Discrete-Time Survival Mixture Analysis

Discrete-Time Survival Mixture Analysis

This paper proposes a general latent variable approach to discrete-time survival analysis of non-repeatable events such as onset of drug use. It is shown how the survival analysis can be formulated as a generalized latent class analysis of event history indicators. The latent class analysis can use covariates and can be combined with the joint modeling of other outcomes such as repeated measure...

A discrete-time Multiple Event Process Survival Mixture (MEPSUM) model.

Traditional survival analysis was developed to investigate the occurrence and timing of a single event, but researchers have recently begun to ask questions about the order and timing of multiple events. A multiple event process survival mixture model is developed here to analyze nonrepeatable events measured in discrete-time that may occur at the same point in time. Building on both traditiona...

Parental Monitoring During Early Adolescence Deters Adolescent Sexual Initiation: Discrete-Time Survival Mixture Analysis

We used discrete-time survival mixture modeling to examine 5,305 adolescents from the 1997 National Longitudinal Survey of Youth regarding the impact of parental monitoring during early adolescence (ages 14-16) on initiation of sexual intercourse and problem behavior engagement (ages 14-23). Four distinctive parental-monitoring groups were identified and labeled as "High," "Increasing," "Decrea...

Discrete-Time Survival Factor Mixture Analysis for Low-Frequency Recurrent Event Histories.

In this article, the latent class analysis framework for modeling single event discrete-time survival data is extended to low-frequency recurrent event histories. A partial gap time model, parameterized as a restricted factor mixture model, is presented and illustrated using juvenile offending data. This model accommodates event-specific baseline hazard probabilities and covariate effects; even...

Discrete-time systems analysis