Components of variance in a group-randomized trial analysed via a random-coefficients model: the Rapid Early Action for Coronary Treatment (REACT) trial

Sample size calculations for group randomized trials with unequal group sizes through Monte Carlo simulations

Group randomized trial design is common in cancer prevention and health promotion research with intervention development. Several methods have been developed to handle the design and analytical issues for group randomized trial including the intraclass correlation coefficient. The widely used methods for the sample size calculation for the group randomized trial assume equal sizes across groups. In practice this assumption often fails and group randomized trial studies suffer from considerably lower statistical power than as planned. A few studies have developed sample size calculation methods for unequal group sizes, but most of them are limited to continuous outcomes. In this study, we develop a method for sample size calculation for group randomized trial studies with unequal group sizes based on Monte Carlo simulation in the mixed effect model framework. This approach incorporates the variation of group sizes and can be applied to group randomized trials with different types of outcomes. Further, it is easy to implement and can be applied to most commonly used group randomized trial designs such as pre-and-post cross-sectional and cohort study designs. We demonstrate the application of the proposed approach to two-arm group randomized trial studies with continuous and binary outcomes through simulations and analysis of a real group randomized trial dataset.

Multilevel factorial experiments for developing behavioral interventions: power, sample size, and resource considerations

Factorial experimental designs have many potential advantages for behavioral scientists. For example, such designs may be useful in building more potent interventions by helping investigators to screen several candidate intervention components simultaneously and to decide which are likely to offer greater benefit before evaluating the intervention as a whole. However, sample size and power considerations may challenge investigators attempting to apply such designs, especially when the population of interest is multilevel (e.g., when students are nested within schools, or when employees are nested within organizations). In this article, we examine the feasibility of factorial experimental designs with multiple factors in a multilevel, clustered setting (i.e., of multilevel, multifactor experiments). We conduct Monte Carlo simulations to demonstrate how design elements-such as the number of clusters, the number of lower-level units, and the intraclass correlation-affect power. Our results suggest that multilevel, multifactor experiments are feasible for factor-screening purposes because of the economical properties of complete and fractional factorial experimental designs. We also discuss resources for sample size planning and power estimation for multilevel factorial experiments. These results are discussed from a resource management perspective, in which the goal is to choose a design that maximizes the scientific benefit using the resources available for an investigation.

A barber-based intervention for hypertension in African American men: design of a group randomized trial

BACKGROUND

Barbershops constitute potential sites for community health promotion programs targeting hypertension (HTN) in African American men but such programs previously have not been formally evaluated.

METHODS

A randomized trial (ClinicalTrials.gov no. NCT00325533) will test whether a continuous HTN detection and medical referral program conducted by influential peers (barbers) in a receptive community setting (barbershops) can promote treatment-seeking behavior and thus lower blood pressure (BP) among the regular customers with HTN. Barbers will offer a BP check with each haircut and encourage appropriate medical referral using real stories of other customers modeling the desired behaviors. A cohort of 16 barbershops will go through a pretest/posttest group-randomization protocol. Serial cross-sectional data collection periods (10 weeks each) will be conducted by interviewers to obtain accurate snapshots of HTN control in each barbershop before and after 10 months of either barber-based intervention or no active intervention. The primary outcome is BP control: BP <135/85 mm Hg (nondiabetic subjects) and <130/80 mm Hg (diabetic subjects) measured in the barbershop during the 2 data collection periods. The multilevel analysis plan uses hierarchical models to assess the effect of covariates on HTN control and secondary outcomes while accounting for clustering of observations within barbershops.

CONCLUSIONS

By linking community health promotion to the health care system, this program could serve as a new model for HTN control and cardiovascular risk reduction in African American men on a nationwide scale.

Using Multilevel Mixtures to Evaluate Intervention Effects in Group Randomized Trials

There is evidence to suggest that the effects of behavioral interventions may be limited to specific types of individuals, but methods for evaluating such outcomes have not been fully developed. This study proposes the use of finite mixture models to evaluate whether interventions, and, specifically, group randomized trials, impact participants with certain characteristics or levels of problem behaviors. This study uses latent classes defined by clustering of individuals based on the targeted behaviors and illustrates the model by testing whether a preventive intervention aimed at reducing problem behaviors affects experimental users of illicit substances differently than problematic substance users or those individuals engaged in more serious problem behaviors. An illustrative example is used to demonstrate the identification of latent classes, specification of random effects in a multilevel mixture model, independent validation of latent classes, and the estimation of power for the proposed models to detect intervention effects. This study proposes specific steps for the estimation of multilevel mixture models and their power and suggests that this model can be applied more broadly to understand the effectiveness of interventions.

Sizing a trial to alter the trajectory of health behaviours: methods, parameter estimates, and their application

Group-randomized trials often involve repeat observations on the same participants. When there are no more than two observations from each participant, standard mixed-model regression methods for a pretest-posttest design can be used. When there are more than two observations from each participant, random coefficients models may be useful. This paper describes the random coefficients analysis appropriate to data from an extended nested cohort design and presents the methods for power analysis and sample size calculations based on that analysis. We provide estimates for the parameters required for those calculations for a number of adolescent health behaviours. We also show how the estimates can be used to plan a future trial.

Design and analysis of group-randomized trials: a review of recent methodological developments

We review recent developments in the design and analysis of group-randomized trials (GRTs). Regarding design, we summarize developments in estimates of intraclass correlation, power analysis, matched designs, designs involving one group per condition, and designs in which individuals are randomized to receive treatments in groups. Regarding analysis, we summarize developments in marginal and conditional models, the sandwich estimator, model-based estimators, binary data, survival analysis, randomization tests, survey methods, latent variable methods and nonlinear mixed models, time series methods, global tests for multiple endpoints, mediation effects, missing data, trial reporting, and software. We encourage investigators who conduct GRTs to become familiar with these developments and to collaborate with methodologists who can strengthen the design and analysis of their trials.

Methods to reduce the impact of intraclass correlation in group-randomized trials

This study reports intraclass correlation (ICC) for dependent variables used in group-randomized trials (GRTs). The authors also document the effect of two methods suggested to reduce the impact of ICC in GRTs; these two methods are modeling time and regression adjustment for covariates. They coded and analyzed 1,188 ICC estimates from 17 published, in press, and unpublished articles representing 21 studies. Findings confirm that both methods can improve the efficiency of analyses shown to be valid across conditions common in GRTs. Investigators planning GRTs should obtain ICC estimates matched to their planned analysis so that they can size their studies properly.