Age differences in outcomes among patients in the "Stimulant Abuser Groups to Engage in 12-Step" (STAGE-12) intervention

Treatment effects on count outcomes with non-normal covariates

The effects of a treatment or an intervention on a count outcome are often of interest in applied research. When controlling for additional covariates, a negative binomial regression model is usually applied to estimate conditional expectations of the count outcome. The difference in conditional expectations under treatment and under control is then defined as the (conditional) treatment effect. While traditionally aggregates of these conditional treatment effects (e.g., average treatment effects) are computed by averaging over the empirical distribution, a recently proposed moment-based approach allows for computing aggregate effects as a function of distribution parameters. The moment-based approach makes it possible to control for (latent) multivariate normally distributed covariates and provides more reliable inferences under certain conditions. In this paper we propose three different ways to account for non-normally distributed continuous covariates in this approach: an alternative, known non-normal distribution; a plausible factorization of the joint distribution; and an approximation using finite Gaussian mixtures. A saturated model is used for categorical covariates, making a distributional assumption obsolete. We further extend the moment-based approach to allow for multiple treatment conditions and the computation of conditional effects for categorical covariates. An illustrative example highlighting the key features of our extension is provided.

Accounting for Latent Covariates in Average Effects from Count Regressions

The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this paper, we propose a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and manifest covariates. We extend the previously presented moment-based approach in several aspects: Building on a multigroup SEM framework for count variables instead of the generalized linear model, we allow for latent covariates and multiple covariates. We provide an illustrative example to explain the application and estimation in structural equation modeling software.

Prevalence Rates of Personality Disorder and Its Association With Methamphetamine Dependence in Compulsory Treatment Facilities in China

Methamphetamine use is popular and rapidly increasing in China, and the co-occurrence of personality disorders has an impact on treatment outcomes and may increase vulnerability of developing dependence. The aim of the present study was to investigate the prevalence rates of personality disorders in methamphetamine users and further explore the association between personality disorders and methamphetamine use status. Five hundred and seventy-seven male methamphetamine users were recruited. The self-developed questionnaire was used for demographics, and a Structural Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (SCID-I/II) was performed covering psychiatric diagnosis. Our study found the prevalence of antisocial personality disorder in male methamphetamine users was 71.4%, followed by borderline (20.2%) and obsessive-compulsive (17.9%) personality disorder. Borderline and antisocial personality disorders were found to be risk factors of methamphetamine dependence (adjusted odds ratio = 2.891, p = 0.007 and adjusted odds ratio = 1.680, p = 0.042). These findings suggested personality disorders were highly prevalent in male methamphetamine users, and the comorbidity of antisocial and borderline personality disorders are especially associated with methamphetamine dependence.