Optimal design and efficiency of two-phase case–control studies with error-prone and error-free exposure measures

R. McNamee

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This paper addresses optimal design and efficiency of two-phase (2P) case-control studies in which the first phase uses an error-prone exposure measure, Z, while the second phase measures true, dichotomous exposure, X, in a subset of subjects. Optimal design of a separate second phase, to be added to a preexisting study, is also investigated. Differential misclassification is assumed throughout. Results are also applicable to 2P cohort studies with error-prone and error-free measures of disease status but error-free exposure measures. While software based on the mean score method of Reilly and Pepe (1995, Biometrika 82, 299-314) can find optimal designs given pilot data, the lack of simple formulae makes it difficult to generalize about efficiency compared to one-phase (1P) studies based on X alone. Here, formulae for the optimal ratios of cases to controls and first- to second-phase sizes, and the optimal second-phase stratified sampling fractions, given a fixed budget, are given. The maximum efficiency of 2P designs compared to a 1P design is deduced and is shown to be bounded from above by a function of the sensitivities and specificities of Z. The efficiency of 'balanced' separate second-phase designs (Breslow and Cain, 1988, Biometrika 75, 11-20) - in which equal numbers of subjects are chosen from each first-phase strata - compared to optimal design is deduced, enabling situations where balanced designs are nearly optimal to be identified.
    Original languageEnglish
    Pages (from-to)590-603
    Number of pages13
    JournalBiostatistics
    Volume6
    Issue number4
    DOIs
    Publication statusPublished - Oct 2005

    Keywords

    • Case-control studies
    • Differential misclassification
    • Efficiency
    • Exposure validation studies
    • Measurement error
    • Two-phase studies
    • Two-stage studies

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