Time‐varying effect models for ordinal responses with applications in substance abuse research

Abstract

Ordinal responses are very common in longitudinal data collected from substance abuse research or other behavioral research. This study develops a new statistical model with free SAS macro’s that can be applied to characterize time-varying effects on ordinal responses. Our simulation study shows that the ordinal-scale time-varying effects model has very low estimation bias and sometimes offers considerably better performance when fitting data with ordinal responses than a model that treats the response as continuous. Contrary to a common assumption that an ordinal scale with several levels can be treated as continuous, our results indicate that it is not so much the number of levels on the ordinal scale but rather the skewness of the distribution that makes a difference on relative performance of linear versus ordinal models. We use longitudinal data from a well-known study on youth at high risk for substance abuse as a motivating example to demonstrate that the proposed model can characterize the time-varying effect of negative peer influences on alcohol use in a way that is more consistent with the developmental theory and existing literature, in comparison to the linear time-varying effect model.

Time-varying effect modeling to address new questions in behavioral research: Examples in marijuana use.

“…The use of TVEM to examine associations as a flexible function of developmental age has only recently been explored (see Dziak, Li, Zimmerman, & Buu, 2014; Evans-Polce, Vasilenko, & Lanza, 2015; Russell, Vasilenko, & Lanza, in press; Schuler, Vasilenko, & Lanza, 2015; Vasilenko & Lanza, 2014). As an example, one study estimated the prevalence of sexual risk behavior and the age-varying effect of depression, a key risk factor that has been linked to sexual risk, from adolescence through early adulthood (Vasilenko & Lanza, 2014).…”

Marijuana use trajectories among drug-using youth presenting to an urban emergency department: Violence and social influences

“…For example, violence is associated with substance use, particularly among those residing in socio-economically disadvantaged communities (Goldstick et al, 2015; Goldstick et al, 2016; Walton et al, 2009; White et al, 2009), likely due to shared risk and promotive factors for some substances (e.g., marijuana), and/or acute pharmacological effects of substances (e.g., alcohol) (Chermack and Giancola, 1997; White et al, 2009). Although parental influences are important during younger ages, peers are the most robust influence for substance use during the transition to adulthood (Abadi et al, 2011; Allen et al, 2012; Brechwald and Prinstein, 2011; Burk et al, 2012; Chein et al, 2011; Dziak et al, 2014; Simons-Morton and Farhat, 2010; Wolfe et al, 2012), increasing exposure to deviant social contexts and distancing youth from protective influences.…”

A time-varying effect model for studying gender differences in health behavior

“…In spite of the fact that the proposed model can handle a variety of measurement scales under the framework of generalized linear models such as continuous and binary outcomes (as demonstrated in our motivating examples), future work is needed to extend the model to deal with other scales that are also common in the health behavior field including ordinal outcomes 10 and zero-inflated counts. 28,29 Furthermore, our work in this paper focuses on the setting that involves a single health behavior.…”