Design considerations for case series models with exposure onset measurement error

Sandra M. Mohammed, Lorien Dalrymple, Damla Şentürk, Danh V. Nguyen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 with 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.

Original languageEnglish (US)
Pages (from-to)772-786
Number of pages15
JournalStatistics in Medicine
Volume32
Issue number5
DOIs
StatePublished - Feb 28 2013

Keywords

  • Case series models
  • Exposure timing measurement error
  • Longitudinal observational database
  • Non-homogeneous Poisson process
  • Sample size

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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