TY - JOUR
T1 - A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications
AU - Penning de Vries, Bas B.L.
AU - van Smeden, Maarten
AU - Groenwold, Rolf H.H.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: RHHG was funded by the Netherlands Organization for Scientific Research (NWO-Vidi project 917.16.430). The views expressed in this article are those of the authors and not necessarily any funding body.
Publisher Copyright:
© The Author(s) 2020.
PY - 2021/2
Y1 - 2021/2
N2 - 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).
AB - 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).
KW - Causal inference
KW - confounding
KW - inverse probability weighting
KW - joint exposure and outcome misclassification
KW - propensity scores
KW - validation data
UR - http://www.scopus.com/inward/record.url?scp=85091804783&partnerID=8YFLogxK
U2 - 10.1177/0962280220960172
DO - 10.1177/0962280220960172
M3 - Article
C2 - 32998668
AN - SCOPUS:85091804783
SN - 0962-2802
VL - 30
SP - 473
EP - 487
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 2
ER -