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Multiple Imputation for Partial Recording Periodontal Examination Protocols
J S Preisser1, T Shing1, B F Qaqish1
1Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Aim:
Partial-mouth recording protocols often result in underestimation of population prevalence and extent of periodontitis. We posit that multiple imputation of measures such as clinical attachment loss for nonselected tooth sites in partial-mouth samples can reduce bias in periodontitis estimates.
Methods:
Multiple imputation for correlated site-level dichotomous outcomes in a generalized estimating equations framework is used to impute site-level binary indicators for clinical attachment loss exceeding a fixed threshold in partial-mouth samples. Periodontitis case definitions are applied to the imputed "complete" dentitions, enabling estimation of prevalence and other summaries of periodontitis for partial-mouth samples as if for full-mouth examinations. A multiple imputation-bootstrap procedure is described and applied for point and variance estimation of these periodontitis measures. The procedure is evaluated with pseudo-partial-mouth samples based on random site selection protocols of 28 to 84 periodontal sites repeatedly generated from full-mouth periodontal examinations of 3,621 participants in the 2013 to 2014 National Health and Nutrition Examination Survey (NHANES) survey.
Results:
Multiple imputation applied to partial-mouth samples overestimated periodontitis mean extent, defined as the number of sites with clinical attachment loss 3 mm or greater, by 9.5% in random site selection protocols with 84 sites and overestimated prevalence by 5% to 10% in all the evaluated protocols.
Conclusions:
In the 2013 to 2014 NHANES data, multiple imputation of site-level periodontal indicators provides less biased estimates of periodontitis prevalence and extent than has been reported from estimates based on the direct application of full-mouth case definitions to partial-mouth samples. Multiple imputation provides a promising solution to the longstanding, vexing problem of estimation bias in partial-mouth recording, with potential application to a wide array of case definitions, periodontitis measures, and partial recording protocols.
Knowledge Transfer Statement:
Partial-mouth sampling, while a resource-efficient strategy for obtaining oral disease estimates, often results in underestimation of periodontitis metrics. Multiple imputation for nonselected periodontal sites produces pseudo-full-mouth data sets that may be analyzed and combined to produce estimates with small bias.
