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Simulation study of misclassification bias in association studies employing partial‐mouth protocols
Author(s) -
Heaton Brenda,
Garcia Raul I.,
Dietrich Thomas
Publication year - 2018
Publication title -
journal of clinical periodontology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.456
H-Index - 151
eISSN - 1600-051X
pISSN - 0303-6979
DOI - 10.1111/jcpe.12979
Subject(s) - periodontitis , logistic regression , medicine , statistics , information bias , dentistry , selection bias , mathematics
Abstract Aim To simulate the exposure misclassification bias potential in studies of perio‐systemic disease associations due to the use of partial‐mouth recording ( PMR ) protocols. Methods Using data from 640 participants in the Dental Longitudinal Study, we evaluated distributions of clinical periodontitis parameters to simulate hypothetical outcome probabilities using bootstrap sampling. Logistic regression models were fit using the hypothetical outcome as the dependent variable. Models were run for exposure classifications based on full‐mouth recording (FMR) and PMR protocols over 10,000 repetitions. Results The impact of periodontitis exposure misclassification was dependent on periodontitis severity. Per cent relative bias for simulated OR s of size 1.5, 2 and 4 ranged from 0% to 30% for the effect of severe periodontitis. The magnitude and direction of the bias was dependent on the underlying distribution of the clinical parameters used in the simulation and the size of the association being estimated. Simulated effects of moderate periodontitis were consistently biased towards the null. Conclusion Exposure misclassification bias occurring through the use of PMR protocols may be dependent on the sensitivity of the classification system applied. Using the CDC ‐ AAP case definition, bias in the estimated effects of severe disease was small, on average. Whereas effects of moderate disease were underestimated to a larger degree.