Study design for non-recurring, time-to-event outcomes in the presence of error-prone diagnostic tests or self-reports.

Sequentially administered, laboratory-based diagnostic tests or self-reported questionnaires are often used to determine the occurrence of a silent event. In this paper, we consider issues relevant in design of studies aimed at estimating the association of one or more covariates with a non-recurring, time-to-event outcome that is observed using a repeatedly administered, error-prone diagnostic procedure. The problem is motivated by the Women's Health Initiative, in which diabetes incidence among the approximately 160,000 women is obtained from annually collected self-reported data. For settings of imperfect diagnostic tests or self-reports with known sensitivity and specificity, we evaluate the effects of various factors on resulting power and sample size calculations and compare the relative efficiency of different study designs. The methods illustrated in this paper are readily implemented using our freely available R software package icensmis, which is available at the Comprehensive R Archive Network website. An important special case is that when diagnostic procedures are perfect, they result in interval-censored, time-to-event outcomes. The proposed methods are applicable for the design of studies in which a time-to-event outcome is interval censored.