Analysis Of Cluster Randomized Trial Research Articles

Inference for the treatment effect in longitudinal cluster randomized trials when treatment effect heterogeneity is ignored.

In cluster-randomized trials, sometimes the effect of the intervention being studied differs between clusters, commonly referred to as treatment effect heterogeneity. In the analysis of stepped wedge and cluster-randomized crossover trials, it is possible to include terms in outcome regression models to allow for such treatment effect heterogeneity yet this is not frequently considered. Outside of some simulation studies of specific cases where the outcome is binary, the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator is unknown. We analytically examine the impact of failing to include terms for treatment effect heterogeneity on the variance of the treatment effect estimator, when outcomes are continuous. Using analysis of variance and feasible generalized least squares we provide expressions for this variance. For both the cluster-randomized crossover design and the stepped wedge design, our analytic derivations indicate that failing to include treatment effect heterogeneity results in the estimates for variance of the treatment effect that are too small, leading to inflation of type I error rates. We therefore recommend assessing the sensitivity of sample size calculations and conclusions drawn from the analysis of cluster randomized trials to the inclusion of treatment effect heterogeneity.

Effect of imbalance and intracluster correlation coefficient in cluster randomization trials with binary outcomes when the available number of clusters is fixed in advance

In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability ( κ) and the intracluster correlation coefficient ( ρ) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case ( κ = 1.0), i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group ( k ⁎) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group ( k ⁎) is at least 20 and the imbalance parameter ( κ) is at least 0.8. Empirical powers were close to the nominal level when ( ρ ≤ 0.02, κ ≥ 0.8, and k ⁎ = 10) or ( ρ ≤ 0.02, κ = 0.8, and k ⁎ = 20).

A Bayesian approach for analyzing a cluster-randomized trial with adjustment for risk misclassification

Bayesian hierarchical modelling techniques have some advantages over classic methods for the analysis of cluster-randomized trial. Bayesian approach is also becoming more popular to deal with measurement error and misclassification problems. We propose a Bayesian approach to analyze a cluster-randomized trial with adjusting for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. Both simulated numerical example and real clinical example are given to illustrate the proposed approach. The Bayesian approach has great potential to be used in misclassification problem in generalized linear mixed model (GLMM) since it allow us to fit complex models and identify all the parameters. Our results suggest that Bayesian approach for analyzing cluster-randomized trial and adjusting for misclassification in GLMM is flexible and powerful.

A comparison of the statistical power of different methods for the analysis of cluster randomization trials with binary outcomes

Cluster randomization trials are randomized controlled trials (RCTs) in which intact clusters of subjects are randomized to either the intervention or to the control. Cluster randomization trials require different statistical methods of analysis than do conventional randomized controlled trials due to the potential presence of within-cluster homogeneity in responses. A variety of statistical methods have been proposed in the literature for the analysis of cluster randomization trials with binary outcomes. However, little is known about the relative statistical power of these methods to detect a statistically significant intervention effect. We conducted a series of Monte Carlo simulations to examine the statistical power of three methods that compare cluster-specific response rates between arms of the trial: the t-test, the Wilcoxon rank sum test, and the permutation test; and three methods that compare subject-level response rates: an adjusted chi-square test, a logistic-normal random effects model, and a generalized estimating equations (GEE) method. In our simulations we allowed the number of clusters, the number of subjects per cluster, the intraclass correlation coefficient and the magnitude of the intervention effect to vary. We demonstrated that the GEE approach tended to have the highest power for detecting a statistically significant intervention effect. However, in most of the 240 scenarios examined, the differences between the competing statistical methods were negligible. The largest mean difference in power between any two different statistical methods across the 240 scenarios was 0.02. The largest observed difference in power between two different statistical methods across the 240 scenarios and 15 pair-wise comparisons of methods was 0.14.

Group sequential methods for cluster randomization trials with binary outcomes

Cluster randomization trials in which intact social units are randomly assigned to different intervention groups have become very popular in recent years, particularly for the evaluation of innovations in the delivery of health care. An extensive literature dealing with the associated methodological challenges has also appeared. Although the monitoring of such trials using formal stopping rules is clearly indicated when the outcomes are irreversible and individual-level data are available sequentially, simple and reliable statistical methods that may be used for this purpose are currently not available. To investigate the validity of standard group sequential methods when applied to cluster randomization trials having binary outcomes. The large sample distributions for each of five test statistics computed from sequentially accumulated data are derived. A simulation study is performed to evaluate the finite sample properties of these statistics when applied to the interim analysis of cluster randomization trials. Data from the World Health Organization antenatal care trial are used to illustrate the methods. Each of the joint distributions is shown to be characterized by a covariance structure that asymptotically satisfies an independent increments structure, a foundation that simplifies group sequential methods. The simulation study reveals that four of the five test statistics evaluated provide satisfactory performance with as few as 10 clusters allocated to each of two interventions. The applicability of our results to effect estimation following a group sequential cluster randomization trial is not investigated, although a theoretical foundation which may be used for this purpose is presented. Standard group sequential methods can be applied to cluster randomization trials when interim analyses are warranted.

Current and future challenges in the design and analysis of cluster randomization trials.

Randomized trials in which the unit of randomization is a community, worksite, school or family are becoming widely used in the evaluation of life-style interventions for the prevention of disease. The increasing interest in adopting a cluster randomization design is being matched by rapid methodological developments. In this paper we describe several of these developments. Brief mention is also made of issues related to economic analysis and to the planning and conduct of meta-analyses for cluster randomization trials. Recommendations for reporting are also discussed.

Book Review: Design and analysis of cluster randomization trials

Book Review: Design and analysis of cluster randomization trials

Design and Analysis of Cluster Randomization Trials in Health Research.

Design and Analysis of Cluster Randomization Trials in Health Research.

Book Reviews

McCONWAY, K. J., JONES, M. C., and TAYLOR, P. C. Statistical Modelling using Genstat.SCHINAZI, R. B. Classical and Spatial Point Processes.PERRY, J. E., SMITH, R. H., WOIWOD, I. P., and MORSE, D. R. (editors). Chaos in Real Data: The Analysis of Non‐linear Dynamics from Short Ecological Time Series.BURTON, R. F. Physiology by Numbers, 2nd edition.BERLINER, L. M., NYCHKA, D., and HOAR, T. (editors). Studies in the Atmospheric Sciences.PRAKASA RAO, B. L. S. Statistical Inference for Diffusion Type Processes.DONNER, A. and KLAR, N. Design and Analysis of Cluster Randomization Trials in Health Research.MATIS, J. H. and KIFFE, T. R. Stochastic Population Models: A Compartmental Perspective.LINDSEY, J. K. Models for Repeated Measurements, 2nd edition.FORD, E. D. Scientific Method for Ecological Research.BURNHAM, K. P. and ANDERSON, D. R. Model Selection and Inference: A Practical Information‐Theoretic Approach.COX, D. R. and REID, N. The Theory of the Design of Experiments.PINHEIRO, J. C. and BATES, D. M. Mixed‐effects models in S and S‐PLUS.VENABLES, W. N. and RIPLEY, B. D. S Programming.KRAUSE, A. and OLSON, M. The Basics of S and S‐PLUS.CHEN, M.‐H., SHAO, Q.‐M., and IBRAHIM, J. G. Monte Carlo Methods in Bayesian Computation.SAHAI, H. and AGEEL, M. L. The Analysis of Variance: Fixed, Random, and Mixed Models.EVERITT, B. S. and DUNN, G. Statistical Analysis of Medical Data: New Developments.MUKHOPADHYAY, N. Probability and Statistical Inference.KNIGHT, K. Mathematical Statistics.Brief reports by the editor MILLER, R. E. Optimization: Foundations and Applications.BLAND, M. An Introduction to Medical Statistics, 3rd edition.RABE‐HESKETH, S. and EVERITT, B. A Handbook of Statistical Analyses using Stata, 2nd edition.KOO, J. O. (editor). American Series in Mathematical and Management Sciences Volume 42. Modern Mathematical, Management, and Statistical Sciences, The Index to the 20th Century, Prologue to the 21st Century.MISHRA, S. N. and SHARMA, B. D. (editors). American Series in Mathematical and Management Sciences Volume 43. FIM‐I, Forum for Interdisciplinary Mathematics Proceedings on Statistical Inference, Combinatorics and Related Areas, Volume I of Proceedings of Banaras Hindu University (BHU) Conference (Varanasi, India, December 1997).VENABLES, W. N. and RIPLEY, B. D. Modern Applied Statistics with S‐PLUS, 3rd edition.

Some Aspects of the Design and Analysis of Cluster Randomization Trials

SUMMARY Trials which randomize intact social groups, or clusters, to different interventions are becoming increasingly widespread. Although statistically less efficient than trials which randomize individuals, such designs are often preferred from a practical or ethical point of view, particularly in the evaluation of health care or educational strategies. We discuss selected issues that arise in the conduct of such trials, including the choice of design, ethical implications, sample size estimation and approaches to the analysis. The discussion is closely tied to methodological issues that have arisen in a recent evaluation trial of a new antenatal care programme, as sponsored by the Special Programme of Research, Development and Research Training in Human Reproduction of the World Health Organization.

Cluster randomization trials in epidemiology: theory and application

Abstract It is becoming increasingly common for epidemiologists to consider randomizing intact clusters (e.g. families, schools, communities) rather than individuals in experimental trials. Reasons are diverse, but include administrative convenience, a desire to reduce the effect of treatment contamination and the need to avoid ethical issues which might otherwise arise. Dependencies among cluster members typical of such designs must be considered when determining sample size and analyzing the resulting data. Well-known methods such as generalized least squares can be used to analyze continuous outcome data, while methods for the analysis of binary outcome data and correlated failure time data are in the development stage. The purpose of this paper is to review methods used in the design and analysis of cluster randomization trials applied in health sciences research.