Mixture Modeling of Patterns of Substance Use: Taking into Account Measurement Bias

点击翻译按钮获取中文摘要

 DESCRIPTION (provided by applicant): Substance abuse in adolescence is an epidemic which takes a significant toll on society and has deleterious physical, psychological, and social effects on adolescents and their families. As such, a great deal of research in recent years has focused broadly on finding patterns among social and behavioral determinants of substance abuse in adolescence, and specifically on the goal of finding clinically relevant subgroups of adolescents according to substance abuse behaviors. This goal is reflected by a recent NIDA program announcement, PA-15-003, entitled Epidemiology of Drug Abuse; this announcement lists trajectories of drug use and patterns of comorbidity with substance use disorders, two areas frequently explored using subgrouping strategies, as areas of interest. The goal of forming meaningful behavioral subtypes has largely been pursued using mixture models, a broad class of models which seeks to divide the sample into meaningful subgroups, known as latent classes, of subjects. Mixture models have seen extensive use in substance abuse studies in the past decade, with behavioral groupings being linked to imaging data, biomarkers, and genetic information. However, despite the their widespread use, virtually all current applications of mixture models have failed to take into account one critical aspect of behavioral measurement known as differential item functioning (DIF). Given an item intended to measure some underlying construct, DIF occurs when subjects with the same level of that underlying construct differ systematically in their responses to an item measuring that construct due to their gender, race, age, or any other individual characteristic. When DIF occurs and is not accounted for, it may render estimates of the level of the underlying construct biased, and inferences about differences between groups invalid. Though techniques for measuring continuous latent variables such as item response theory (IRT) and confirmatory factor analysis (CFA) have given serious attention to DIF as a fundamental threat to the validity of inferences, there has not yet been systematic study of DIF in the measurement of latent classes in mixture models, and there are no known applications of DIF analyses to mixture model results in the substance abuse literature. The proposed research seeks to rectify this shortcoming by rigorously investigating DIF in mixture models with a focus on behavioral studies of substance abuse. The four aims of the proposed project are to: (1) analytically define the ways that DIF can manifest in mixture models; (2) develop a flexible test of DIF in mixture models; (3) use a computer simulation to test (a) the consequences of un-modeled DIF in mixture models and (b) the efficacy of the test developed in Aim 2; and (4) to use DIF modeling techniques in an empirical study of substance abuse in the transition to adulthood. Through the pursuit of these aims, this project will allow fo mixture models to better consider individual differences, thereby enhancing the generalizability of these results in behavioral substance abuse research.