} \frac{P(t \leq T < t + \Delta t | T \geq t)}{\Delta t}\]
This looks intimidating, but the intuition is straightforward. The hazard is like asking: “For governments that have made it to year 10, what’s their risk of falling in the next instant?” It’s the political science equivalent of a medical researcher asking about the risk of heart attack for 60-year-olds who’ve been healthy so far.
The hazard shape tells you what kind of political process you’re dealing with. Three patterns show up repeatedly:
Different Hazard Patterns:
t <- seq(0, 10, 0.01)
# constant hazard (exponential)
h_constant <- rep(1, length(t))
# increasing hazard (Weibull with shape > 1)
h_increasing <- 2 * t
# decreasing hazard (Weibull with shape < 1)
h_decreasing <- 1 / sqrt(t + 0.1)
# bathtub hazard (common in engineering, sometimes in political science)
h_bathtub <- 2 * exp(-t) + 0.3 + 0.05 * t
hazard_df <- data.frame(
Time = rep(t, 4),
Hazard = c(h_constant, h_increasing, h_decreasing, h_bathtub),
Type = factor(rep(c("Constant", "Increasing", "Decreasing", "Bathtub"),
each = length(t)),
levels = c("Constant", "Increasing", "Decreasing", "Bathtub"))
)
ggplot(hazard_df, aes(x = Time, y = Hazard)) +
geom_line(linewidth = 1.2) +
facet_wrap(~Type, scales = "free_y") +
labs(title = "Different Hazard Rate Patterns in Political Science",
subtitle = "The shape of hazard reveals the underlying political process",
x = "Time",
y = "Hazard Rate $h(t)$") +
theme_minimal() +
theme(legend.position = "none")
Each hazard shape tells a different theoretical story. A constant hazard suggests that risk doesn’t change over time—the process is “memoryless.” This might describe coup risk in chronically unstable states where the probability of a coup next month is always the same regardless of how long it’s been since the last one.
An increasing hazard indicates growing risk over time, suggesting decay or wearing out. Peace agreements might follow this pattern: as grievances accumulate and memories of war fade, the risk of conflict resumption grows. Democratic backsliding might also exhibit increasing hazard as institutions gradually weaken.
A decreasing hazard reveals consolidation or learning. New democracies face their highest breakdown risk early (think of all the failed democratic transitions in Africa in the 1990s), but those that survive the initial period become increasingly stable. This matches Przeworski’s famous finding that no democracy with GDP per capita above $6,000 has ever failed.
The bathtub hazard (high early, low in the middle, high late) captures complex life cycles. Coalition governments might be vulnerable immediately after formation (while portfolios are contested) and near elections (when partners position for the next contest), but stable in between.
2.3 The Cumulative Hazard Function
The cumulative hazard \(H(t)\) accumulates instantaneous risk:
\[H(t) = \int_0^t h(u) du\]
2.4 Mathematical Relationships
The survival, hazard, density, and cumulative hazard functions are mathematically linked:
\[S(t) = \exp[-H(t)]\]
\[h(t) = -\frac{d\log S(t)}{dt} = \frac{f(t)}{S(t)}\]
\[f(t) = h(t) \cdot S(t) = -\frac{dS(t)}{dt}\]
Know one function, derive the others. Like the Rosetta Stone, but for survival analysis.
2.5 The Probability Density Function
For continuous durations, the PDF \(f(t)\) represents the unconditional probability of the event at time \(t\):
\[f(t) = h(t) \cdot S(t)\]
The probability of an event at time t depends on both:
- Surviving to time \(t\): \(S(t)\)
- The hazard at time \(t\) given survival: \(h(t)\)
3 The Connection to Binary Models: Discrete-Time Duration Models
3.1 From Continuous to Discrete Time
In practice, we often observe duration data in discrete time intervals (years, months, quarters). This is where our binary models knowledge becomes directly applicable!
Consider observing whether a government falls each year. For each government i in each year t it’s at risk, we observe:
\[Y_{it} = \begin{cases} 1 & \text{if government falls in year } t \\ 0 & \text{if government survives year } t \end{cases}\]
3.2 The Discrete-Time Hazard
The discrete-time hazard is:
\[h_{it} = P(Y_{it} = 1 | \text{survived to } t)\]
This is just a probability—exactly what we modeled with logit, probit, and cloglog!
3.3 Why Cloglog is Special for Duration
Remember the cloglog model from our binary discussion:
\[P(Y_{it} = 1) = 1 - \exp[-\exp(\alpha_t + X_{it}\beta)]\]
This has a special interpretation in duration analysis. If the continuous-time hazard is:
\[h(t) = \exp(\alpha + X\beta)\]
and we observe the process at discrete intervals, the discrete-time hazard follows a cloglog model!
The Magic: Cloglog preserves the proportional hazards assumption when moving from continuous to discrete time. Logit and probit don’t have this property.
3.4 Setting Up Discrete-Time Data
What we’re doing: Converting duration data (one row per government) into person-period format (one row per government per time period). This lets us use standard binary models (logit/probit/cloglog) for duration analysis.
Why this works: We’re modeling whether failure occurs in each period, conditional on surviving to that period. It’s like asking “Given you made it to year 5, what’s the probability you fail in year 5?”
To estimate discrete-time models, we need to restructure our data:
# transform duration data into person-period format for discrete-time analysis
# one row per government becomes one row per government per time period
# this lets us use standard glm (logit/probit/cloglog) for duration analysis
set.seed(6886)
n <- 200 # number of governments
max_time <- 10 # maximum observation period
# step 1: create government-level data (standard format)
gov_data <- data.frame(
id = 1:n,
democracy = rbinom(n, 1, 0.5),
gdp_growth = rnorm(n, 2, 1)
)
# simulate durations with Weibull baseline and covariate effects
# higher democracy and GDP growth increase survival
shape <- 1.5
scale <- exp(3 + 0.5 * gov_data$democracy + 0.1 * gov_data$gdp_growth)
gov_data$duration <- pmin(rweibull(n, shape, scale), max_time)
gov_data$censored <- as.numeric(gov_data$duration == max_time)
# show first few observations
cli_h3("Government-Level Data (Standard Format)")
head(gov_data)## id democracy gdp_growth duration censored
## 1 1 1 2.8137033 10 1
## 2 2 1 0.8355282 10 1
## 3 3 1 2.9709611 10 1
## 4 4 1 2.7134753 10 1
## 5 5 0 1.0033995 10 1
## 6 6 1 1.2722216 10 1# step 2: transform to person-period format
# each government gets one row for each period it was at risk
# event = 1 only in the period when failure occurs (if it occurs)
person_period_data <- gov_data %>%
group_by(id) %>%
reframe(
time = 1:ceiling(duration),
democracy = democracy,
gdp_growth = gdp_growth,
event = as.numeric(time == ceiling(duration) & censored == 0)
) %>%
filter(time <= ceiling(gov_data$duration[id]))
# show structure
cli_h3("Person-Period Data (Expanded Format)")
cli_text("Notice: Government 1 has multiple rows, one for each period at risk")
head(person_period_data, 15)## # A tibble: 15 × 5
## id time democracy gdp_growth event
## <int> <int> <int> <dbl> <dbl>
## 1 1 1 1 2.81 0
## 2 1 2 1 2.81 0
## 3 1 3 1 2.81 0
## 4 1 4 1 2.81 0
## 5 1 5 1 2.81 0
## 6 1 6 1 2.81 0
## 7 1 7 1 2.81 0
## 8 1 8 1 2.81 0
## 9 1 9 1 2.81 0
## 10 1 10 1 2.81 0
## 11 2 1 1 0.836 0
## 12 2 2 1 0.836 0
## 13 2 3 1 0.836 0
## 14 2 4 1 0.836 0
## 15 2 5 1 0.836 03.5 Estimating Discrete-Time Models
Once in person-period format, we can use our familiar binary models:
# logit model
dt_logit <- glm(event ~ factor(time) + democracy + gdp_growth,
data = person_period_data,
family = binomial(link = "logit"))
# cloglog model (theoretically preferred)
dt_cloglog <- glm(event ~ factor(time) + democracy + gdp_growth,
data = person_period_data,
family = binomial(link = "cloglog"))
# compare results
modelsummary(
list("Logit" = dt_logit, "Cloglog" = dt_cloglog),
estimate = "{estimate} ({std.error})",
statistic = NULL,
coef_omit = "factor\\(time\\)",
gof_map = c("nobs", "aic", "bic", "logLik"),
title = "Discrete-Time Duration Models: Logit vs Cloglog"
)| Logit | Cloglog | |
|---|---|---|
| (Intercept) | -3.926 (0.777) | -3.940 (0.771) |
| democracy | -1.484 (0.455) | -1.471 (0.452) |
| gdp_growth | -0.118 (0.169) | -0.114 (0.166) |
| Num.Obs. | 1869 | 1869 |
| AIC | 336.1 | 336.1 |
| BIC | 402.5 | 402.5 |
| Log.Lik. | -156.027 | -156.029 |
# calculate and compare average marginal effects
ame_logit <- avg_slopes(dt_logit,
variables = c("democracy", "gdp_growth"),
type = "response")
ame_cloglog <- avg_slopes(dt_cloglog,
variables = c("democracy", "gdp_growth"),
type = "response")
# combine AMEs for comparison
ame_comparison <- bind_rows(
ame_logit |> mutate(model = "Logit"),
ame_cloglog |> mutate(model = "Cloglog")
) |>
select(model, term, estimate, std.error, conf.low, conf.high)
print(ame_comparison)##
## Term Estimate Std. Error 2.5 % 97.5 %
## democracy -0.02166 0.00598 -0.03339 -0.00993
## gdp_growth -0.00202 0.00291 -0.00773 0.00369
## democracy -0.02166 0.00598 -0.03339 -0.00993
## gdp_growth -0.00199 0.00291 -0.00770 0.00372# visualize how hazard rates change over time for democracies vs non-democracies
# we need factor(time) because our model includes time dummies
# y-axis shows probability of failure in each period, conditional on survival
time_vals <- 1:10 # time periods to predict
dem_vals <- c(0, 1) # democracy values (0=autocracy, 1=democracy)
gdp_mean <- mean(person_period_data$gdp_growth) # hold GDP at mean
# create all combinations
pred_data <- expand.grid(
time = factor(time_vals),
democracy = dem_vals,
gdp_growth = gdp_mean
)
# get predictions from both models
pred_logit <- predict(dt_logit, newdata = pred_data, type = "response")
pred_cloglog <- predict(dt_cloglog, newdata = pred_data, type = "response")
# Combine into plotting data frame
hazard_predictions <- rbind(
data.frame(
time = rep(time_vals, 2),
democracy = rep(dem_vals, each = 10),
estimate = pred_logit,
model = "Logit"
),
data.frame(
time = rep(time_vals, 2),
democracy = rep(dem_vals, each = 10),
estimate = pred_cloglog,
model = "Cloglog"
)
)
# check actual range of predictions for appropriate y-axis
cli_text("Hazard rate range: [{round(min(hazard_predictions$estimate), 3)}, {round(max(hazard_predictions$estimate), 3)}]")
# verify data structure
cli_h3("Data Check for Hazard Predictions Plot")
cli_text("Number of predictions: {nrow(hazard_predictions)}")
cli_text("Models included: {paste(unique(hazard_predictions$model), collapse=', ')}")
cli_text("Democracy values: {paste(unique(hazard_predictions$democracy), collapse=', ')}")
cli_text("Time periods: {paste(range(hazard_predictions$time), collapse=' to ')}")
# plot baseline hazard comparison
ggplot(hazard_predictions,
aes(x = time, y = estimate,
linetype = factor(democracy),
group = interaction(model, democracy))) +
geom_line(linewidth = 1.2) +
facet_wrap(~model) +
labs(title = "Predicted Hazard Rates Over Time",
subtitle = "Probability of government failure in each time period",
x = "Time Period",
y = "Hazard Probability (Pr of failure)",
linetype = "Democracy") +
theme_minimal() +
scale_linetype_discrete(labels = c("0" = "Non-Democracy", "1" = "Democracy")) +
scale_y_continuous(limits = c(0, max(hazard_predictions$estimate) * 1.1))
3.5.1 Interpreting Discrete-Time Results
Reading the Plot: - Y-axis: Hazard rate = Pr(fail in period t | survived to t) - X-axis: Time periods since government formation - Lines: Different for democracy=0 vs democracy=1 - Panels: Logit (left) vs Cloglog (right) link functions
What We Learn:
- Democracy’s Protective Effect
- Solid line (non-democracy) consistently above dashed line (democracy)
- Interpretation: At any given time, democracies less likely to fail
- Magnitude: Democracy reduces hazard by ~50% (exp(β) ≈ 0.5)
- Real world: This is why Sweden has had 1 constitutional crisis since 1945 while Thailand has had 19 coups
- Duration Dependence
- Hazard changes over time (not flat)
- Pattern suggests “liability of newness”—young governments most vulnerable
- After surviving initial periods, risk stabilizes
- Connects to Linz’s (1978) consolidation theory: democracies that survive early challenges become increasingly stable
- Model Comparison
- Logit and cloglog give similar predictions here
- Cloglog theoretically correct when continuous process observed at discrete intervals
- Rule of thumb: Use cloglog for duration data, logit for truly discrete processes
- In practice: If results differ substantially, you have bigger problems
Substantive Magnitude: Let’s translate statistics to politics. A democracy with hazard rate 0.05 vs non-democracy at 0.10: - After 1 year: 95% vs 90% survival (5 percentage point difference) - After 5 years: 77% vs 59% survival (18 percentage point difference) - After 10 years: 60% vs 35% survival (25 percentage point difference)
This compounds—small period-by-period advantages create large long-term differences. It’s why consolidated democracies seem immortal while autocracies face constant coup risk.
4 Continuous-Time Parametric Models
While discrete-time models are intuitive, continuous-time models are often more appropriate when: - The exact timing matters - Events can occur at any moment - We want to make assumptions about the hazard shape
4.1 Proportional Hazards (PH) vs Accelerated Failure Time (AFT)
Parametric models can be formulated in two frameworks:
Proportional Hazards (PH): \[h(t|X_i) = h_0(t) \exp(X_i\beta)\]
- Covariates multiplicatively shift the hazard
- \(\exp(\beta)\) = hazard ratio
- Baseline hazard shape unchanged by covariates
Accelerated Failure Time (AFT): \[T_i = T_0 \exp(\gamma X_i) \epsilon_i\]
- Covariates accelerate or decelerate time
- \(\exp(\gamma)\) = time ratio
- Log-linear model for duration
PH models how covariates affect instantaneous risk; AFT models how covariates stretch or compress the time scale. Some distributions (Weibull, exponential) have both PH and AFT representations; others (Gompertz) are PH only; still others (log-normal, log-logistic) are AFT only. It’s like Pokémon types—each has its strengths.
4.2 The Exponential Model
The exponential model assumes a constant hazard over time:
\[h(t) = \lambda\]
where \(\lambda > 0\).
Mathematical properties:
- Memoryless: \(P(T > s + t | T > s) = P(T > t)\)
- Survival function: \(S(t) = \exp(-\lambda t)\)
- Mean duration: \(E(T) = 1/\lambda\)
Political science applications:
The exponential rarely fits political data well—most political processes exhibit duration dependence. Olson and Zeckhauser (1966) initially used exponential models for alliance duration, but subsequent work showed this was too restrictive. The memoryless property implies that a 50-year-old democracy faces the same breakdown risk as a 1-year-old democracy, which contradicts everything we know about democratic consolidation (Gasiorowski 1995).
The model occasionally works for truly random processes. Goemans (2000) found that battle deaths in interstate wars approximate a Poisson process, making the exponential reasonable for modeling time between major battles. Similarly, terrorist attacks in unstable regions sometimes follow exponential waiting times between events (Clauset and Gleditsch 2012).
4.3 The Weibull Model
The Weibull generalizes the exponential by allowing monotonic duration dependence:
\[h(t) = \lambda p t^{p-1}\]
where \(\lambda > 0\) is the scale and \(p > 0\) is the shape parameter.
Shape parameter interpretation:
- \(p = 1\): Reduces to exponential (no duration dependence)
- \(p > 1\): Increasing hazard (positive duration dependence)
- \(p < 1\): Decreasing hazard (negative duration dependence)
Political science applications:
Weibull models dominate political duration analysis because monotonic hazards fit many processes. Warwick (1992) used Weibull models to show that parliamentary governments face increasing hazard over time (\(p > 1\)), as coalition tensions accumulate and elections approach. The shape parameter itself becomes theoretically interesting—Diermeier and Stevenson (1999) found \(p \approx 1.5\) across European parliaments, suggesting common institutional dynamics.
For democratization, Weibull models with \(p < 1\) capture the “liability of newness”—young democracies face highest breakdown risk immediately after transition, then stabilize over time (Svolik 2008). The estimated \(p\) provides a direct test of consolidation theories.
# fit Weibull model using survival package
library(survival)
# create survival object
surv_obj <- Surv(time = gov_data$duration,
event = 1 - gov_data$censored)
# fit Weibull model
weibull_fit <- survreg(surv_obj ~ democracy + gdp_growth,
data = gov_data,
dist = "weibull")
summary(weibull_fit)##
## Call:
## survreg(formula = surv_obj ~ democracy + gdp_growth, data = gov_data,
## dist = "weibull")
## Value Std. Error z p
## (Intercept) 3.0344 0.2884 10.52 <2e-16
## democracy 1.0584 0.3680 2.88 0.004
## gdp_growth 0.0826 0.1201 0.69 0.492
## Log(scale) -0.3284 0.1688 -1.95 0.052
##
## Scale= 0.72
##
## Weibull distribution
## Loglik(model)= -157.2 Loglik(intercept only)= -164.5
## Chisq= 14.48 on 2 degrees of freedom, p= 0.00072
## Number of Newton-Raphson Iterations: 8
## n= 200# note: survreg uses AFT parameterization
# extract key parameters for interpretation
shape_param <- 1/weibull_fit$scale
aft_coefs <- coef(weibull_fit)[-1]
time_ratios <- exp(aft_coefs)
# create summary table
aft_se <- sqrt(diag(vcov(weibull_fit)))[2:3] # extract SEs for democracy and gdp_growth only
weibull_summary <- data.frame(
Variable = names(aft_coefs),
AFT_Coef = as.numeric(aft_coefs),
Time_Ratio = as.numeric(time_ratios),
SE = as.numeric(aft_se),
p_value = 2 * pnorm(-abs(aft_coefs/aft_se)),
row.names = NULL
)
weibull_summary[, -1] <- round(weibull_summary[, -1], 3)
print(weibull_summary)## Variable AFT_Coef Time_Ratio SE p_value
## 1 democracy 1.058 2.882 0.368 0.004
## 2 gdp_growth 0.083 1.086 0.120 0.492# test for duration dependence
shape_se <- shape_param^2 * weibull_fit$scale[1]
z_shape <- (shape_param - 1) / shape_se
p_shape <- 2 * pnorm(-abs(z_shape))
# use marginaleffects for predictions
# create scenarios for prediction
scenarios_weibull <- datagrid(
democracy = c(0, 1),
gdp_growth = c(0, 2, 4),
model = weibull_fit
)
# get predicted survival times
pred_times <- predict(weibull_fit, newdata = scenarios_weibull, type = "response")
scenarios_weibull$predicted_duration <- pred_times
# display predicted durations
cli_h3("Predicted Mean Durations")
print(scenarios_weibull[, c("democracy", "gdp_growth", "predicted_duration")])## democracy gdp_growth predicted_duration
## 1 0 0 20.78927
## 2 0 2 24.52134
## 3 0 4 28.92339
## 4 1 0 59.90750
## 5 1 2 70.66205
## 6 1 4 83.34724# create survival curves for visualization
# note: survreg models need different approach for survival curves
time_grid <- seq(0.1, 10, 0.5)
plot_data <- list()
for(i in 1:nrow(scenarios_weibull)) {
# predict quantiles from survreg model
pred_quantiles <- predict(weibull_fit,
newdata = scenarios_weibull[i,],
type = "quantile",
p = 1 - pweibull(time_grid,
shape = 1/weibull_fit$scale,
scale = exp(predict(weibull_fit,
newdata = scenarios_weibull[i,],
type = "lp"))))
# calculate survival probabilities
lp <- predict(weibull_fit, newdata = scenarios_weibull[i,], type = "lp")
surv_probs <- 1 - pweibull(time_grid,
shape = 1/weibull_fit$scale,
scale = exp(lp))
plot_data[[i]] <- data.frame(
time = time_grid,
survival = surv_probs,
democracy = scenarios_weibull$democracy[i],
gdp_growth = scenarios_weibull$gdp_growth[i],
group = paste0("Dem=", scenarios_weibull$democracy[i],
", Growth=", scenarios_weibull$gdp_growth[i])
)
}
plot_df <- do.call(rbind, plot_data)
# create enhanced visualization
ggplot(plot_df, aes(x = time, y = survival, color = group)) +
geom_line(linewidth = 1.2) +
labs(title = "Weibull Model: Predicted Survival Curves",
subtitle = paste("Shape parameter =", round(1/weibull_fit$scale, 2),
"indicating", ifelse(1/weibull_fit$scale > 1,
"increasing hazard",
"decreasing hazard")),
x = "Time (years)",
y = "Survival Probability",
color = "Scenario") +
theme_minimal() +
facet_wrap(~democracy, labeller = labeller(democracy = c("0" = "Non-Democracy",
"1" = "Democracy")))
4.3.1 Interpreting the Weibull Results
The Weibull model uses the accelerated failure time (AFT) parameterization in R’s survreg(), where positive coefficients indicate longer survival. The democracy coefficient of 1.058 translates to a time ratio of 2.88—democracies survive nearly three times longer than non-democracies. Converting to hazard ratios for comparison with Cox models, democracy reduces the hazard by approximately 80% (HR ≈ 0.20, p = 0.004).
The shape parameter of 1.39 indicates positive duration dependence—the hazard increases over time. This finding, statistically different from 1 (p < 0.05), rejects the exponential model’s constant hazard assumption. Governments become increasingly vulnerable the longer they survive, consistent with coalition fatigue or electoral cycle pressures. The predicted durations show democracies lasting approximately 50 years under average conditions, compared to 17 years for non-democracies.
4.4 The Log-Normal and Log-Logistic Models
Not all political processes exhibit monotonic hazards. Some peak then decline.
Log-Normal:
The log-normal hazard rises initially then falls, capturing “honeymoon” effects. Alt and King (1994) showed cabinet hazard peaks around 18 months—early enough for reshuffles, late enough for accumulated tensions, but declining afterward as survivors approach scheduled elections. This non-monotonic pattern would be missed by Weibull models.
Log-Logistic:
The log-logistic allows both non-monotonic and monotonic decreasing hazards, with the advantage that odds ratios have closed-form solutions. Beck, Katz, and Tucker (1998) demonstrated that log-logistic models often fit interstate conflict data better than Weibull, particularly for modeling peace spells between wars where hazard peaks during power transitions then declines.
# fit log-normal model
lognormal_fit <- survreg(surv_obj ~ democracy + gdp_growth,
data = gov_data,
dist = "lognormal")
# fit log-logistic model
loglogistic_fit <- survreg(surv_obj ~ democracy + gdp_growth,
data = gov_data,
dist = "loglogistic")
# compare models
modelsummary(
list("Weibull" = weibull_fit,
"Log-Normal" = lognormal_fit,
"Log-Logistic" = loglogistic_fit),
estimate = "{estimate} ({std.error})",
statistic = NULL,
gof_map = c("nobs", "aic", "bic", "logLik"),
title = "Parametric Duration Models Comparison"
)| Weibull | Log-Normal | Log-Logistic | |
|---|---|---|---|
| (Intercept) | 3.034 (0.288) | 2.968 (0.367) | 2.850 (0.303) |
| democracy | 1.058 (0.368) | 1.221 (0.361) | 1.079 (0.361) |
| gdp_growth | 0.083 (0.120) | 0.144 (0.152) | 0.098 (0.131) |
| Log(scale) | -0.328 (0.169) | 0.337 (0.144) | -0.389 (0.165) |
| Num.Obs. | 200 | 200 | 200 |
| AIC | 322.5 | 322.3 | 322.2 |
| BIC | 335.7 | 335.5 | 335.4 |
# compare using AIC
models <- list(
Exponential = survreg(surv_obj ~ democracy + gdp_growth,
data = gov_data, dist = "exponential"),
Weibull = weibull_fit,
LogNormal = lognormal_fit,
LogLogistic = loglogistic_fit
)
# Model comparison metrics
aic_comparison <- data.frame(
Model = names(models),
AIC = sapply(models, AIC),
BIC = sapply(models, BIC),
LogLik = sapply(models, logLik)
)
# add delta AIC (difference from best model)
aic_comparison$DeltaAIC <- aic_comparison$AIC - min(aic_comparison$AIC)
aic_comparison <- aic_comparison[order(aic_comparison$AIC),]
print(aic_comparison)## Model AIC BIC LogLik DeltaAIC
## LogLogistic LogLogistic 322.2348 335.4281 -157.1174 0.00000000
## LogNormal LogNormal 322.3104 335.5037 -157.1552 0.07559628
## Weibull Weibull 322.4682 335.6615 -157.2341 0.23337607
## Exponential Exponential 323.8566 333.7515 -158.9283 1.62175815cli_h3("Model Selection Guidance")
cli_li("DeltaAIC < 2: Substantial support")
cli_li("DeltaAIC 4-7: Less support")
cli_li("DeltaAIC > 10: No support")
cli_alert_success("Best model: {aic_comparison$Model[1]}")
# Substantive interpretation of model comparison
cli_h2("What the Model Comparison Tells Us")
best_model <- aic_comparison$Model[1]
if(best_model == "Exponential") {
cli_alert_info("The exponential model fits best, suggesting:")
cli_li("Constant hazard over time (memoryless process)")
cli_li("Government failure risk doesn't depend on survival time")
cli_li("Unusual for political processes - check your data")
} else if(best_model == "Weibull") {
shape_param <- 1/weibull_fit$scale
cli_alert_info("The Weibull model fits best, suggesting:")
if(shape_param > 1) {
cli_li(sprintf("Increasing hazard over time (shape = %.2f > 1)", shape_param))
cli_li("Governments become more vulnerable over time")
cli_li("Consistent with coalition fatigue or electoral cycles")
} else {
cli_li(sprintf("Decreasing hazard over time (shape = %.2f < 1)", shape_param))
cli_li("Governments consolidate power over time")
cli_li("Consistent with democratic consolidation theories")
}
} else if(best_model == "LogNormal" || best_model == "LogLogistic") {
cli_alert_info(sprintf("The %s model fits best, suggesting:", best_model))
cli_li("Non-monotonic hazard (increases then decreases)")
cli_li("Highest risk at intermediate durations")
cli_li("Consistent with honeymoon followed by consolidation")
}
# compare key coefficients across models
cli_h3("Democracy Effect Across Parametric Models")
for(model_name in names(models)) {
coef_dem <- coef(models[[model_name]])["democracy"]
cat(sprintf(" %s: Time ratio = %.3f\n", model_name, exp(coef_dem)))
}## Exponential: Time ratio = 4.271
## Weibull: Time ratio = 2.882
## LogNormal: Time ratio = 3.391
## LogLogistic: Time ratio = 2.940cli_li("Consistent direction across models increases confidence")5 The Cox Proportional Hazards Model
5.1 Motivation
Parametric models force us to specify the baseline hazard shape. When theory provides no guidance about functional form—or when we want to remain agnostic—the Cox model sidesteps this issue entirely.
5.2 The Model
The Cox model specifies:
\[h(t|X_i) = h_0(t) \exp(X_i\beta)\]
where: - \(h_0(t)\) is the baseline hazard (left unspecified) - \(\exp(X_i\beta)\) is the relative hazard
Key Assumption: Proportional hazards—the ratio of hazards for any two individuals is constant over time:
\[\frac{h(t|X_i)}{h(t|X_j)} = \frac{\exp(X_i\beta)}{\exp(X_j\beta)}\]
This ratio does not depend on \(t\).
5.3 Partial Likelihood
Cox’s breakthrough was showing we can estimate \(\beta\) without specifying \(h_0(t)\). The partial likelihood conditions on the observed failure times and considers only the ordering of events:
\[L(\beta) = \prod_{j=1}^d \frac{\exp(X_j\beta)}{\sum_{l \in R_j} \exp(X_l\beta)}\]
where: - \(d\) = number of events - \(R_j\) = risk set at time \(t_j\) (all units still at risk) - \(X_j\) = covariates for unit failing at \(t_j\)
Interpretation: Each term is the conditional probability that unit \(j\) fails at time \(t_j\), given that one unit fails and the risk set \(R_j\). The baseline hazard \(h_0(t)\) cancels out in the ratio.
Properties:
- Not a true likelihood (ignores time intervals between events)
- But has standard asymptotic properties
- Robust to misspecification of baseline hazard
# estimate a cox proportional hazards model - the workhorse of duration analysis
# no need to specify baseline hazard shape (it's a "nuisance" parameter)
# coefficients are log hazard ratios (exponentiate for hazard ratios)
# first create the survival object (time + censoring indicator)
surv_obj <- Surv(time = gov_data$duration,
event = 1 - gov_data$censored)
# fit Cox model
cox_fit <- coxph(surv_obj ~ democracy + gdp_growth,
data = gov_data)
# display model summary
summary(cox_fit)## Call:
## coxph(formula = surv_obj ~ democracy + gdp_growth, data = gov_data)
##
## n= 200, number of events= 33
##
## coef exp(coef) se(coef) z Pr(>|z|)
## democracy -1.4672 0.2306 0.4519 -3.247 0.00117 **
## gdp_growth -0.1139 0.8924 0.1660 -0.686 0.49275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## democracy 0.2306 4.337 0.0951 0.559
## gdp_growth 0.8924 1.121 0.6446 1.235
##
## Concordance= 0.676 (se = 0.041 )
## Likelihood ratio test= 14.41 on 2 df, p=7e-04
## Wald test = 11.22 on 2 df, p=0.004
## Score (logrank) test = 13.33 on 2 df, p=0.001# extract hazard ratios with confidence intervals
hr_table <- exp(cbind(coef(cox_fit), confint(cox_fit)))
colnames(hr_table) <- c("HR", "Lower_95", "Upper_95")
print(round(hr_table, 3))## HR Lower_95 Upper_95
## democracy 0.231 0.095 0.559
## gdp_growth 0.892 0.645 1.235# test proportional hazards assumption
cli_h3("Testing Proportional Hazards Assumption")
cli_text("The Cox model assumes the effect of covariates is constant over time.")
cli_text("If this assumption holds, the coefficient for democracy should not vary with time.")
ph_test <- cox.zph(cox_fit)
print(ph_test)## chisq df p
## democracy 0.682 1 0.41
## gdp_growth 0.130 1 0.72
## GLOBAL 0.787 2 0.67# Explain the test
if(ph_test$table["democracy", "p"] > 0.05) {
cli_alert_success("Democracy p-value = {round(ph_test$table['democracy', 'p'], 3)} > 0.05: PH assumption holds")
} else {
cli_alert_warning("Democracy p-value = {round(ph_test$table['democracy', 'p'], 3)} < 0.05: PH assumption may be violated")
}
# Plot Schoenfeld residuals to visualize the test
# extract the residuals and create a ggplot
cli_text("Visual check: Points scattered around zero support the PH assumption.")
cli_text("Systematic patterns suggest time-varying effects.")
# create data frame with schoenfeld residuals
schoenfeld_df <- data.frame(
time = ph_test$x, # Event times
residual = ph_test$y[, "democracy"] # Schoenfeld residuals for democracy
)
# show what we're testing
cli_text("Number of event times in test: {length(ph_test$x)}")
cli_text("Residual range: [{round(min(schoenfeld_df$residual), 3)}, {round(max(schoenfeld_df$residual), 3)}]")
# create the plot
ggplot(schoenfeld_df, aes(x = time, y = residual)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "loess", se = TRUE, color = "blue", alpha = 0.3) +
geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
labs(title = "Test of Proportional Hazards: Democracy",
subtitle = "Points should scatter randomly around zero if assumption holds",
x = "Time (years)",
y = "Schoenfeld Residuals",
caption = paste("p-value =", round(ph_test$table["democracy", "p"], 3))) +
theme_minimal()
# Interpretation of PH test
cli_h3("Understanding the Proportional Hazards Test")
cli_text("The plot shows Schoenfeld residuals—the difference between observed and expected covariate values at each failure time.")
cli_text("• Flat trend (horizontal): Democracy's effect is constant over time ✓")
cli_text("• Upward trend: Democracy's effect strengthens over time")
cli_text("• Downward trend: Democracy's effect weakens over time")
cli_text("Real-world meaning: If PH holds, being a democracy reduces failure risk by the same percentage whether it's year 1 or year 10.")
# predict survival probabilities for different scenarios
scenarios <- datagrid(
democracy = c(0, 1),
gdp_growth = c(0, 2, 4),
model = cox_fit
)
# get survival predictions at specific times
surv_times <- c(1, 3, 5, 7, 10)
cox_predictions <- list()
for(i in 1:nrow(scenarios)) {
surv_fit <- survfit(cox_fit, newdata = scenarios[i,])
surv_probs <- summary(surv_fit, times = surv_times)$surv
cox_predictions[[i]] <- data.frame(
democracy = scenarios$democracy[i],
gdp_growth = scenarios$gdp_growth[i],
time = surv_times,
survival = surv_probs
)
}
cox_pred_df <- do.call(rbind, cox_predictions)
# plot survival curves
ggplot(cox_pred_df,
aes(x = time, y = survival,
color = factor(democracy),
linetype = factor(gdp_growth))) +
geom_line(size = 1.2) +
geom_point() +
labs(title = "Cox Model: Predicted Survival Probabilities",
subtitle = "By democracy status and GDP growth levels",
x = "Time",
y = "Survival Probability",
color = "Democracy",
linetype = "GDP Growth") +
theme_minimal() +
scale_color_manual(values = c("0" = "red", "1" = "blue"),
labels = c("0" = "Non-Democracy", "1" = "Democracy")) +
scale_linetype_discrete(labels = c("0" = "Low", "2" = "Medium", "4" = "High"))
5.3.1 Interpreting the Cox Model
The Cox model results show democracy reduces the hazard of failure. With a hazard ratio of 0.231 (95% CI: 0.095-0.559), democratic governments face about 77% lower risk of failure at any given time compared to non-democracies (p = 0.001).
GDP growth shows a modest protective effect (HR = 0.892), suggesting each 1% increase in growth reduces failure risk by about 11%, though this doesn’t reach conventional significance levels (p = 0.493). The concordance of 0.676 indicates reasonable discrimination between high and low-risk observations.
The proportional hazards test reveals no significant violations (global test p > 0.05), supporting the model’s key assumption that relative hazards remain constant over time. The Schoenfeld residual plots show no systematic patterns, further validating this assumption. These diagnostics give us confidence in the hazard ratio interpretations.
6 Cabinet Duration: A Complete Political Science Example
To see how duration models work in practice, let’s analyze a question central to comparative politics: what determines how long coalition governments survive? This example will demonstrate the complete workflow that political scientists use, from data setup through publication-ready results.
6.1 Setting Up the Analysis
Everyone cites King et al. (1990) on coalition duration, so we will too. The puzzle: coalitions need to balance multiple parties, manage ideological differences, and survive both internal fights and external shocks. Here’s simulated data that looks like real coalition governments:
# generate realistic cabinet duration data
set.seed(12345)
n_cabinets <- 500
# generate covariates common in government duration studies
cabinet_data <- data.frame(
cabinet_id = 1:n_cabinets,
# Coalition vs single-party (40% are coalitions)
coalition = factor(rbinom(n_cabinets, 1, 0.4),
labels = c("Single-party", "Coalition")),
# Majority vs minority government
majority = factor(rbinom(n_cabinets, 1, 0.6),
labels = c("Minority", "Majority")),
# Economic growth rate (normally distributed around 2%)
econ_growth = rnorm(n_cabinets, 2, 2),
# Political polarization index (0-1 scale)
polarization = runif(n_cabinets, 0, 1),
# Post-election honeymoon period (30% of cabinets)
post_election = factor(rbinom(n_cabinets, 1, 0.3),
labels = c("No", "Yes")),
# External crisis (war, pandemic, etc.)
crisis = factor(rbinom(n_cabinets, 1, 0.2),
labels = c("No", "Yes"))
)
# generate durations using weibull with realistic parameters
# based on empirical patterns from warwick (1994)
linear_predictor <- 3 +
-0.5 * (cabinet_data$coalition == "Coalition") +
0.8 * (cabinet_data$majority == "Majority") +
0.1 * cabinet_data$econ_growth +
-1.2 * cabinet_data$polarization +
0.4 * (cabinet_data$post_election == "Yes") +
-0.6 * (cabinet_data$crisis == "Yes")
# generate durations with weibull shape = 1.5 (increasing hazard)
true_scale <- exp(linear_predictor)
true_shape <- 1.5
durations <- rweibull(n_cabinets, shape = true_shape, scale = true_scale)
# add realistic censoring (some cabinets still in power at study end)
study_end <- 15 # 15 years of observation
cabinet_data$duration <- pmin(durations, study_end)
cabinet_data$failed <- as.numeric(durations <= study_end)
# summary statistics that match real-world patterns
cli_h2("Cabinet Duration Data Summary")
cli_li("Number of cabinets: {n_cabinets}")
cli_li("Failed (government fell): {sum(cabinet_data$failed)}")
cli_li("Censored (still governing): {sum(1 - cabinet_data$failed)}")
cli_li("Median duration (failed only): {round(median(cabinet_data$duration[cabinet_data$failed == 1]), 2)} years")
cli_li("Mean duration (all): {round(mean(cabinet_data$duration), 2)} years")
# create descriptive table
if(requireNamespace("modelsummary", quietly = TRUE)) {
datasummary(coalition + majority + econ_growth + polarization +
post_election + crisis ~ Mean + SD + Min + Max,
data = cabinet_data,
title = "Table 1: Descriptive Statistics for Cabinet Duration Analysis")
} else {
# Simple alternative
summary(cabinet_data[, c("coalition", "majority", "econ_growth",
"polarization", "post_election", "crisis")])
}| Mean | SD | Min | Max | ||
|---|---|---|---|---|---|
| coalition | Single-party | ||||
| Coalition | |||||
| majority | Minority | ||||
| Majority | |||||
| econ_growth | 2.02 | 2.01 | -3.56 | 8.66 | |
| polarization | 0.49 | 0.29 | 0.00 | 1.00 | |
| post_election | No | ||||
| Yes | |||||
| crisis | No | ||||
| Yes |
6.2 Kaplan-Meier Analysis: The Non-Parametric Foundation
Before diving into regression models, political scientists typically begin with Kaplan-Meier curves to visualize survival patterns. This non-parametric approach makes no assumptions about the underlying hazard and provides our first look at the data:
# create survival object
surv_obj <- Surv(time = cabinet_data$duration,
event = cabinet_data$failed)
# Overall survival curve
km_overall <- survfit(surv_obj ~ 1, data = cabinet_data)
# By coalition status - the key comparison
km_coalition <- survfit(surv_obj ~ coalition, data = cabinet_data)
# create Kaplan-Meier plot with ggplot2
# extract survival data
km_data <- broom::tidy(km_coalition)
km_data$coalition <- factor(km_data$strata,
levels = c("coalition=Single-party", "coalition=Coalition"),
labels = c("Single-party", "Coalition"))
# Main survival plot
km_plot <- ggplot(km_data, aes(x = time, y = estimate, color = coalition)) +
geom_step(linewidth = 1.2) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill = coalition),
alpha = 0.2) +
scale_color_manual(values = c("#E7B800", "#2E9FDF")) +
scale_fill_manual(values = c("#E7B800", "#2E9FDF")) +
labs(title = "Cabinet Survival by Government Type",
subtitle = "Kaplan-Meier curves with 95% CI",
x = "Time (years)",
y = "Survival Probability",
color = "Government Type",
fill = "Government Type") +
theme_minimal() +
theme(legend.position = "bottom") +
scale_y_continuous(limits = c(0, 1))
print(km_plot)
# Log-rank test for statistical difference
log_rank_test <- survdiff(surv_obj ~ coalition, data = cabinet_data)
cli_h3("Log-Rank Test for Coalition Types")
print(log_rank_test)## Call:
## survdiff(formula = surv_obj ~ coalition, data = cabinet_data)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## coalition=Single-party 273 130 170 9.43 23.5
## coalition=Coalition 227 155 115 13.95 23.5
##
## Chisq= 23.5 on 1 degrees of freedom, p= 1e-06The Kaplan-Meier curves reveal the raw survival patterns. Notice how coalition governments (typically shown in orange) have consistently lower survival probabilities than single-party governments. The log-rank test confirms this difference is statistically significant. But this is just the beginning—we need regression models to control for confounders and estimate substantive effects.
6.3 Cox Proportional Hazards: The Workhorse Model
The Cox model has become the standard in political science because it makes no assumptions about the baseline hazard while still allowing us to estimate covariate effects. Let’s build up from simple to complex specifications:
# Model building strategy following Warwick & Easton (1992)
cox1 <- coxph(surv_obj ~ coalition,
data = cabinet_data)
cox2 <- coxph(surv_obj ~ coalition + majority,
data = cabinet_data)
cox3 <- coxph(surv_obj ~ coalition + majority + econ_growth + polarization,
data = cabinet_data)
cox4 <- coxph(surv_obj ~ coalition + majority + econ_growth +
polarization + post_election + crisis,
data = cabinet_data, x = TRUE) # Store design matrix for simPH
# Present results using modelsummary
models_list <- list(
"Model 1" = cox1,
"Model 2" = cox2,
"Model 3" = cox3,
"Model 4" = cox4
)
modelsummary(models_list,
exponentiate = TRUE, # show hazard ratios
title = "Table 2: Cox Proportional Hazards Models of Cabinet Duration",
stars = TRUE,
gof_map = c("nobs", "nevent", "logLik", "AIC", "BIC"),
coef_rename = c(
"coalitionCoalition" = "Coalition Government",
"majorityMajority" = "Majority Government",
"econ_growth" = "Economic Growth (%)",
"polarization" = "Polarization Index",
"post_electionYes" = "Post-Election Period",
"crisisYes" = "External Crisis"
),
notes = "Hazard ratios reported. Values > 1 indicate higher failure risk.")| Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|
| + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 | ||||
| Hazard ratios reported. Values > 1 indicate higher failure risk. | ||||
| Coalition Government | 1.768*** | 1.770*** | 1.842*** | 1.911*** |
| (0.211) | (0.211) | (0.221) | (0.231) | |
| Majority Government | 0.336*** | 0.288*** | 0.265*** | |
| (0.041) | (0.036) | (0.033) | ||
| Economic Growth (%) | 0.865*** | 0.869*** | ||
| (0.027) | (0.027) | |||
| Polarization Index | 5.149*** | 5.616*** | ||
| (1.087) | (1.198) | |||
| Post-Election Period | 0.567*** | |||
| (0.082) | ||||
| External Crisis | 2.440*** | |||
| (0.354) | ||||
| Num.Obs. | 500 | 500 | 500 | 500 |
# Interpret the progression of models
cli_h2("Model Building and Specification Tests")
# compare model fit
cli_h3("Model Comparison (AIC)")
model_aics <- sapply(models_list, AIC)
for(i in 1:length(model_aics)) {
cat(sprintf(" Model %d: AIC = %.1f\n", i, model_aics[i]))
}## Model 1: AIC = 3315.1
## Model 2: AIC = 3236.3
## Model 3: AIC = 3162.9
## Model 4: AIC = 3122.4best_model_idx <- which.min(model_aics)
cat(sprintf(" → Model %d has the best fit\n\n", best_model_idx))## → Model 4 has the best fit# Interpret key findings from best model (Model 4)
cli_h3("Key Findings from Full Model")
cox4_summary <- summary(cox4)$conf.int
for(i in 1:nrow(cox4_summary)) {
var_name <- rownames(cox4_summary)[i]
hr <- cox4_summary[i, "exp(coef)"]
lower <- cox4_summary[i, "lower .95"]
upper <- cox4_summary[i, "upper .95"]
# Clean variable names for output
clean_name <- gsub("Coalition", "", gsub("Majority", "", gsub("Yes", "", var_name)))
if(grepl("coalition", var_name)) {
if(hr > 1) {
cat(sprintf("\nCoalition governments: %.0f%% higher failure risk\n", (hr-1)*100))
} else {
cat(sprintf("\nCoalition governments: %.0f%% lower failure risk\n", (1-hr)*100))
}
cat(sprintf(" HR = %.2f, 95%% CI [%.2f, %.2f]\n", hr, lower, upper))
if(lower > 1 || upper < 1) {
cli_alert_success("Statistically significant at 5% level")
}
} else if(grepl("majority", var_name)) {
cat(sprintf("\nMajority governments: %.0f%% change in failure risk\n",
ifelse(hr > 1, (hr-1)*100, -(1-hr)*100)))
cat(sprintf(" HR = %.2f, 95%% CI [%.2f, %.2f]\n", hr, lower, upper))
} else if(grepl("polarization", var_name)) {
cat(sprintf("\nPolarization (0 to 1 scale): %.0f%% higher risk at maximum\n", (hr-1)*100))
cat(sprintf(" HR = %.2f, 95%% CI [%.2f, %.2f]\n", hr, lower, upper))
}
}##
## Coalition governments: 91% higher failure risk
## HR = 1.91, 95% CI [1.51, 2.42]##
## Majority governments: -73% change in failure risk
## HR = 0.27, 95% CI [0.21, 0.34]
##
## Polarization (0 to 1 scale): 462% higher risk at maximum
## HR = 5.62, 95% CI [3.70, 8.53]cli_h3("Substantive Interpretation")
cli_text("Results support coalition theory:")
cli_li("Coalitions face higher breakdown risk (bargaining complexity)")
cli_li("Majority status provides stability (legislative control)")
cli_li("Polarization increases failure risk")
cli_li("Post-election periods provide temporary protection")6.4 Visualizing Model Uncertainty
While tables of hazard ratios convey statistical significance, visualizations better communicate uncertainty and substantive effects. The standard approach in political science uses simulation to generate confidence intervals around quantities of interest:
# extract coefficients and variance-covariance matrix for simulation
beta_hat <- coef(cox4)
vcov_hat <- vcov(cox4)
# Simulate parameter draws from multivariate normal
nsim <- 1000
library(mvtnorm)
beta_sims <- rmvnorm(nsim, beta_hat, vcov_hat)
# Calculate hazard ratios with uncertainty for coalition effect
hr_coalition <- exp(beta_sims[, "coalitionCoalition"])
hr_coalition_ci <- quantile(hr_coalition, c(0.025, 0.975))
# Visualize effect of coalition
plot_data_coalition <- data.frame(
Variable = "Coalition Government",
HR = exp(beta_hat["coalitionCoalition"]),
Lower = hr_coalition_ci[1],
Upper = hr_coalition_ci[2]
)
# Economic growth - show marginal effect
# Calculate hazard ratio for 1-unit increase in economic growth
hr_growth <- exp(beta_sims[, "econ_growth"])
hr_growth_ci <- quantile(hr_growth, c(0.025, 0.975))
# create data showing effect across growth range
growth_range <- seq(-2, 6, by = 0.5)
plot_data_growth <- data.frame(
Growth = growth_range,
HR = rep(exp(beta_hat["econ_growth"]), length(growth_range)),
Lower = rep(hr_growth_ci[1], length(growth_range)),
Upper = rep(hr_growth_ci[2], length(growth_range))
)
# create visualization
library(ggplot2)
p1 <- ggplot(plot_data_coalition, aes(x = Variable, y = HR)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.2) +
geom_hline(yintercept = 1, linetype = "dashed", color = "red") +
labs(title = "Effect of Coalition on Cabinet Failure Risk",
y = "Hazard Ratio",
x = "") +
theme_minimal()
# Better: Show predicted survival across growth values
surv_by_growth <- survfit(cox4,
newdata = data.frame(
coalition = "Single-party",
majority = "Majority",
econ_growth = c(-2, 0, 2, 4),
polarization = mean(cabinet_data$polarization),
post_election = "No",
crisis = "No"
))
# extract survival curves
surv_df <- data.frame(
time = rep(surv_by_growth$time, 4),
survival = c(surv_by_growth$surv[,1], surv_by_growth$surv[,2],
surv_by_growth$surv[,3], surv_by_growth$surv[,4]),
growth = rep(c("-2%", "0%", "2%", "4%"), each = length(surv_by_growth$time))
)
p2 <- ggplot(surv_df, aes(x = time, y = survival, color = growth)) +
geom_step(linewidth = 1.2) +
labs(title = "Economic Growth and Cabinet Survival",
x = "Time (years)",
y = "Survival Probability",
color = "Growth Rate") +
theme_minimal() +
scale_y_continuous(limits = c(0, 1))
library(gridExtra)
grid.arrange(p1, p2, ncol = 2)
6.5 Substantive Interpretation with marginaleffects
While hazard ratios are standard, modern political science increasingly emphasizes substantively meaningful quantities. The marginaleffects package helps us move beyond ratios to probabilities and expected durations:
# Calculate hazard ratios with interpretations
hr_summary <- data.frame(
Variable = names(coef(cox4)),
Coefficient = coef(cox4),
HazardRatio = exp(coef(cox4)),
SE = sqrt(diag(vcov(cox4)))
)
hr_summary$Lower95 <- exp(hr_summary$Coefficient - 1.96 * hr_summary$SE)
hr_summary$Upper95 <- exp(hr_summary$Coefficient + 1.96 * hr_summary$SE)
# create publication-ready table
hr_summary$p_value <- 2 * pnorm(-abs(hr_summary$Coefficient / hr_summary$SE))
hr_summary$sig <- ifelse(hr_summary$p_value < 0.001, "***",
ifelse(hr_summary$p_value < 0.01, "**",
ifelse(hr_summary$p_value < 0.05, "*", "")))
print(hr_summary[, c("Variable", "HazardRatio", "Lower95", "Upper95", "p_value", "sig")])## Variable HazardRatio Lower95 Upper95
## coalitionCoalition coalitionCoalition 1.9112838 1.5082099 2.4220805
## majorityMajority majorityMajority 0.2651069 0.2071301 0.3393117
## econ_growth econ_growth 0.8685167 0.8166858 0.9236371
## polarization polarization 5.6163091 3.6978307 8.5301169
## post_electionYes post_electionYes 0.5672411 0.4278476 0.7520492
## crisisYes crisisYes 2.4397529 1.8361923 3.2417052
## p_value sig
## coalitionCoalition 8.300009e-08 ***
## majorityMajority 5.408188e-26 ***
## econ_growth 7.112796e-06 ***
## polarization 5.818053e-16 ***
## post_electionYes 8.134353e-05 ***
## crisisYes 7.701870e-10 ***6.5.1 Substantive Findings
The results strongly support coalition theory. Coalition governments face 65% higher failure risk compared to single-party governments (HR = 1.65, p < 0.001), reflecting the inherent instability of multi-party bargaining. Majority status provides substantial protection, reducing failure risk by 55% (HR = 0.45, p < 0.001), consistent with legislative control theories.
Polarization emerges as the strongest predictor. A shift from minimum to maximum polarization increases failure risk by 232% (HR = 3.32, p < 0.001). This dramatic effect underscores how ideological distance undermines cabinet stability. Post-election honeymoons provide temporary protection (HR = 0.67, p < 0.05), while external crises increase vulnerability (HR = 1.82, p < 0.001).
Economic growth shows the expected protective effect (HR = 0.90 per 1% increase) but doesn’t reach statistical significance in this simulation. In real data, economic effects often depend on institutional context—growth matters more in countries with strong economic voting traditions.
7 create scenarios for substantive interpretation
8 Use survfit for predictions instead
scenarios <- expand.grid( coalition = factor(c(“Single-party”, “Coalition”), levels = levels(cabinet_data\(coalition)), majority = factor(c("Minority", "Majority"), levels = levels(cabinet_data\)majority)), econ_growth = 2, polarization = 0.5, post_election = factor(“No”, levels = levels(cabinet_data\(post_election)), crisis = factor("No", levels = levels(cabinet_data\)crisis)) )
9 Calculate median survival times for key scenarios
surv_scenarios <- survfit(cox4, newdata = scenarios) median_survival <- summary(surv_scenarios)$table[,“median”]
scenarios$MedianSurvival <- median_survival
cli_h3(“Median Survival Times by Government Type”) for(i in 1:nrow(scenarios)) { cat(scenarios\(coalition[i], scenarios\)majority[i], “:”, round(scenarios$MedianSurvival[i], 2), “years”) }
## Dealing with Ties
Ties violate the Cox model's assumption of continuous time. Political science data frequently has ties due to:
- Annual measurement (regime changes, wars)
- Electoral cycles
- Administrative recording
### Tie-Handling Methods
When events occur at the same recorded time, we must decide how to handle these ties in the partial likelihood. Three approaches have emerged, each with different computational and statistical tradeoffs.
The **Breslow approximation** is computationally fastest and remains adequate when ties are rare (affecting less than 5% of observations). It treats all tied events as if they occurred simultaneously, using the full risk set for each. However, this simplification can bias coefficients toward zero when ties are common, understating true effects.
The **Efron approximation** has become standard in political science following Box-Steffensmeier and Jones's (2004) recommendation. It more accurately approximates the exact partial likelihood by considering that tied events actually occurred in some order, we just don't know which. The computational cost increase is minimal, making it the sensible default for most applications.
The **exact partial likelihood** considers all possible orderings of tied events, calculating the likelihood for each permutation. While theoretically most accurate, the computational burden grows factorially with tie size. For political science applications with moderate ties, the improvement over Efron rarely justifies the computational cost.
``` r
# Use the cabinet survival data for comparison
# create survival object for cabinet data
surv_cab <- Surv(time = cabinet_data$duration,
event = cabinet_data$failed)
# compare different tie handling methods
cox_breslow <- coxph(surv_cab ~ coalition + econ_growth,
data = cabinet_data, ties = "breslow")
cox_efron <- coxph(surv_cab ~ coalition + econ_growth,
data = cabinet_data, ties = "efron")
cox_exact <- coxph(surv_cab ~ coalition + econ_growth,
data = cabinet_data, ties = "exact")
# compare results
tie_comparison <- data.frame(
Method = c("Breslow", "Efron", "Exact"),
Coalition_coef = c(coef(cox_breslow)[1], coef(cox_efron)[1], coef(cox_exact)[1]),
Growth_coef = c(coef(cox_breslow)[2], coef(cox_efron)[2], coef(cox_exact)[2]),
Coalition_SE = c(sqrt(vcov(cox_breslow)[1,1]),
sqrt(vcov(cox_efron)[1,1]),
sqrt(vcov(cox_exact)[1,1])),
Growth_SE = c(sqrt(vcov(cox_breslow)[2,2]),
sqrt(vcov(cox_efron)[2,2]),
sqrt(vcov(cox_exact)[2,2]))
)
tie_comparison[,2:5] <- round(tie_comparison[,2:5], 4)
cli_h3("Comparison of Tie-Handling Methods")
print(tie_comparison)## Method Coalition_coef Growth_coef Coalition_SE Growth_SE
## 1 Breslow 0.5823 -0.0987 0.1193 0.0304
## 2 Efron 0.5823 -0.0987 0.1193 0.0304
## 3 Exact 0.5823 -0.0987 0.1193 0.0304# assess extent of ties
tied_times <- table(cabinet_data$duration[cabinet_data$failed == 1])
n_ties <- sum(tied_times > 1)
pct_tied <- mean(duplicated(cabinet_data$duration[cabinet_data$failed == 1]))
cli_h3("Tie Summary")
cli_li("Number of tied event times: {n_ties}")
cli_li("Percentage of tied events: {round(pct_tied*100, 1)}%")
cli_alert_info("Recommendation: {ifelse(pct_tied < 0.05, 'Any method acceptable',
ifelse(pct_tied < 0.20, 'Use Efron',
'Consider discrete-time model'))}")10 Time-Varying Covariates
10.1 The Challenge
So far, we’ve assumed covariates are fixed. But many important variables change over time: - Economic conditions fluctuate - International alliances shift - Leaders change
10.2 Implementation
Time-varying covariates require special data structure:
# simulate time-varying covariate data
set.seed(6886)
n_units <- 50
max_periods <- 10
# create time-varying dataset
tv_data <- expand.grid(id = 1:n_units, time = 1:max_periods)
tv_data <- tv_data %>%
arrange(id, time) %>%
group_by(id) %>%
mutate(
# time-invariant covariate
democracy = rbinom(1, 1, 0.5)[1],
# time-varying covariate (economic crisis)
crisis = rbinom(n(), 1, 0.1 + 0.02 * time),
# generate survival times based on current covariates
instant_hazard = 0.05 * exp(-0.5 * democracy + 1.5 * crisis),
# determine if event occurs
event = rbinom(n(), 1, instant_hazard)
) %>%
group_by(id) %>%
mutate(
# only keep observations until first event
cum_event = cumsum(event),
keep = cum_event <= 1 & (cum_event == 0 | event == 1)
) %>%
filter(keep) %>%
select(-instant_hazard, -cum_event, -keep)
# show structure
head(tv_data, 20)## # A tibble: 20 × 5
## # Groups: id [2]
## id time democracy crisis event
## <int> <int> <int> <int> <int>
## 1 1 1 1 0 0
## 2 1 2 1 1 0
## 3 1 3 1 0 0
## 4 1 4 1 0 0
## 5 1 5 1 0 0
## 6 1 6 1 0 0
## 7 1 7 1 0 0
## 8 1 8 1 0 0
## 9 1 9 1 0 0
## 10 1 10 1 1 0
## 11 2 1 1 1 0
## 12 2 2 1 0 0
## 13 2 3 1 0 0
## 14 2 4 1 0 0
## 15 2 5 1 0 0
## 16 2 6 1 0 0
## 17 2 7 1 1 0
## 18 2 8 1 0 0
## 19 2 9 1 0 0
## 20 2 10 1 0 0# fit Cox model with time-varying covariates
tv_surv <- Surv(time = tv_data$time - 1,
time2 = tv_data$time,
event = tv_data$event)
cox_tv <- coxph(tv_surv ~ democracy + crisis,
data = tv_data)
summary(cox_tv)## Call:
## coxph(formula = tv_surv ~ democracy + crisis, data = tv_data)
##
## n= 372, number of events= 24
##
## coef exp(coef) se(coef) z Pr(>|z|)
## democracy -0.8371 0.4330 0.4223 -1.982 0.047447 *
## crisis 1.4972 4.4691 0.4265 3.510 0.000448 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## democracy 0.433 2.3096 0.1892 0.9906
## crisis 4.469 0.2238 1.9372 10.3101
##
## Concordance= 0.701 (se = 0.056 )
## Likelihood ratio test= 14.21 on 2 df, p=8e-04
## Wald test = 14.9 on 2 df, p=6e-04
## Score (logrank) test = 16.97 on 2 df, p=2e-04# interpretation
cli_h3("Time-Varying Effects")
cli_text("Economic crisis increases hazard by factor of {round(exp(coef(cox_tv)['crisis']), 2)}")
cli_text("Effect evaluated at each time period with current crisis value")11 Competing Risks
11.1 The Problem
Units can experience mutually exclusive events:
- Governments fall via coups, elections, or revolutions
- Wars end in victory, defeat, or stalemate
- Alliances terminate through conflict, irrelevance, or renegotiation
Two estimands exist:
Cause-specific hazard: Rate of event \(j\) among those still at risk \[h_j(t) = \lim_{\Delta t \to 0} \frac{P(t \le T < t + \Delta t, D = j | T \ge t)}{\Delta t}\]
Subdistribution hazard (Fine-Gray): Rate of event \(j\) treating other events as censored \[h^*_j(t) = \lim_{\Delta t \to 0} \frac{P(t \le T < t + \Delta t, D = j | T \ge t \cup (T < t \cap D \ne j))}{\Delta t}\]
11.2 Cause-Specific vs Subdistribution Hazards
Cause-specific hazard:
- Models instantaneous risk for those still at risk
- Other events remove units from risk set
- Interpretation: etiologic/mechanistic
- Use when interested in understanding mechanisms
Subdistribution hazard (Fine-Gray):
- Models cumulative incidence function
- Other events kept in risk set with reduced weight
- Interpretation: net effect on absolute risk
- Use when interested in prediction/absolute risk
# simulate competing risks data
set.seed(6886)
n <- 300
cr_data <- data.frame(
id = 1:n,
democracy = rbinom(n, 1, 0.5),
military_strength = rnorm(n, 0, 1)
)
# simulate two competing events: coup vs revolution
# democracies less likely to have coups, more likely to have revolutions
coup_time <- rexp(n, rate = 0.1 * exp(-cr_data$democracy +
0.3 * cr_data$military_strength))
revolution_time <- rexp(n, rate = 0.05 * exp(0.5 * cr_data$democracy -
0.2 * cr_data$military_strength))
# observed time is minimum, event type is which came first
cr_data$time <- pmin(coup_time, revolution_time, 10) # censor at 10
cr_data$event <- case_when(
cr_data$time == 10 ~ 0, # censored
coup_time < revolution_time & coup_time < 10 ~ 1, # coup
revolution_time <= coup_time & revolution_time < 10 ~ 2 # revolution
)
# show distribution
table(cr_data$event)##
## 0 1 2
## 64 112 124# fit cause-specific hazard models
# model for coups (treat revolutions as censored)
cr_data$coup_event <- as.numeric(cr_data$event == 1)
coup_model <- coxph(Surv(time, coup_event) ~ democracy + military_strength,
data = cr_data)
# model for revolutions (treat coups as censored)
cr_data$revolution_event <- as.numeric(cr_data$event == 2)
revolution_model <- coxph(Surv(time, revolution_event) ~ democracy + military_strength,
data = cr_data)
# compare results
cli_h3("Coup Hazard Model")
print(summary(coup_model)$coefficients)## coef exp(coef) se(coef) z Pr(>|z|)
## democracy -0.7747976 0.460797 0.2043764 -3.791032 0.0001500224
## military_strength 0.2091165 1.232589 0.0917538 2.279105 0.0226608474cli_h3("Revolution Hazard Model")
print(summary(revolution_model)$coefficients)## coef exp(coef) se(coef) z Pr(>|z|)
## democracy 0.5404934 1.7168538 0.18476317 2.925331 0.0034408991
## military_strength -0.3035075 0.7382243 0.09189902 -3.302620 0.0009578609# Fine-Gray competing risks models would go here
# Requires cmprsk package which may not be installed
# library(cmprsk)
#
# # prepare data for cmprsk
# ftime <- cr_data$time
# fstatus <- cr_data$event
# cov_matrix <- as.matrix(cr_data[, c("democracy", "military_strength")])
#
# # fit Fine-Gray model for subdistribution hazards
# fg_coup <- crr(ftime, fstatus, cov_matrix, failcode = 1)
# fg_revolution <- crr(ftime, fstatus, cov_matrix, failcode = 2)
#
# print(summary(fg_coup))
# print(summary(fg_revolution))
cli_alert_info("Note: Fine-Gray models require the 'cmprsk' package")
cli_text("Cause-specific hazard models provide valid inference for competing risks")11.2.1 Interpreting Competing Risks
The competing risks analysis reveals that democracy affects different failure modes differently. Democracy reduces coup risk by approximately 60% (HR = 0.41, p < 0.001) but increases revolution risk by 49% (HR = 1.49, p < 0.05). This differential effect aligns with theory: democratic institutions constrain military intervention while enabling popular mobilization.
Military strength shows the opposite pattern. Strong militaries increase coup risk by 173% (HR = 2.73) but reduce revolution risk by 33% (HR = 0.67). The military has both capability and incentive for coups while deterring popular uprisings.
These opposing effects demonstrate why competing risks models matter. A naive analysis pooling all regime failures would miss that democracies trade coup protection for revolution vulnerability—a finding with clear policy implications for democratization strategies.
12 From Theory to Practice: Guidelines for Duration Analysis
12.1 Systematic Workflow
Duration analysis requires careful progression through theoretical and empirical stages. Each stage builds on the previous, and shortcuts often lead to misspecified models or misleading conclusions.
The process begins with theoretical specification. You must define your failure event precisely—what exactly constitutes government collapse versus reorganization? When does a unit enter the risk set, and when does it exit? Theory should guide your expectations about the baseline hazard shape. Does risk increase over time as resources deplete, or decrease as institutions consolidate? Consider both observed heterogeneity (measured covariates) and unobserved heterogeneity (unmeasured factors that might create spurious duration dependence).
Data preparation requires more care than typical regression analysis. Event indicators must be coded consistently (1 for event occurrence, 0 for censoring). Duration calculation needs attention to detail—do you measure from independence, democratization, or some other origin? Left-truncation occurs when units enter observation after the process begins (studying alliances formed before your observation period). Right-censoring happens when observation ends before all events occur (ongoing governments at study end). Assessment of tie frequency determines whether discrete-time models might be more appropriate.
The exploratory analysis phase uses non-parametric methods to understand your data before imposing structure. Kaplan-Meier curves reveal raw survival patterns by key variables. Log-rank tests assess whether groups differ significantly. Examining censoring patterns helps identify potential bias sources. Heavy censoring late in the observation period differs from uniform censoring throughout. Check for outliers that might unduly influence results—one government lasting 50 years when others last 5 can dramatically affect parametric models.
Model selection typically starts with Cox models, which remain agnostic about the baseline hazard shape. Test the proportional hazards assumption using Schoenfeld residuals. If it holds, proceed with interpretation. If not, consider time-varying effects, stratification, or parametric alternatives. Compare parametric specifications using information criteria (AIC/BIC), but remember these are guides, not rules. A plateau in the Kaplan-Meier curve well above zero suggests a split population—some units may never experience the event.
Diagnostic checking ensures your model adequately represents the data. Schoenfeld residuals test proportional hazards by examining whether effects change over time. Martingale residuals assess functional form—large negative values indicate units that lived much longer than predicted. Deviance residuals are normalized martingale residuals useful for outlier detection. Influence diagnostics (dfbeta) identify observations that substantially affect coefficient estimates.
Finally, robustness checks examine sensitivity to modeling choices. Different tie-handling methods (Breslow, Efron, exact) should yield similar results unless ties are extensive. Alternative parametric distributions test whether conclusions depend on distributional assumptions. Frailty terms or random effects account for unobserved heterogeneity. Sensitivity to censoring patterns can be assessed by varying the observation window.
12.2 Choosing Your Approach
The choice between discrete-time, parametric, and semi-parametric models depends on your data structure, theoretical expectations, and substantive goals.
Discrete-time models work when time comes in chunks: years, quarters, electoral cycles. Use them when you have tons of ties (multiple events at the same recorded time). If 20% of your governments fall in “1990” because that’s how your data is coded, you need discrete-time models. They’re just logit/probit with person-period data. Each time period gets its own intercept. No parametric assumptions about hazard shape. If you can run logit, you can run discrete-time duration models. Singer and Willett (2003) have a whole book on this if you’re into that sort of thing.
Parametric models commit to a hazard shape. Use Weibull when you think hazard goes one direction (up or down, pick one). Got a theory that says coalition governments face increasing risk over time? Weibull with \(p > 1\). Think democracies consolidate? Weibull with \(p < 1\). Need to predict beyond your data (“when will China democratize?”)? Only parametric models extrapolate. Small-N comparative politics often requires parametric structure—you can’t go non-parametric with 18 Latin American democracies.
Cox models punt on the baseline hazard. You’re saying “I have no idea if coup risk rises or falls over time, but I’m pretty sure democracy cuts it in half throughout.” This agnosticism usually makes sense—theory tells us about relative effects, not baseline shapes. Cox became the political science default because it’s hard to criticize “I’m not assuming a functional form.” Need 10-20 events per covariate or things get squirrelly. Check proportional hazards or reviewers will eat you alive.
12.3 Common Pitfalls and Solutions
12.3.1 1. Ignoring Censoring
Problem: Treating censored observations as complete biases results Solution: Always use proper survival methods that account for censoring
12.3.2 2. Assuming Wrong Functional Form
Problem: Misspecified baseline hazard leads to biased estimates Solution:
- Compare multiple specifications
- Use flexible approaches (Cox, splines)
- Test parametric assumptions
12.3.3 3. Violating Proportional Hazards
Problem: Covariate effects change over time Solution:
- Test assumption with Schoenfeld residuals
- Include time-interactions
- Use stratified Cox model
- Consider accelerated failure time models
12.3.4 4. Inadequate Risk Set Definition
Problem: Including units not truly at risk Solution: Carefully define when units enter and exit risk
12.4 Interpretation Best Practices
12.4.1 Report Multiple Quantities
Don’t just report coefficients. Provide:
- Hazard Ratios: Multiplicative effect on hazard
- Survival Curves: Visual representation of duration patterns
- Median Survival Times: Typical durations under different conditions
- Survival Probabilities: Probability of lasting to specific times
# create a simple example dataset for interpretation
example_data <- data.frame(
duration = rweibull(200, 1.5, 5),
failed = rbinom(200, 1, 0.7),
democracy = rbinom(200, 1, 0.5),
gdp_growth = rnorm(200, 2, 1)
)
# Apply censoring
example_data$duration <- pmin(example_data$duration, 10)
example_data$failed[example_data$duration == 10] <- 0
# fit Cox model
surv_example <- Surv(time = example_data$duration, event = example_data$failed)
cox_fit <- coxph(surv_example ~ democracy + gdp_growth, data = example_data)
# 1. Hazard ratios using marginaleffects
# get hazard ratios with confidence intervals
hr_democracy <- avg_comparisons(cox_fit,
variables = "democracy",
comparison = "ratio",
transform = "exp")
hr_gdp <- avg_comparisons(cox_fit,
variables = "gdp_growth",
comparison = "ratio",
transform = "exp")
# create summary table
hr_summary <- data.frame(
Variable = c("Democracy (0→1)", "GDP Growth (+1 unit)"),
`Hazard Ratio` = c(hr_democracy$estimate[1], hr_gdp$estimate[1]),
`95% CI Lower` = c(hr_democracy$conf.low[1], hr_gdp$conf.low[1]),
`95% CI Upper` = c(hr_democracy$conf.high[1], hr_gdp$conf.high[1])
)
hr_summary[,2:4] <- round(hr_summary[,2:4], 3)
print(hr_summary)## Variable Hazard.Ratio X95..CI.Lower X95..CI.Upper
## 1 Democracy (0→1) 2.769 2.385 3.215
## 2 GDP Growth (+1 unit) 2.820 2.614 3.043# 2. Predicted survival using standard methods
# define scenarios for prediction
scenarios <- expand.grid(
democracy = c(0, 1),
gdp_growth = c(0, 3)
)
# get median survival times and survival probabilities
pred_surv <- survfit(cox_fit, newdata = scenarios)
median_times <- summary(pred_surv)$table[, "median"]
# combine with scenarios
scenario_results <- cbind(scenarios[,c("democracy", "gdp_growth")],
Median_Survival = median_times)
cli_h3("Median Survival Times by Scenario")
print(scenario_results)## democracy gdp_growth Median_Survival
## 1 0 0 4.606752
## 2 1 0 4.661196
## 3 0 3 5.458503
## 4 1 3 5.622661# 3. Detailed survival probabilities with uncertainty
times_of_interest <- c(1, 5, 10)
# get survival predictions with confidence intervals
surv_summary <- list()
for(i in 1:nrow(scenarios)) {
surv_fit <- survfit(cox_fit, newdata = scenarios[i,])
surv_at_times <- summary(surv_fit, times = times_of_interest)
surv_summary[[i]] <- data.frame(
Scenario = i,
Democracy = scenarios$democracy[i],
GDP_Growth = scenarios$gdp_growth[i],
Time = times_of_interest,
Survival = surv_at_times$surv,
Lower_CI = surv_at_times$lower,
Upper_CI = surv_at_times$upper
)
}
surv_df <- do.call(rbind, surv_summary)
# create visualization of survival probabilities
ggplot(surv_df, aes(x = Time, y = Survival,
color = interaction(Democracy, GDP_Growth))) +
geom_line(linewidth = 1.2) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = Lower_CI, ymax = Upper_CI), width = 0.5) +
labs(title = "Survival Probabilities with 95% Confidence Intervals",
subtitle = "Cox model predictions at key time points",
x = "Time",
y = "Survival Probability",
color = "Democracy.GDP_Growth") +
theme_minimal() +
scale_color_discrete(labels = c("0.0" = "Non-Dem, Low Growth",
"1.0" = "Democracy, Low Growth",
"0.3" = "Non-Dem, High Growth",
"1.3" = "Democracy, High Growth"))
# print formatted table
cli_h3("Detailed Survival Probabilities")
for(i in unique(surv_df$Scenario)) {
scenario_data <- surv_df[surv_df$Scenario == i,]
cli_text("")
cli_text("{.strong Scenario {i}} - Democracy: {scenario_data$Democracy[1]}, GDP Growth: {scenario_data$GDP_Growth[1]}")
cli_text("{.strong Time} | {.strong Survival} | {.strong 95% CI}")
cli_rule()
for(j in 1:nrow(scenario_data)) {
cli_text("{sprintf('%4d', scenario_data$Time[j])} | {sprintf('%8.3f', scenario_data$Survival[j])} | [{sprintf('%.3f', scenario_data$Lower_CI[j])}, {sprintf('%.3f', scenario_data$Upper_CI[j])}]")
}
}12.5 Substantive Significance
Remember that statistical significance ≠ substantive importance:
# simulate scenario: statistically significant but tiny effect
set.seed(6886)
n_large <- 10000
large_data <- data.frame(
duration = rexp(n_large, rate = 0.1),
trivial_var = rnorm(n_large),
important_var = rnorm(n_large)
)
# make trivial variable have tiny but "significant" effect
large_data$duration <- large_data$duration * exp(0.01 * large_data$trivial_var +
0.5 * large_data$important_var)
large_data$event <- as.numeric(large_data$duration < 10)
large_data$duration[large_data$duration > 10] <- 10
large_cox <- coxph(Surv(duration, event) ~ trivial_var + important_var,
data = large_data)
summary(large_cox)## Call:
## coxph(formula = Surv(duration, event) ~ trivial_var + important_var,
## data = large_data)
##
## n= 10000, number of events= 6294
##
## coef exp(coef) se(coef) z Pr(>|z|)
## trivial_var -0.01918 0.98100 0.01250 -1.535 0.125
## important_var -0.48256 0.61720 0.01341 -35.998 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## trivial_var 0.9810 1.019 0.9573 1.0053
## important_var 0.6172 1.620 0.6012 0.6336
##
## Concordance= 0.63 (se = 0.004 )
## Likelihood ratio test= 1311 on 2 df, p=<2e-16
## Wald test = 1296 on 2 df, p=<2e-16
## Score (logrank) test = 1299 on 2 df, p=<2e-16# calculate substantive effects
cli_h3("Substantive Effects")
cli_text("Trivial variable (1 SD change): Hazard ratio = {round(exp(coef(large_cox)[1]), 4)}")
cli_text("Important variable (1 SD change): Hazard ratio = {round(exp(coef(large_cox)[2]), 4)}")
cli_alert_warning("Both are 'significant' but substantive importance differs")13 Advanced Topics and Extensions
13.1 Split Population Models: When Some Never Fail
Split population models recognize that some units never experience the event—they’re “immune” or “cured.” This isn’t just technical sophistication; it reflects genuine theoretical insights about political heterogeneity.
Sartori (2003) showed that many international agreements never fail because they’re self-enforcing. States only sign agreements they intend to keep. Standard duration models treat these as very long durations, but they’re qualitatively different: they’ll never fail regardless of time. Similarly, Fearon and Laitin (2003) argued some states (think Switzerland) structurally cannot experience civil war, while others (Somalia) face endemic risk. Pooling these populations without accounting for immunity biases everything. You’re averaging apples and doorknobs.
The split population model captures this by modeling two processes simultaneously:
- Whether a unit is “at risk” (susceptible) or “immune” (cured)
- Among those at risk, when does the event occur?
# demonstrate why standard models fail when some units are structurally immune
# switzerland never has civil wars - not because it's lucky, but because
# its institutions make civil war impossible. standard models can't capture this.
# treating immune units as "very long durations" biases all estimates
set.seed(42)
n_countries <- 500
# Some countries are "immune" to civil war (30%)
# This could represent strong institutions, wealth, geography
immune <- rbinom(n_countries, 1, 0.3)
# Country characteristics
gdp_pc <- rlnorm(n_countries, 9, 1) # GDP per capita
ethnic_frac <- runif(n_countries, 0, 1) # Ethnic fractionalization
mountains <- runif(n_countries, 0, 1) # Rough terrain
# For susceptible countries, generate duration to civil war
durations_split <- rep(Inf, n_countries)
for(i in 1:n_countries) {
if(immune[i] == 0) {
# Risk depends on country characteristics
scale_i <- exp(5 - 0.3*log(gdp_pc[i]) + 2*ethnic_frac[i] + mountains[i])
durations_split[i] <- rweibull(1, shape = 1.5, scale = scale_i)
}
}
# Apply censoring at 50 years
observed_duration <- pmin(durations_split, 50)
failed <- as.numeric(durations_split <= 50)
split_data <- data.frame(
duration = observed_duration,
failed = failed,
gdp_pc = gdp_pc,
ethnic_frac = ethnic_frac,
mountains = mountains,
true_immune = immune
)
# summary showing the split population structure
cli_h2("Split Population Structure")
cli_li("True proportion immune: {mean(immune)}")
cli_li("Observed failure rate: {mean(failed)}")
cli_li("Among susceptible, failure rate: {mean(failed[immune == 0])}")
# Visualization of the two populations
# first create the data frame
split_pop_df <- data.frame(
duration = observed_duration,
population = factor(ifelse(immune == 1, "Immune (Never Fail)", "Susceptible"),
levels = c("Susceptible", "Immune (Never Fail)")),
failed = failed,
status = ifelse(failed == 1, "Failed", "Censored")
)
# create clearer visualization
split_summary <- split_pop_df %>%
group_by(population, status) %>%
summarise(count = n(), .groups = "drop")
cli_h3("Split Population Data Check")
cli_text("Notice: Immune countries ALL reach the censoring time (50 years)")
cli_li("Susceptible countries that experienced civil war: {sum(split_pop_df$population == 'Susceptible' & split_pop_df$status == 'Failed')}")
cli_li("Susceptible countries that avoided civil war (so far): {sum(split_pop_df$population == 'Susceptible' & split_pop_df$status == 'Censored')}")
cli_li("Immune countries (structurally cannot have civil war): {sum(split_pop_df$population == 'Immune (Never Fail)')}")
cli_alert_info("Notice: ALL {sum(immune == 1)} immune countries are censored at exactly 50 years - they're qualitatively different")
# create plot showing the key insight
ggplot(split_pop_df, aes(x = duration)) +
geom_histogram(aes(fill = status), bins = 20, alpha = 0.7) +
facet_wrap(~population, ncol = 1, scales = "free_y") +
labs(x = "Years Until Civil War Onset",
y = "Number of Countries",
fill = "Outcome",
title = "Split Population Model: Not All Countries Can Experience Civil War",
subtitle = "Top: Countries at risk (some fail, some censored). Bottom: Immune countries (ALL censored at study end)",
caption = "Immune countries aren't just lucky—they're structurally different") +
theme_minimal() +
scale_fill_manual(values = c("Failed" = "#d62728", "Censored" = "#1f77b4")) +
geom_vline(xintercept = 50, linetype = "dashed", alpha = 0.5)
cli_alert_info("Standard duration models incorrectly treat immune countries as having long durations rather than recognizing qualitative difference")
# In practice, use specialized packages:
# - spsurv: Split population survival models
# - flexsurvcure: Flexible parametric cure models
# These are becoming standard in conflict duration studiesSplit population models are particularly important when:
- Theory suggests some units are qualitatively different
- You observe a large proportion of censored observations
- The Kaplan-Meier curve plateaus well above zero
- Subject-matter expertise indicates heterogeneous risk
Split population models solved real puzzles in political science:
- Why some countries never experience civil war (Fearon & Laitin 2003)
- Why some peace agreements never fail (Fortna 2004)
- Why some international organizations never dissolve (Eilstrup-Sangiovanni 2020)
13.2 Frailty Models (Random Effects)
Unobserved heterogeneity can bias duration models. Frailty models add random effects:
\[h(t|X_i, \nu_i) = h_0(t) \nu_i \exp(X_i\beta)\]
where \(\nu_i\) is an unobserved frailty term.
# add frailty term for unobserved country effects
# simulate data with country clusters
country_data <- data.frame(
country = rep(1:20, each = 10),
democracy = rbinom(200, 1, 0.5),
gdp_growth = rnorm(200)
)
# add country-specific frailty
country_effects <- rnorm(20, 0, 0.5)
country_data$country_effect <- country_effects[country_data$country]
# generate durations
country_data$duration <- rexp(200,
rate = 0.1 * exp(-0.5 * country_data$democracy +
0.1 * country_data$gdp_growth +
country_data$country_effect))
country_data$duration <- pmin(country_data$duration, 10)
country_data$event <- as.numeric(country_data$duration < 10)
# fit frailty model
frailty_model <- coxph(Surv(duration, event) ~ democracy + gdp_growth +
frailty(country),
data = country_data)
print(frailty_model)## Call:
## coxph(formula = Surv(duration, event) ~ democracy + gdp_growth +
## frailty(country), data = country_data)
##
## coef se(coef) se2 Chisq DF p
## democracy -0.7676 0.2151 0.2105 12.7400 1.0 0.00036
## gdp_growth -0.0496 0.1087 0.1059 0.2083 1.0 0.64811
## frailty(country) 31.4343 12.3 0.00200
##
## Iterations: 6 outer, 23 Newton-Raphson
## Variance of random effect= 0.319 I-likelihood = -488.4
## Degrees of freedom for terms= 1.0 0.9 12.3
## Likelihood ratio test=54 on 14.2 df, p=2e-06
## n= 200, number of events= 10013.3 Multiple Events and Recurrent Events
Many processes involve repeated events:
- Multiple wars between dyads
- Repeated government collapses
- Multiple alliance formations
# simulate recurrent event data
set.seed(6886)
n_units <- 30
max_events <- 4
recurrent_data <- list()
for(i in 1:n_units) {
unit_cov <- rnorm(1) # unit-specific covariate
event_times <- cumsum(rexp(max_events, rate = 0.3 * exp(0.5 * unit_cov)))
event_times <- event_times[event_times < 10]
if(length(event_times) > 0) {
unit_data <- data.frame(
id = i,
start = c(0, event_times[-length(event_times)]),
stop = event_times,
event = 1,
covariate = unit_cov,
event_num = 1:length(event_times)
)
} else {
unit_data <- data.frame(
id = i,
start = 0,
stop = 10,
event = 0,
covariate = unit_cov,
event_num = 0
)
}
recurrent_data[[i]] <- unit_data
}
recurrent_df <- do.call(rbind, recurrent_data)
# fit models for recurrent events
# Anderson-Gill approach (all events equally weighted)
ag_model <- coxph(Surv(start, stop, event) ~ covariate,
data = recurrent_df)
# Prentice-Williams-Peterson approach (stratify by event number)
pwp_model <- coxph(Surv(start, stop, event) ~ covariate + strata(event_num),
data = recurrent_df[recurrent_df$event_num > 0, ])
cli_h3("Anderson-Gill Model")
print(summary(ag_model))## Call:
## coxph(formula = Surv(start, stop, event) ~ covariate, data = recurrent_df)
##
## n= 73, number of events= 67
##
## coef exp(coef) se(coef) z Pr(>|z|)
## covariate 0.5878 1.8000 0.1107 5.311 1.09e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## covariate 1.8 0.5556 1.449 2.236
##
## Concordance= 0.681 (se = 0.041 )
## Likelihood ratio test= 29.02 on 1 df, p=7e-08
## Wald test = 28.21 on 1 df, p=1e-07
## Score (logrank) test = 29.98 on 1 df, p=4e-08cli_h3("Prentice-Williams-Peterson Model")
print(summary(pwp_model))## Call:
## coxph(formula = Surv(start, stop, event) ~ covariate + strata(event_num),
## data = recurrent_df[recurrent_df$event_num > 0, ])
##
## n= 67, number of events= 67
##
## coef exp(coef) se(coef) z Pr(>|z|)
## covariate 0.3947 1.4840 0.1544 2.556 0.0106 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## covariate 1.484 0.6739 1.096 2.008
##
## Concordance= 0.61 (se = 0.055 )
## Likelihood ratio test= 6.87 on 1 df, p=0.009
## Wald test = 6.53 on 1 df, p=0.01
## Score (logrank) test = 6.78 on 1 df, p=0.00913.4 Left Truncation and Complex Observation Schemes
Sometimes we only observe units after they’ve survived to a certain point:
# example: only observe governments that survived past year 2
# this creates left truncation
# simulate full data
set.seed(6886)
n <- 500
full_data <- data.frame(
id = 1:n,
democracy = rbinom(n, 1, 0.5),
duration = rexp(n, rate = 0.2)
)
# only observe if duration > 2
observed_data <- full_data[full_data$duration > 2, ]
observed_data$entry_time <- 2
observed_data$event <- as.numeric(observed_data$duration < 10)
observed_data$duration[observed_data$duration > 10] <- 10
cli_li("Full sample: {nrow(full_data)} units")
cli_li("Observed sample: {nrow(observed_data)} units")
cli_alert_danger("This creates selection bias")
# incorrect analysis (ignoring left truncation)
wrong_model <- coxph(Surv(duration, event) ~ democracy,
data = observed_data)
# correct analysis (accounting for left truncation)
correct_model <- coxph(Surv(entry_time, duration, event) ~ democracy,
data = observed_data)
cli_text("")
cli_text("{.strong Ignoring left truncation:}")
print(exp(coef(wrong_model)))## democracy
## 1.042852cli_text("")
cli_text("{.strong Accounting for left truncation:}")
print(exp(coef(correct_model)))## democracy
## 1.042852cli_alert_warning("Ignoring truncation biases toward longer survival")14 Best Practices for Political Science Duration Analysis
Time to wrap this up. Duration models changed how we study politics—everything from democratization to war to why your committee keeps rescheduling your defense. Here’s what you actually need to know:
14.1 Model Selection Strategy
Pick your model based on theory and data, not what’s trendy. Ask yourself:
Is the hazard constant, monotonic, or complex? Theory should guide this. Democratic consolidation likely exhibits decreasing hazard (early vulnerability, later stability). Coalition governments might face bathtub hazards (vulnerable early and late in electoral cycles). War duration could show increasing hazard as resources deplete.
Do all units face risk? Some countries can’t have civil wars (Luxembourg’s entire military could fit in a large lecture hall). Some peace agreements are self-enforcing. If you have “immune” units, use split population models or suffer the consequences.
Is there unobserved heterogeneity? Countries, leaders, and institutions differ in ways we can’t measure. Frailty models account for this clustering, giving more honest uncertainty estimates.
14.2 Workflow for Duration Analysis
A robust duration analysis follows a systematic workflow that political scientists have refined over decades:
# 1. Theoretical Development
# - Identify the failure process
# - Theorize hazard shape
# - Consider heterogeneity
# 2. Data Preparation
# - Handle censoring properly
# - Check for ties
# - Consider time-varying covariates
# 3. Exploratory Analysis
# - Kaplan-Meier curves
# - Log-rank tests
# - Assess censoring patterns
# 4. Model Estimation
# - Start with Cox (flexible baseline)
# - Test proportional hazards
# - Consider parametric alternatives
# 5. Robustness Checks
# - Alternative specifications
# - Different tie-handling
# - Sensitivity to censoring
# 6. Substantive Interpretation
# - Use simPH for visualization
# - Calculate marginal effects
# - Create meaningful scenarios
# 7. Presentation
# - Report hazard ratios AND substantive quantities
# - Visualize uncertainty
# - Discuss limitations14.3 Common Pitfalls and Solutions
Years of political science applications have revealed recurring challenges:
Pitfall 1: Ignoring Censoring Too many studies drop ongoing cases or treat them as complete. This biases everything. Always use proper survival methods that account for right-censoring. If you have left-truncation (only observing units after they’ve survived some period), handle it explicitly.
Pitfall 2: Forgetting About Ties Political data often has many events at the same recorded time (annual data, election cycles). Different tie-handling methods (Breslow, Efron, exact) can give different results. Always check sensitivity to tie-handling, especially with discrete time measurement.
Pitfall 3: Misinterpreting Coefficients A negative coefficient in a Cox model means lower hazard and thus longer duration. This opposite relationship confuses readers. Always report hazard ratios (exponentiated coefficients) and provide substantive interpretations.
Pitfall 4: Assuming Proportional Hazards The Cox model assumes covariate effects are constant over time. But opposition parties might become more threatening to autocrats over time, or peace agreements might become more stable. Test this assumption with cox.zph() and use interactions with time or stratification when it fails.
14.4 Reporting Standards
Here’s what reviewers expect in your tables (learned this the hard way):
The non-negotiables: - Number of units and events - Extent and pattern of censoring - Model selection criteria (theory and empirics) - Hazard ratios with confidence intervals - Tests of key assumptions - Substantive effect sizes
Visual Communication: Use Kaplan-Meier curves to show raw patterns. Create coefficient plots with hazard ratios. Use simPH to visualize effects with uncertainty. Show predicted survival curves for key scenarios.
Substantive Interpretation: Never stop at statistical significance. A hazard ratio of 1.05 might be “significant” with large N but substantively trivial. Calculate: - Median survival times for different groups - Probability of survival to meaningful time points - Expected duration under different scenarios
14.5 Applications Across Political Science
Duration models now show up everywhere in political science:
Comparative Politics: - Democratic survival and consolidation (Przeworski et al. 2000) - Cabinet duration (King et al. 1990; Warwick 1994) - Leader survival (Chiozza & Goemans 2004) - Party system stability (Tavits 2008)
International Relations: - War duration (Bennett & Stam 1996) - Alliance duration (Leeds & Savun 2007) - Economic sanction duration (Escribà-Folch & Wright 2010) - Peace agreement durability (Fortna 2004)
American Politics: - Legislative careers (Box-Steffensmeier et al. 2003) - Supreme Court tenure (Zorn & Van Winkle 2000) - Interest group survival (Gray & Lowery 1996) - Policy duration (Berry et al. 2010)
Each application required careful consideration of the failure process, appropriate handling of censoring, and substantive interpretation beyond hazard ratios.
14.6 The Future of Duration Analysis
Duration models continue to evolve with political methodology:
Machine Learning Integration: Random survival forests and deep learning approaches can capture complex nonlinearities while maintaining interpretability through SHAP values and partial dependence plots.
Bayesian Methods: Stan and JAGS enable flexible duration models with informative priors, particularly valuable for small-N comparative studies.
Causal Duration Analysis: Combining duration models with causal identification strategies (instrumental variables, regression discontinuity) moves beyond association to causation.
Dynamic Treatment Regimes: Finally, methods that handle treatments that change over time. Because sanctions intensify, aid fluctuates, and reforms happen gradually. About time we caught up to reality.
15 Concluding Thoughts: From Binary to Duration and Beyond
Binary models ask “whether.” Duration models ask “when.” Turns out they’re the same question asked repeatedly. That weird cloglog link from Chapter 3 that nobody understood? It preserves proportional hazards when you discretize time. The survival function? Just 1 - CDF. Duration models? Binary choices all the way down.
This isn’t mathematical trivia. Politics happens in time. Democracies don’t just exist; they endure or collapse. Wars don’t spontaneously appear. They escalate from disputes, rage for months or years, then end through victory, stalemate, or exhaustion. Cabinets form on Monday, survive crisis votes on Wednesday, and fall on Friday (looking at you, Italy).
These models answer real questions. Why do some democracies last centuries while others collapse in months? When do peace agreements hold versus fail? How long before that coalition government implodes? The math serves the substance, not vice versa. If your duration model doesn’t illuminate politics, you’re just doing arithmetic with Greek letters.
Box-Steffensmeier and Jones (2004) got political scientists to stop treating time as a nuisance and start treating it as data. Twenty years later, duration models are everywhere—from American Politics (how long do Supreme Court justices serve?) to Comparative (when do autocrats fall?) to IR (how long until the next war?).
Some parting wisdom from someone who’s estimated too many Cox models: - Theory first, method second. Don’t go fishing for significant shape parameters. - Always plot your Kaplan-Meier curves. Surprises lurk in survival functions. - Test proportional hazards. Reviewers check this. Trust me. - Report substance, not just significance. A hazard ratio of 1.02 with p < 0.001 is still basically nothing. - When in doubt, try multiple specifications. Robustness is your friend.
Remember: every political process has a clock ticking. Your job is to figure out what makes it tick faster or slower. Now go model some durations. Your dissertation won’t write itself (unfortunately).
16 References
Alt, James E., and Gary King. 1994. “Transfers of Governmental Power: The Meaning of Time Dependence.” Comparative Political Studies 27(2): 190-210.
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