Friendship Dissolution Within Social Networks Modeled Through Multilevel Event History Analysis.

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common-as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network.

Parental and adolescent health behaviors and pathways to adulthood.

This paper examines associations among parental and adolescent health behaviors and pathways to adulthood. Using data from the National Longitudinal Study of Adolescent to Adult Health, we identify a set of latent classes describing pathways into adulthood and examine health-related predictors of these pathways. The identified pathways are consistent with prior research using other sources of data.

Delineating prototypical patterns of substance use initiations over time.

Aims: The purpose of this paper is to discover patterns of drug use initiations over time through a multiple event process survival mixture model (MEPSUM model), a novel approach for substance use and prevention research.

Design: The MEPSUM model combines survival analysis and mixture modeling-specifically latent class analysis-to examine individual differences in the timing of initiation and cumulative risk of substance use over time, and is applied to cross-sectional survey data on drug initiations.

Setting: Data are drawn from the 2009 National Survey on Drug Use and Health.

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 traditional univariate survival analysis and univariate survival mixture analysis, the model approximates the underlying multivariate distribution of hazard functions via a discrete-point finite mixture in which the mixing components represent prototypical patterns of event occurrence.