Analysis Of Group-randomized Trials Research Articles

Essential Ingredients and Innovations in the Design and Analysis of Group-Randomized Trials.

This article reviews the essential ingredients and innovations in the design and analysis of group-randomized trials. The methods literature for these trials has grown steadily since they were introduced to the biomedical research community in the late 1970s, and we summarize those developments. We review, in addition to the group-randomized trial, methods for two closely related designs, the individually randomized group treatment trial and the stepped-wedge group-randomized trial. After describing the essential ingredients for these designs, we review the most important developments in the evolution of their methods using a new bibliometric tool developed at the National Institutes of Health. We then discuss the questions to be considered when selecting from among these designs or selecting the traditional randomized controlled trial. We close with a review of current methods for the analysis of data from these designs, a case study to illustrate each design, and a brief summary.

An evaluation of constrained randomization for the design and analysis of group-randomized trials with binary outcomes.

Group-randomized trials are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group-level covariates between arms. Therefore, covariate-based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a prespecified percentage of candidate allocations, and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs, with varying degrees of analysis-based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F-test, the KC-corrected GEE t-test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387-1396), and two permutation tests when the prognostic group-level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis-based adjustment for non-prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed.

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e.g., augmented generalized estimating equations, targeted maximum likelihood, and quadratic inference functions). In addition, we describe developments in analysis of alternative group designs (including stepped-wedge GRTs, network-randomized trials, and pseudocluster randomized trials), which require clustering to be accounted for in their design and analysis.

Review of Recent Methodological Developments in Group-Randomized Trials: Part 1-Design.

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have highlighted the developments of the past 13 years in design with a companion article to focus on developments in analysis. As a pair, these articles update the 2004 review. We have discussed developments in the topics of the earlier review (e.g., clustering, matching, and individually randomized group-treatment trials) and in new topics, including constrained randomization and a range of randomized designs that are alternatives to the standard parallel-arm GRT. These include the stepped-wedge GRT, the pseudocluster randomized trial, and the network-randomized GRT, which, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis.

Ignoring the group in group-level HIV/AIDS intervention trials: a review of reported design and analytic methods

Studies evaluating the efficacy of HIV/AIDS interventions often involve the random assignment of groups of participants or the treatment of participants in groups. These studies require analytic methods that take within-group correlation into account. We reviewed published studies to determine the extent to which within-group correlation was dealt with properly. We reviewed group-randomized trials (GRTs) and individually randomized group treatment (IRGT) trials published in HIV/AIDS and general public health journals 2005-2009. At least two of the authors reviewed each article, recording descriptive characteristics, sample size estimation methods, analytic methods, and judgments about whether the methods took intraclass correlation into account properly. Of those articles including sufficient information to judge whether analytic methods were correct, only 24% used only appropriate methods for dealing with the intraclass correlation. The percentages differed substantially for GRTs (41.7%) and IRGT trials (8.0%). Most of the articles (69.2%) also made no mention of a priori sample size estimation. A majority of the articles in our review reported analyses ignoring the intraclass correlation. This practice may result in underestimated variance, inappropriately small P values, and incorrect conclusions about the effectiveness of interventions. Previous trials that were analyzed incorrectly need to be re-analyzed, and future trials should be designed and analyzed with appropriate methods. Also, journal reviewers and editors need to be aware of the special requirements for design and analysis of GRTs and IRGT trials and judge the quality of articles reporting on such trials according to appropriate standards.

Design and Analysis of Group-Randomized Trials in Cancer: A Review of Current Practices

Previous reviews have identified problems in the design and analysis of group-randomized trials in a number of areas. Similar problems may exist in cancer research, but there have been no comprehensive reviews. We searched Medline and PubMed for group-randomized trials focused on cancer prevention and control that were published between 2002 and 2006. We located and reviewed 75 articles to determine whether articles included evidence of taking group randomization into account in establishing the size of the trial, such as reporting the expected intraclass correlation, the group component of variance, or the variance inflation factor. We also examined the analytical approaches to determine their appropriateness. Only 18 (24%) of the 75 articles documented appropriate methods for sample size calculations. Only 34 (45%) limited their reports to analyses judged to be appropriate. Fully 26 (34%) failed to report any analyses that were judged to be appropriate. The most commonly used inappropriate analysis was an analysis at the individual level that ignored the groups altogether. Nine articles (12%) did not provide sufficient information. Many investigators who use group-randomized trials in cancer research do not adequately attend to the special design and analytic challenges associated with these trials. Failure to do so can lead to reporting type I errors as real effects, mislead investigators and policy-makers, and slow progress toward control and prevention of cancer. A collaborative effort by investigators, statisticians, and others will be required to ensure that group-randomized trials are planned and analyzed using appropriate methods so that the scientific community can have confidence in the published results.

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.

Design and analysis of group-randomized trials: a review of recent practices.

We reviewed group-randomized trials (GRTs) published in the American Journal of Public Health and Preventive Medicine from 1998 through 2002 and estimated the proportion of GRTs that employ appropriate methods for design and analysis. Of 60 articles, 9 (15.0%) reported evidence of using appropriate methods for sample size estimation. Of 59 articles in the analytic review, 27 (45.8%) reported at least 1 inappropriate analysis and 12 (20.3%) reported only inappropriate analyses. Nineteen (32.2%) reported analyses at an individual or subgroup level, ignoring group, or included group as a fixed effect. Hence increased vigilance is needed to ensure that appropriate methods for GRTs are employed and that results based on inappropriate methods are not published.

Design and Analysis of Group-Randomized Trials

Design and Analysis of Group-Randomized Trials