Sample size recalculation in sequential diagnostic trials

• Published in: Biostatistics (Oxford, England) Vol. 11; no. 1; pp. 151 - 163
• Main Authors: Tang, Liansheng Larry, Liu, Aiyi
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2010; Oxford Publishing Limited (England)
• Subjects: Accuracy; Algorithms; Analysis of Variance; Area Under Curve; Biomarkers - blood; Comparative studies; Computer Simulation; Controlled Clinical Trials as Topic - methods; Diagnostic Techniques and Procedures; Epidemiologic Research Design; Humans; Medical diagnosis; Models, Statistical; Neoplasms - blood; Neoplasms - diagnosis; ROC Curve; Sample Size; Sensitivity and Specificity; Simulation; Statistics, Nonparametric; Trials; Sensitivity; Specificity; Error spending function; Diagnostic accuracy; ROC
• ISSN: 1465-4644; 1468-4357; 1468-4357
• DOI: 10.1093/biostatistics/kxp044

Before a comparative diagnostic trial is carried out, maximum sample sizes for the diseased group and the nondiseased group need to be obtained to achieve a nominal power to detect a meaningful difference in diagnostic accuracy. Sample size calculation depends on the variance of the statistic of interest, which is the difference between receiver operating characteristic summary measures of 2 medical diagnostic tests. To obtain an appropriate value for the variance, one often has to assume an arbitrary parametric model and the associated parameter values for the 2 groups of subjects under 2 tests to be compared. It becomes more tedious to do so when the same subject undergoes 2 different tests because the correlation is then involved in modeling the test outcomes. The calculated variance based on incorrectly specified parametric models may be smaller than the true one, which will subsequently result in smaller maximum sample sizes, leaving the study underpowered. In this paper, we develop a nonparametric adaptive method for comparative diagnostic trials to update the sample sizes using interim data, while allowing early stopping during interim analyses. We show that the proposed method maintains the nominal power and type I error rate through theoretical proofs and simulation studies.