Comparative Effectiveness of Individual Versus Family-Based Substance Use Treatment on Adolescent Self-Injurious Thoughts and Behaviors 2022
Functions from the TWANG package have been used in each of the below publications (grouped by treatment setting). Also see TWANG Tutorials.
This study evaluates the effectiveness of motivational enhancement therapy/cognitive behavioral therapy–5 (MET/CBT-5) when delivered in community practice settings relative to standard community-based adolescent treatment.
Youth with marijuana problems who received a research-based treatment (motivational enhancement therapy plus cognitive behavioral therapy [MET/CBT5]) had better outcomes than similar youth treated in community-based programs.
Counter to widely held beliefs, pet ownership does not improve children's general or psychological health.
In 2009, RAND launched the Deployment Life Study to study military family readiness. This report presents analyses on marital relationships, family environment, psychological and behavioral health, child well-being, and military integration.
The study tested whether adolescents receiving substance abuse treatment at facilities offering full or partial mental health services have better 12-month substance use and mental health outcomes than youths at facilities with no mental health services.
To help fill the gap in the research on the effectiveness of military family support programs, a RAND study explored the curriculum, themes, and outcomes of Operation Purple, a free weeklong summer camp program for youth with a deployed parent.
People living in unaffordable housing are more likely to rate their health as poor.
Chapter in Propensity score analysis: Fundamentals and developments edited by Wei Pan and Haiyan Bai. Published in 2015 by Guildford Publications, Inc, New York.
While the underlying theory of collaborative targeted minimum loss-based estimation is quite general, current implementations involve greedy stepwise selection of covariates into the exposure model.
This discussion highlights potentially meaningful ways to optimize propensity score machine learning methods to allow for minimal bias and less variability.
Synthetic estimation is a promising approach when patient noncompliance is affecting a clinical trial.
While the underlying theory of collaborative targeted minimum loss-based estimation is quite general, current implementations involve greedy stepwise selection of covariates into the exposure model.
The use of propensity scores to control for pretreatment imbalances on observed variables in non-randomized or observational studies examining the causal effects of treatments or interventions has become widespread over the past decade.
The purpose of this study was to compare the relative effectiveness of three treatment modalities for adolescent substance use: biological drug screening (BDS), Motivational Enhancement Therapy-Cognitive Behavioral Therapy (MET/CBT5), and BDS combined with MET/CBT5, relative to no treatment.
We use a marginal structural model with inverse-probability-of-treatment weighting to estimate the causal effects of cumulative treatment experiences over a period of 9 months on drug use at the end of 1-year among 2870 adolescents receiving care in community-based treatment settings.
This article considers the problem of examining causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and putative confounders are time-varying.
Forthcoming.
This article considers the problem of examining causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and putative confounders are time-varying.
This article considers the problem of examining causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and putative confounders are time-varying.
Studies have reported relationships between urban sprawl, physical activity, and obesity, but – to date – no studies have considered the relationship between sprawl and coronary heart disease (CHD) endpoints.
In this paper, we describe the key steps for implementing a code quality assurance process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results.
Clinical studies aimed at identifying effective treatments to reduce the risk of disease or death often require long term follow-up of participants in order to observe a sufficient number of events to precisely estimate the treatment effect.
The authors developed a new tool for assessing the sensitivity of findings on treatment effectiveness to differential follow-up rates in the two treatment conditions being compared.
Participants in longitudinal studies on the effects of drug treatment and criminal justice system interventions are at high risk for institutionalization. Use of principal stratification to control for institutionalization at follow-up is evaluated.
Develops a causal modeling framework for evaluating the standard approaches for addressing the confounding of the effects of ignoring institutionalization of drug treatment clients and other effects, and illustrates the results of each approach.