title: Using historical control information for the design and analysis of clinical trials with overdispersed count data.
abstract: Results from clinical trials are never interpreted in isolation. Previous studies in a similar setting provide valuable information for designing a new trial. For the analysis, however, the use of trial-external information is challenging and therefore controversial, although it seems attractive from an ethical or efficiency perspective. Here, we consider the formal use of historical control data on lesion counts in a multiple sclerosis trial. The approach to incorporating historical data is Bayesian, in that historical information is captured in a prior that accounts for between-trial variability and hence leads to discounting of historical data. We extend the meta-analytic-predictive approach, a random-effects meta-analysis of historical data combined with the prediction of the parameter in the new trial, from normal to overdispersed count data of individual-patient or aggregate-trial format. We discuss the prior derivation for the lesion mean count in the control group of the new trial for two populations. For the general population (without baseline enrichment), with 1936 control patients from nine historical trials, between-trial variability was moderate to substantial, leading to a prior effective sample size of about 45 control patients. For the more homogenous population (with enrichment), with 412 control patients from five historical trials, the prior effective sample size was approximately 63 patients. Although these numbers are small relative to the historical data, they are fairly typical in settings where between-trial heterogeneity is moderate. For phase II, reducing the number of control patients by 45 or by 63 may be an attractive option in many multiple sclerosis trials.
authors.affiliation: Novartis Pharma AG, CH-4002 Basel, Switzerland. sandro.gsteiger@a3.epfl.ch
authors.forename: Sandro
authors.initials: S
authors.lastname: Gsteiger
authors.forename: Beat
authors.initials: B
authors.lastname: Neuenschwander
authors.forename: Francois
authors.initials: F
authors.lastname: Mercier
authors.forename: Heinz
authors.initials: H
authors.lastname: Schmidli
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D035061:Control Groups; D006801:Humans; D015201:Meta-Analysis as Topic; D009103:Multiple Sclerosis / Q000473:pathology*; D012107:Research Design; D018401:Sample Size
keywords: Bayesian inference; historical data; meta-analytic predictive; negative binomial distribution; overdispersed counts