title: Meta-analysis of aggregate data on medical events.
abstract: Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship, and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared with time-to-event methods. To deal with differences in follow-up-at the cost of assuming specific distributions for event and drop-out times-we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases, and discontinuations in each group, as well as the planned trial duration and groups sizes. Such a model also enables exchangeability assumptions about parameters of survival distributions, for which they are more appropriate than for the expected proportion of patients with an event across trials of substantially different length. Borrowing information from other trials within a meta-analysis or from historical data is particularly useful for rare events data. Prior information or exchangeability assumptions also avoid the parameter identifiability problems that arise when using more flexible event and drop-out time distributions than the exponential one. We discuss the derivation of robust historical priors and illustrate the discussed methods using an example. We also compare the proposed approach against other aggregate data meta-analysis methods in a simulation study. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Biostatistical Sciences and Pharmacometrics, Novartis Pharma AG, Basel, Switzerland.
authors.forename: Björn
authors.initials: B
authors.lastname: Holzhauer
mesh_terms: D002318:Cardiovascular Diseases / Q000139:chemically induced; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy; D006801:Humans; D007004:Hypoglycemic Agents / Q000009:adverse effects / Q000627:therapeutic use; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D000077154:Rosiglitazone; D013223:Statistics as Topic; D045162:Thiazolidinediones / Q000009:adverse effects / Q000627:therapeutic use; D013997:Time Factors
chemical_list: D007004:Hypoglycemic Agents; D045162:Thiazolidinediones; D000077154:Rosiglitazone
keywords: aggregate data; exchangeability; historical data; meta-analysis; time-to-event