An internal pilot design for prospective cancer screening trials with unknown disease prevalence. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Brinton, John; Ringham, Brandy; Glueck, Deborah
• Is Part Of: Trials. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-12
• Subjects: Cancer screening -- Internal pilot area under the curve -- Type I error -- Power -- Receiver operating characteristic analysis; Group-randomized trials -- Periodicals; Randomized Controlled Trials -- Periodicals; 615.0727
• DOI: 10.1186/s13063-015-0951-3
• Languages: English
• ISSNs: 1745-6215

Abstract

Abstract Background For studies that compare the diagnostic accuracy of two screening tests, the sample size depends on the prevalence of disease in the study population, and on the variance of the outcome. Both parameters may be unknown during the design stage, which makes finding an accurate sample size difficult. Methods To solve this problem, we propose adapting an internal pilot design. In this adapted design, researchers will accrue some percentage of the planned sample size, then estimate both the disease prevalence and the variances of the screening tests. The updated estimates of the disease prevalence and variance are used to conduct a more accurate power and sample size calculation. Results We demonstrate that in large samples, the adapted internal pilot design produces no Type I inflation. For small samples (N less than 50), we introduce a novel adjustment of the critical value to control the Type I error rate. We apply the method to two proposed prospective cancer screening studies: 1) a small oral cancer screening study in individuals with Fanconi anemia and 2) a large oral cancer screening trial. Conclusion Conducting an internal pilot study without adjusting the critical value can cause Type I error rate inflation in small samples, but not in large samples. An internal pilot approach usually achieves goal power and, for most studies with sample size greater than 50, requires no Type I error correction. Further, we have provided a flexible and accurate approach to bound Type I error below a goal level for studies with small sample size.