Abstract
Subjects in randomized controlled trials do not always comply to the treatment condition they have been assigned to. This may cause the estimated effect of the intervention to be biased and also affect efficiency, coverage of confidence intervals, and statistical power. In cluster randomized trials non‐compliance may occur at the subject level but also at the cluster level. In the latter case, all subjects within the same cluster have the same compliance status. The purpose of this study is to investigate the statistical implications of non‐compliance in cluster randomized trials. A simulation study was conducted with varying degrees of non‐compliance at either the cluster level or subject level. The probability of non‐compliance depends on a covariate at the cluster or subject level. Various realistic values of the intraclass correlation coefficient and cluster size are used. The data are analyzed by intention to treat, as treated, per protocol and the instrumental variable approach. The results show non‐compliance may result in downward biased estimates of the intervention effect and an under‐ or overestimate of its standard deviation. The coverage of the confidence intervals may be too small, and in most cases, empirical power is too small. The results are more severe when the probability of non‐compliance increases and the covariate that affects compliance is unobserved. It is advocated to avoid non‐compliance. If this is not possible, compliance status and covariates that affect compliance should be measured and included in the statistical model.
Highlights
The cluster randomized trial design is often used in the biomedical, health, and behavioral sciences
For moderate probabilities of non-compliance at the subject level, per protocol (PP) may result in a smaller standard deviation and an increased power in case the covariate is included in the model
As we mentioned in the introduction, our simulation study extends previous simulation studies by Jo and co-authors[29,30] as it does take intention to treat analysis (ITT) and complier average causal effect (CACE) into account and AT and PP
Summary
The cluster randomized trial design is often used in the biomedical, health, and behavioral sciences. With cluster randomized trials complete clusters, such as school classes, households, or general practices are randomized to treatment conditions.[1,2,3,4,5] The effect of an intervention, relative to a control, can only be estimated without bias when the subjects who are randomized to intervention receive the intervention. Non-compliance often occurs, and in cluster randomized trials, it may occur at the level of the subject and at the level of the cluster. MOERBEEK AND VAN SCHIE an example a trial on new dietary guidelines. Non-compliance at the cluster level occurs when complete families do not comply to these new guidelines, while non-compliance at the subject level occurs when a few but not all family members do not comply
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