Techniques of Event History Modeling: New Approaches to Causal Analysis Book

	Preface
1	Introduction	1
1.1	Causal Modeling and Observation Plans	1
1.2	Event History Analysis and Causal Modeling	21
2	Event History Data Structures	38
2.1	Basic Terminology	38
2.2	Event History Data Organization	42
3	Nonparametric Descriptive Methods	56
3.1	Life Table Method	56
3.2	Product-Limit Estimation	71
3.3	Comparing Survivor Functions	76
4	Exponential Transition Rate Models	86
4.1	The Basic Exponential Model	87
4.2	Models with Multiple Destinations	101
4.3	Models with Multiple Episodes	111
5	Piecewise Constant Exponential Models	120
5.1	The Basic Model	120
5.2	Models without Covariates	122
5.3	Models with Proportional Covariate Effects	125
5.4	Models with Period-Specific Effects	125
6	Exponential Models with Time-Dependent Covariates	131
6.1	Parallel and Interdependent Processes	131
6.2	Interdependent Processes: The System Approach	134
6.3	Interdependent Processes: The Causal Approach	138
6.4	Episode Splitting with Qualitative Covariates	140
6.5	Episode Splitting with Quantitative Covariates	152
6.6	Application Examples	157
7	Parametric Models of Time-Dependence	176
7.1	Interpretation of Time-Dependence	177
7.2	Gompertz-Makeham Models	180
7.3	Weibull Models	191
7.4	Log-Logistic Models	197
7.5	Log-Normal Models	202
7.6	Sickle Models	206
8	Methods to Check Parametric Assumptions	213
8.1	Simple Graphical Methods	213
8.2	Pseudoresiduals	219
9	Semi-Parametric Transition Rate Models	228
9.1	Partial Likelihood Estimation	229
9.2	Time-Dependent Covariates	234
9.3	The Proportionality Assumption	240
9.4	Baseline Rates and Survivor Functions	248
9.5	Application Example	251
10	Problems of Model Specification	255
10.1	Unobserved Heterogeneity	255
10.2	Models with a Mixture Distribution	261
10.3	Discussion	274
	App. A: Basic Information About TDA	279
	References	282
	About the Authors	304
	Index	305