Sample Size Determination Under Selection Bias: Robust Tolerance Limits for Prevalent Cohort Data

TL;DR

This paper extends classical tolerance limit formulas to account for selection bias in cohort data, providing robust sample size calculations for biased sampling schemes in medical studies.

Contribution

It introduces modified formulas for sample size determination that accommodate bias, validated through simulations and real data application.

Findings

Modified formulas perform well under various bias conditions.

Simulation results show improved accuracy over traditional methods.

Application to dementia data demonstrates practical utility.

Abstract

Tolerance limits have received considerable attention in the statistical literature, with applications reaching far beyond their initial role in quality control. The well-known formula of Scheff\'e and Tukey (1944) establishes a simple, distribution-free relation between sample size and population coverage by two given order statistics and a given confidence level. A key requirement in applying this formula is the availability of an unbiased, representative sample from the population of interest. However, as it often happens in biological and medical applications, various logistical constraints may preclude the possibility of obtaining an unbiased sample. We derive extensions of this formula which accommodate a large class of biased sampling schemes including weight bias and censoring. The modified formulae are validated through a simulation study and compared to its unmodified counterpart. We illustrate the use of the modified formulae using the partially observed failure times for individuals with dementia using data collected from the Canadian Study of Health and Aging.