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Differential Misclassification of Disease under Partial-Mouth Sampling

Aim: The effect of misclassification of a cluster-level dichotomous outcome (disease) due to partial-cluster sampling on its association with a dichotomous exposure is investigated. Methods: Disease (e.g., chronic periodontitis) is deemed to exist in a cluster (e.g., full mouth) when a condition of interest (e.g., pocket depth or clinical attachment loss exceeding an established threshold) is present in number and pattern across observations (e.g., tooth sites) in the cluster according to a specific criterion. When a subset of observations within each cluster is selected (i.e., partial-mouth sampling), specificity of disease is 100% (in the absence of site-level measurement error), whereas sensitivity is imperfect and generally unknown. Using conditional probability arguments, we investigate disease misclassification under partial-cluster sampling and its impact on the estimated disease-exposure association when the exposure is cluster level and measured without error. Results: When the probability of disease varies by exposure status, outcome misclassification at the cluster level is differential under partial-cluster sampling and depends on 1) the partial recording protocol, including the number of observations sampled and the particular sites selected in a cluster; 2) the joint probability structure of the condition within clusters; and 3) the criterion for disease. A numeric example demonstrates that disease-exposure odds ratios under partial-cluster random sampling can be biased in either direction (toward or away from the null) relative to gold-standard odds ratios under full-cluster sampling. Conclusions: In general, misclassification of disease is differential under partial-cluster sampling. In particular, sensitivity and negative predictive values depend on exposure status, which leads to biased inference. Knowledge Transfer Statement: Partial-mouth sampling causes disease misclassification probabilities, including sensitivity, to vary by exposure groups when disease prevalence differs between groups. As a result, disease-exposure associations may be under- or overestimated by standard analysis procedures for periodontal data relative to full-mouth estimates. Procedures that address bias are needed for partial-recording protocols.