Clinical Trials

❘❙❚■ Chapter 15 | Cluster Randomized TrialsAnalysis of cluster randomized trialsIn analyzing CRTs, the experimental unit is often used as the unit of analysis,although summary statistics can be made for each cluster. In PROMIS-UK,although randomization was at the level of the hospital, it was planned thatguideline adherence would be measured at both the patient and hospital levels,but analysis of subsequent outcome variables be done at the patient level.Cluster effectIn the analysis of CRTs, failure to control for the cluster effect (correlationbetween individuals within the same cluster) can lead to a biased estimate oftreatment effect, such as a P-value and confidence intervals that overstate thesignificance of the result, and hence have an inflated Type I error rate (rejectionof a true null hypothesis) [11,12]. This, in turn, increases the chances of spurioussignificant findings and misleading conclusions.Donner showed, using data from Murray et al., that the P-value for this specificstudy changed from 0.03 if the effect of clustering is ignored to >0.10 afteradjusting for the effect of clustering [8,13]. This example is typical, and shows thatthe evidence for a statistically significant treatment effect can be exaggeratedif the cluster effect is not taken into account in a CRT design.Statistical methods and modelsThe classic statistical methods and models, such as the t-test (see Chapter 19),are not appropriate for the analysis of CRTs because they are based on a strongassumption that all individuals in a sample are independent from each other(ie, there is no cluster effect in the sample). Fortunately, many advanced statisticalmethodologies have been developed to address the cluster effect in CRTs. Theseapproaches include the robust variance estimate method and the random effect(multilevel), general estimating equation, and Bayesian hierarchical models [11,12].The common thread of these techniques is to take into account the cluster effectby relaxing the independence assumption in their methodological developments.Of these models, the random effect model has been widely used because it not onlycontrols for a cluster effect, but also provides an estimate of the cluster effect.By applying a random effect model, we can assess to what extent the treatmenteffect could be contaminated by the cluster effect. Another advantage of thismodel is its ability to take into account heterogeneity due to other unobservablefactors. In addition, appropriate exploratory covariate adjustments can be made,adjusting for any major imbalances in the groups – including the type of hospital,the case mix of physicians treating patients (specialists versus nonspecialists),and patient characteristics.146