Sample size and performance estimation for biomarker combinations based on pilot studies with small sample sizes

Abstract

High throughput technologies like microarrays, next generation sequencing and mass spectrometry enable the measurement of tens of thousands of biomarker candidates in pilot studies. Biological systems are often too complex to be based on simple single cause-effect associations and from the medical practice point of view, a single biomarker may not possess the desired sensitivity and/or specificity for disease classification and outcome prediction. Therefore, the efforts of researchers currently aims at combining biomarkers. The intention of biomarker pilot studies with small sample sizes is often to explore the possibility of finding good biomarker combinations and not to find and evaluate a final combination of biomarkers with high predictive value. The aim of the pilot study is to answer the question whether it is worthwhile to extend the study to a larger study and to obtain information about the required sample size. In this paper, we propose a method to judge the potential in a small biomarker pilot study without the need to explicitly identifying and confirming a specific subset of biomarkers. In addition, we provide a method for sample size estimation for an extended study when the results of the pilot study look promising. Abbreviations: ROC: receiver operating characteristic curve; AUC: Area Under the ROC curve; HAUCA: high AUC abundance; ER: Estrogen receptor; BCs: Biomarker candidates; w: with; wt: without.