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 ORs 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.
Keywords: bias; misclassification; periodontal disease; periodontitis; simulation.
© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.