Sample size estimation and re-estimation of cluster randomized controlled trials for real-time feedback, debriefing, and retraining system of cardiopulmonary resuscitation for out-of-hospital cardiac arrests

Abstract

Background: In cluster randomized controlled trials (RCTs) for emergency medical services (EMS) system, we encounter the situation that the actual cluster size and ratio of allocated patients between two groups eventually differ from those used for sample size estimation because of the nature of patient enrollment. In such trials, estimations of effect size of test intervention and intra-cluster correlation coefficient (ICC) used for sample size estimation are also difficult. To improve efficient management on clinical cluster RCTs, we need to understand the effect of such inconsistencies of the design parameters on the type I error rate and statistical power of testing. Methods: We planned the trial which evaluated the 1-month favorable neurological survival of out-of-hospital cardiac arrest patients with or without real-time feedback, debriefing, and retraining system by EMS personnel. Under the conditions that we possibly encountered in this trial, we examined the effect of inconsistencies in the actual ICC, cluster size, and ratio of patient allocation with those expected for sample size estimation on the type I error rate and power, using simulation studies. We further investigated the contribution of incorporating sample size re-estimation, based on the results of interim analysis of the trial, on the power increase. Results: This simulation study showed that the inconsistencies of cluster size and patient allocation ratio decreased the power by 5–10% in some cases. In addition, the power decreased by 3–4% when the actual ICC was larger than that expected for sample size estimation. Furthermore, the use of a generalized estimating equation method to evaluate the difference in the 1-month favorable neurological survival between two groups caused inflation of type I error rate. Finally, the increase in power by incorporating sample size re-estimation was limited. Conclusions: We identified remarkable effects of sample size estimation and re-estimations in a cluster RCT for real-time feedback, debriefing, and retraining system of cardiopulmonary resuscitation for out-of-hospital cardiac arrests. The estimation of design parameters for sample size estimation is generally challenging in cluster RCTs for EMS system; therefore, it is important to conduct a trial simulation that assesses the statistical performances under sample sizes based on the various expected values of the design parameters before beginning the trial. Keywords: Cluster randomization, Emergency medical services, Intracluster correlation coefficient, Sample size re-estimation