Sample size estimation for recurrent event data using multifrailty and multilevel survival models

Introduction In epidemiology and clinical research, recurrent events refer to individuals who are likely to experience transient clinical events repeatedly over an observation period. Examples include hospitalizations in patients with heart failure, fractures in osteoporosis studies and the occurrence of new lesions in oncology. Methodes We provided an in-depth analysis of the sample size required for the analysis of recurrent time-to-event data using multifrailty or multilevel survival models. We covered the topic from the simple shared frailty model to a hierarchical or joint modeling. We relied on a Wald-type test statistic to estimate the sample size assuming either a single or multiple endpoints. Resultats The simulations revealed that power decreased as heterogeneity increased. We also observed that it was preferable to maximize the number of patients (rather than the number of events per patient) as power increased with the number of patients for a fixed number of events. Conclusion Each model investigated can address the question of the number of subjects for recurrent events. However, depending on the research question, one model will be more suitable than another. We illustrated our methodology with the AFFIRM-AHF trial investigating the effect of intravenous ferric carboxymaltose in patients hospitalised for acute heart failure. Mots clés Sample size; Frailty model; Recurrent events; Randomized; Joint models Déclaration de liens d'intérêts Les auteurs n'ont pas précisé leurs éventuels liens d'intérêts.