Duration Models and Proportional Hazards in Political Science

2001 ◽  
Vol 45 (4) ◽  
pp. 972 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Christopher J. W. Zorn
Keyword(s):  
Political Science ◽  
Proportional Hazards ◽  
Duration Models

scholarly journals ESTIMATING PANEL DATA DURATION MODELS WITH CENSORED DATA

2008 ◽  
Vol 24 (5) ◽  
pp. 1254-1276 ◽  
Author(s):  
Sokbae Lee
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Unobserved Heterogeneity ◽  
Right Censoring ◽  
Duration Models ◽  
Finite Sample ◽  
Asymptotically Normal ◽  
Monte Carlo Studies

This paper presents a method for estimating a class of panel data duration models, under which an unknown transformation of the duration variable is linearly related to the observed explanatory variables and the unobserved heterogeneity (or frailty) with completely known error distributions. This class of duration models includes a panel data proportional hazards model with fixed effects. The proposed estimator is shown to be n1/2-consistent and asymptotically normal with dependent right censoring. The paper provides some discussions on extending the estimator to the cases of longer panels and multiple states. Some Monte Carlo studies are carried out to illustrate the finite-sample performance of the new estimator.

scholarly journals Proper Specification of Nonproportional Hazards Corrections in Duration Models

2017 ◽  
Vol 25 (1) ◽  
pp. 138-144 ◽  
Author(s):  
Shuai Jin ◽  
Frederick J. Boehmke
Keyword(s):  
Proportional Hazards ◽  
Model Specification ◽  
Duration Models ◽  
Time Varying ◽  
Nonproportional Hazards ◽  
Incorrect Model ◽  
Proper Estimation ◽  
Time Invariant ◽  
Time Varying Covariates ◽  
Biased Estimates

Parametric and nonparametric duration models assume proportional hazards: The effect of a covariate on the hazard rate stays constant over time. Researchers have developed techniques to test and correct nonproportional hazards, including interacting the covariates with some function of time. Including this interaction term means that the specification now involves time-varying covariates, and the model specification should reflect this feature. However, in situations with no time-varying covariates initially, researchers often continue to model the duration with only time-invariant covariates. This error results in biased estimates, particularly for the covariates interacted with time. We investigate this issue in over forty political science articles and find that of those studies that begin with time-invariant covariates and correct for nonproportional hazards the majority suffer from incorrect model specification. Proper estimation usually produces substantively or statistically different results.

scholarly journals The implementation of the random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries

2020 ◽  
Author(s):  
Andrew Whetten ◽  
John R Stevens ◽  
Damon Cann
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Political Science ◽  
Selection Process ◽  
Power Sharing ◽  
Duration Models ◽  
Time To Event ◽  
Peace Agreement ◽  
Peace Agreements ◽  
Random Survival Forest

Time-to-event analysis is a common occurrence in political science. In recent years, there has been an increased usage of machine learning methods in quantitative political science research. This article advocates for the implementation of machine learning duration models to assist in a sound model selection process. We provide a brief introduction to the random survival forest (RSF) algorithm and contrast it to a popular predecessor, the Cox proportional hazards model. We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. We provide evidence of significantly higher survival probabilities for peace agreements with 3rd party mediated design and implementation. We also detect increased survival probabilities for peace agreements that incorporate territorial power-sharing and avoid multiple rebel party signatories. Further, the RSF provides a novel approach for ranking of peace agreement criteria importance in predicting peace agreement duration. Our findings justify the robust interpretability and competitive performance of the random survival forest algorithm in numerous circumstances, in addition to promoting a diverse, holistic model-selection process for time-to-event political science data.

Event Dependence and Heterogeneity in Duration Models: The Conditional Frailty Model

2007 ◽  
Vol 15 (3) ◽  
pp. 237-256 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Suzanna De Boef ◽  
Kyle A. Joyce
Keyword(s):  
Political Science ◽  
State Policy ◽  
Frailty Model ◽  
Event History ◽  
Duration Models ◽  
Terrorist Attacks ◽  
Repeated Events ◽  
Event Dependence ◽  
State Policy Adoption ◽  
Event Times

We introduce the conditional frailty model, an event history model that separates and accounts for both event dependence and heterogeneity in repeated events processes. Event dependence and heterogeneity create within-subject correlation in event times thereby violating the assumptions of standard event history models. Simulations show the advantage of the conditional frailty model. Specifically they demonstrate the model's ability to disentangle the sources of within-subject correlation as well as the gains in both efficiency and bias of the model when compared to the widely used alternatives, which often produce conflicting conclusions. Two substantive political science problems illustrate the usefulness and interpretation of the model: state policy adoption and terrorist attacks.

Beyond the Hazard Ratio: Generating Expected Durations from the Cox Proportional Hazards Model

2017 ◽  
Vol 50 (1) ◽  
pp. 303-320 ◽  
Author(s):  
Jonathan Kropko ◽  
Jeffrey J. Harden
Keyword(s):  
Political Science ◽  
Hazard Function ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Hazard Ratios ◽  
Quantities Of Interest

The Cox proportional hazards model is a commonly used method for duration analysis in political science. Typical quantities of interest used to communicate results come from the hazard function (for example, hazard ratios or percentage changes in the hazard rate). These quantities are substantively vague, difficult for many audiences to understand and incongruent with researchers’ substantive focus on duration. We propose methods for computing expected durations and marginal changes in duration for a specified change in a covariate from the Cox model. These duration-based quantities closely match researchers’ theoretical interests and are easily understood by most readers. We demonstrate the substantive improvements in interpretation of Cox model results afforded by the methods with reanalyses of articles from three subfields of political science.

scholarly journals Different Words, Same Song: Advice for Substantively Interpreting Duration Models

2019 ◽  
Vol 52 (4) ◽  
pp. 691-695
Author(s):  
Benjamin T. Jones ◽  
Shawna K. Metzger
Keyword(s):  
Best Practices ◽  
Regression Model ◽  
Political Science ◽  
Regression Models ◽  
Duration Models ◽  
Duration Model ◽  
Model Interpretation ◽  
Common Misconceptions

ABSTRACTThe use of duration models in political science continues to grow, more than a decade after Box-Steffensmeier and Jones (2004). However, several common misconceptions about the models still persist. To improve scholars’ use and interpretation of duration models, we point out that they are a type of regression model and therefore follow the same rules as other more commonly used regression models. In this article, we present four maxims as guidelines. We survey the various duration model interpretation strategies and group them into four categories, which is an important organizational exercise that does not appear elsewhere. We then discuss the strengths and weaknesses of these strategies, noting that all are correct from a technical perspective. However, some strategies make more sense than others for nontechnical reasons, which ultimately informs best practices.

scholarly journals The onset of peace

2012 ◽  
Vol 7 (1) ◽  
Author(s):  
Ron Smith ◽  
Ali Tasiran
Keyword(s):  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Duration Models ◽  
Hazard Models ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
Internal Wars ◽  
Statistical Issues ◽  
Conflict Termination ◽  
Low Activity

Using the Uppsala Conflict Data Program's Conflict Termination Dataset, 1946-2007, we investigate determinants of war duration: How long does war last before the onset of peace? We provide an exposition of the nature of the data and of the transformations statistical issues involved in quantifying the dynamics of conflict, in particular the onset of peace. Various duration models are used to analyze the length of wars that ended with victory and peace or cease fires or show low activity. Multispell Cox proportional hazards models and single-spell log-logistic hazard models suggest that major wars are of shorter duration than minor wars, internal wars last longer than wars between states, and peace comes quicker in Europe than in other regions. We find only small differences in the determinants of terminated wars and wars with low activities or no activities.

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