JDR clinical and translational research · 2018
Differential Misclassification of Disease under Partial-Mouth Sampling.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Preisser J S, Sanders A E, Lyles R H
Аннотация
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.
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.