 Meta-analytic-predictive use of historical variance data for the design and analysis of clinical trialsHeinz Schmidli, Beat Neuenschwander and Tim Friede Computational Statistics & Data Analysis, 2017, vol. 113, issue C, 100-110 Abstract: Continuous endpoints are common in clinical trials. The design and analysis of such trials is often based on models assuming normally distributed data, possibly after an appropriate transformation. When planning a new trial, information on the variance of the endpoint is usually available from historical trials. Although the idea to use historical data for a new trial is not new, literature on how to formally summarize and use these data on variances is scarce. The meta-analytic-predictive (MAP) approach consists of a random-effects meta-analysis of the historical variance data and a prediction of the variance in the new clinical trial. Two applications that rely on the MAP approach are considered: first, the selection of the sample size in the new trial, guided by the prediction of the variance; and, second, the inclusion of the predicted variance in a Bayesian analysis of the new trial. A clinical trial in patients with wet age-related macular degeneration illustrates the methodology. Keywords: Bayesian approach; Between-trial heterogeneity; Evidence synthesis; Meta-analysis; Sample size; Standard deviation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:113:y:2017:i:c:p:100-110 DOI: 10.1016/j.csda.2016.08.007 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.