Natural Sciences
Life Sciences
Scientific Computing
Life Science

Geraldine Rauch, Institut für Biometrie und Klinische Epidemiologie, Charité, Universitätsmedizin Berlin

Marsilius Arkaden, Turm Süd, Im Neuenheimer Feld 130.2

Heidelberger Kolloquium Medizinische Biometrie, Informatik und Epidemiologie

It is intuitive that the correct choice of the sample size is of major importance for an ethical justification of a trial and a responsible spending of resources. In an underpowered trial, the research hypothesis is unlikely to be proven, resources are wasted and patients are unnecessarily exposed to the study-specific risks. If the sample size is too large, the market approval is prolonged and later recruited patient in the control arm are exposed to a treatment already known to be less effective. The parameter assumptions required for sample size calculation should be based on previously published results from the literature and on aspects of clinical relevance. In clinical practice, however, historical studies for the research topic of interest are often not directly comparable to the current situation under investigation or simply do not exist. Moreover, the results of previous studies often show a high variability or are even contradictory. Calculating the ‘correct’ sample size is thus a difficult task. On the other side, the consequences of a ‘wrong’ sample size are severe. A variety of sample size recalculation strategies have been proposed. Most frequently, these rules are based on conditional power arguments (e.g. [1], [2],[3]). This approach assumes implicitly that the true treatment effect is equal to the effect observed at the interim analysis. The conditional power approach is often criticized for this unrealistic assumption as the available information at the interim stage is usually limited and thus the treatment effect estimate shows a rather high variability resulting in a highly variable sample size. We present a new sample size recalculation strategy based on resampling which uses the interim effect as the expectation of a distribution rather than assuming that the interim effect is the true one. [1] Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics 1999; 1286-1290. [2] Mehta CR, Pocock SJ. Adaptive increase in sample size when interim results are promising: A practical guide with eamples. Stat. Med. 2011;30:3267-3284. [3] Jennison C, Turnbull BW. Adaptive sample size modification in clinical trials: start small then ask for more? Stat. Med. 2015, 34: 3793–3810

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