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A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

Statistical Methods in Medical Research, vol. 30, pp. 473–487

Abstract

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425–436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor).

Authors 3

  1. Leiden University Medical Center

    Affiliation as printed

    Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

    LEIDEN UNIVERSITY (MEDICAL CENTER)

  2. Leiden University Medical Center

    Affiliation as printed

    Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

    Department of Clinical Epidemiology, Leiden University Medical Center , Leiden , the Netherlands

  3. Leiden University Medical Center

    Affiliation as printed

    Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

    Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands

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References 32