Testing the Impact of the Whole-Day Good Behavior Game on Aggressive Behavior: Results of a Classroom-Based Randomized Effectiveness Trial.

Three generations of developmental epidemiologically based randomized field trials of the Good Behavior Game (GBG) have been delivered to Baltimore elementary schools. With the collaboration of family and community partners, all three trials were directed at decreasing proximal targets of aggressive behavior and improving learning in first-grade classrooms with distal mental health and substance abuse outcomes. GBG is a group-contingent classroom behavior management strategy that promotes classmate/peer concern for each child's positive behavior by rewarding teams with below-criterion levels of aggressive, disruptive behavior. GBG targets early risk factors for the above distal outcomes: aggressive, disruptive behavior, family/school relationships, and school failure. Here, we report on the third-generation randomized prevention trial of the GBG (whole-day first grade program (WD)), including 12 elementary schools. WD enhanced the standard curriculum in the areas of classroom behavior management; academic instruction, particularly reading; and family-classroom partnerships. Using a within-school classroom randomized trial design, we: 1) evaluate the effectiveness of the WD program by sex and cohort and 2) measure variation in WD impact by the quality of teachers' behavior management practices. Data from 961 first graders were used in general growth mixture modeling that accounts for classroom randomization to identify distinct developmental trajectories of aggressive, disruptive behavior and GBG impact on these trajectories. In the chronic high aggression trajectory of males, ratings of aggression after WD implementation and to the end of third grade were significantly lower in the WD condition than in controls in classrooms with a higher WD dosage (Cohort 2) and especially in classrooms with higher quality of WD implementation. For females, we found a modest but significant benefit of GBG in the low trajectory class when cohorts were combined. Regarding policy implications, embedding GBG into the curricula in teacher's colleges could better support student learning and behavior. Clinical Trials Registration number: NCT00257088.

Statistical properties of four effect-size measures for mediation models.

This project examined the performance of classical and Bayesian estimators of four effect size measures for the indirect effect in a single-mediator model and a two-mediator model. Compared to the proportion and ratio mediation effect sizes, standardized mediation effect-size measures were relatively unbiased and efficient in the single-mediator model and the two-mediator model. Percentile and bias-corrected bootstrap interval estimates of ab/s Y , and ab(s X )/s Y in the single-mediator model outperformed interval estimates of the proportion and ratio effect sizes in terms of power, Type I error rate, coverage, imbalance, and interval width. For the two-mediator model, standardized effect-size measures were superior to the proportion and ratio effect-size measures. Furthermore, it was found that Bayesian point and interval summaries of posterior distributions of standardized effect-size measures reduced excessive relative bias for certain parameter combinations. The standardized effect-size measures are the best effect-size measures for quantifying mediated effects.

Estimating drug effects in the presence of placebo response: causal inference using growth mixture modeling.

Placebo-controlled randomized trials for antidepressants and other drugs often show a response for a sizeable percentage of the subjects in the placebo group. Potential placebo responders can be assumed to exist also in the drug treatment group, making it difficult to assess the drug effect. A key drug research focus should be to estimate the percentage of individuals among those who responded to the drug who would not have responded to the placebo ('Drug Only Responders'). This paper investigates a finite mixture model approach to uncover percentages of up to four potential mixture components: Never Responders, Drug Only Responders, Placebo Only Responders, and Always Responders. Two examples are used to illustrate the modeling, a 12-week antidepressant trial with a continuous outcome (Hamilton D score) and a 7-week schizophrenia trial with a binary outcome (illness level). The approach is formulated in causal modeling terms using potential outcomes and principal stratification. Growth mixture modeling (GMM) with maximum-likelihood estimation is used to uncover the different mixture components. The results point to the limitations of the conventional approach of comparing marginal response rates for drug and placebo groups. It is useful to augment such reporting with the GMM-estimated prevalences for the four classes of subjects and the Drug Only Responder drug effect estimate.