To investigate the ability of the propensity score (PS) to reduce confounding bias in the presence of nondifferential misclassification of treatment, using simulations.
Using an example from the pregnancy medication safety literature, we carried out simulations to quantify the effect of nondifferential misclassification of treatment under varying scenarios of sensitivity and specificity, exposure prevalence (10%, 50%), outcome type (continuous and binary), true outcome (null and increased risk), confounding direction, and different PS applications (matching, stratification, weighting, regression), and obtained measures of bias and 95% confidence interval coverage.
All methods were subject to substantial bias toward the null due to nondifferential exposure misclassification (range: 0%–47% for 50% exposure prevalence and 0%–80% for 10% exposure prevalence), particularly if specificity was low (<97%). PS stratification produced the least biased effect estimates. We observed that the impact of sensitivity and specificity on the bias and coverage for each adjustment method is strongly related to prevalence of exposure: as exposure prevalence decreases and/or outcomes are continuous rather than categorical, the effect of misclassification is magnified, producing larger biases and loss of coverage of 95% confidence intervals. PS matching resulted in unpredictably biased effect estimates.
The results of this study underline the importance of assessing exposure misclassification in observational studies in the context of PS methods. Although PS methods reduce confounding bias, bias owing to nondifferential misclassification is of potentially greater concern.
Selected portions of this work were presented as a poster at the 2016 annual meeting of the Society for Epidemiologic Research and as an oral presentation at the annual meeting of the International Society for Pharmacoepidemiology.
M.E.W. and S.C. are supported by internal institutional funds. H.M.E.N is funded by the ERC StG DrugsInPregnancy (639377). K.L.L. receives salary support from investigator initiated grants from NCI (R21CA198172), NCATS (TL1TR001454), NIA (R21AG046839), NINR (R56NR015498), NIOSH (R21OH010769), NIGMS (R25GM113686), and Merck.
Reprints: Mollie E. Wood, MPH, PhD, PB 1068 Blindern, Oslo 0316, Norway. E-mail: firstname.lastname@example.org.
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