Design Considerations for Case Series Models with Exposure Onset Measurement Error

• Activity Number: 42
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Sandra Mohammed*+ and Lorien Dalrymple and Damla Senturk and Danh Nguyen
• Companies: University of California at Davis and University of California at Davis School of Medicine and University of California at Los Angeles and University of California at Davis
• Keywords: case series models ; exposure timing measurement error ; longitudinal observational database ; non-homogeneous Poisson process ; sample size

Abstract

The case series model allows for estimation of the relative incidence of events, such as cardiovascular events, within a pre-specified time window after an exposure, such as an infection. The method requires only cases (individuals with events) and controls for all fixed/time-invariant confounders. The measurement error case series model extends the original case series model to handle imperfect data, where the timing of an infection (exposure) is not known precisely. In this work, we propose a method for power/sample size determination for the measurement error case series model. Extensive simulation studies are used to assess the accuracy of the proposed sample size formulas. We also examine the magnitude of the relative loss of power due to exposure onset measurement error, compared to the ideal situation where the time of exposure is measured precisely. To facilitate the design of case series studies, we provide publicly available web-based tools for determining power/sample size for both the measurement error case series model as well as the standard case series model.