%0 Journal Article
%@ 1471-2288
%A Weber, Dorothea
%A Uhlmann, Lorenz
%A Schönenberger, Silvia
%A Kieser, Meinhard
%C London ; Berlin, Heidelberg
%D 2019
%F heidok:26829
%I BioMed Central ; Springer
%J BMC Medical Research Methodology
%K Adaptive design, Clinical Trials, Sample size recalculation, Matched cohort, Prospective matching
%N 150
%P 1-11
%T Adaptive propensity score procedure improves matching in prospective observational trials
%U https://archiv.ub.uni-heidelberg.de/volltextserver/26829/
%V 19
%X Background: Randomized controlled trials are the gold-standard for clinical trials. However, randomization is not always feasible. In this article we propose a prospective and adaptive matched case-control trial design assuming that a control group already exists.    Methods: We propose and discuss an interim analysis step to estimate the matching rate using a resampling step followed by a sample size recalculation. The sample size recalculation is based on the observed mean resampling matching rate. We applied our approach in a simulation study and to a real data set to evaluate the characteristics of the proposed design and to compare the results to a naive approach.    Results: The proposed design achieves at least 10% higher matching rate than the naive approach at final analysis, thus providing a better estimation of the true matching rate. A good choice for the interim analysis seems to be a fraction of around 1/2 to 2/3 of the control patients.    Conclusion: The proposed resampling step in a prospective matched case-control trial design leads to an improved estimate of the final matching rate and, thus, to a gain in power of the approach due to sensible sample size recalculation.