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.

Researchers are encouraged to take advantage of software to implement missing value imputation, as estimates of activity are more precise and less biased in the presence of intermittent missing accelerometer data than those derived from an observed data analysis approach.

Planning a group‐randomized trial is a complex process. Care must be taken to delineate the research question, to create the research team, to plan the research design, to avoid potential design and analytic problems, to select variables of interest and their measures, to plan the intervention, and to ensure adequate power. Failure in any of these areas may lead to a dissappointing result; attention to each will increase the likelihood of success

OBJECTIVES: A randomized school based trial sought to increase fruit and vegetable consumption among children using a multicomponent approach. METHODS: The intervention, conducted in 20 elementary schools in St. Paul, targeted a multiethnic group of children who were in the fourth grade in spring 1995 and the fifth grade in fall 1995. The intervention consisted of behavioral curricula in classrooms, parental involvement, school food service changes, and industry support and involvement. Lunchroom observations and 24-hour food recalls measured food consumption. Parent telephone surveys and a health behavior questionnaire measured psychosocial factors. RESULTS: The intervention increased lunchtime fruit consumption and combined fruit and vegetable consumption, lunchtime vegetable consumption among girls, and daily fruit consumption as well as the proportion of total daily calories attributable to fruits and vegetables. CONCLUSIONS: Multicomponent school-based programs can increase fruit and vegetable consumption among children. Greater involvement of parents and more attention to increasing vegetable consumption, especially among boys, remain challenges in future intervention research.

OBJECTIVES. The Minnesota Heart Health Program is a 13-year research and demonstration project to reduce morbidity and mortality from coronary heart disease in whole communities. METHODS. Three pairs of communities were matched on size and type; each pair had one education site and one comparison site. After baseline surveys, a 5- to 6-year program of mass media, community organization, and direct education for risk reduction was begun in the education communities, whereas surveys continued in all sites. RESULTS. Many intervention components proved effective in targeted groups. However, against a background of strong secular trends of increasing health promotion and declining risk factors, the overall program effects were modest in size and duration and generally within chance levels. CONCLUSIONS. These findings suggest that even such an intense program may not be able to generate enough additional exposure to risk reduction messages and activities in a large enough fraction of the population to accelerate the remarkably favorable secular trends in health promotion activities and in most coronary heart disease risk factors present in the study communities.

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.

Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data.

When treatments are administered in groups, clients interact in ways that lead to violations of a key assumption of most statistical analyses-the assumption of independence of observations. The resulting dependencies, when not properly accounted for, can increase Type I errors dramatically. Of the 33 studies of group-administered treatment on the empirically supported treatments list, none appropriately analyzed their data. The current authors provide corrections that can be applied to improper analyses. After the corrections, only 12.4% to 68.2% of tests that were originally reported as significant remained significant, depending on what assumptions were made about how large the dependencies among observations really are. Of the 33 studies, 6-19 studies no longer had any significant results after correction. The authors end by providing recommendations for researchers planning group-administered treatment research.