Maximum-likelihood and closed-form estimators of epidemiologic measures under misclassification
- 作者：S ; er Greenl
- 关键词：Bias ; Epidemiologic methods ; Measurement error ; Misclassification ; Odds ratio ; Relative risk ; Validation
- 刊名：Journal of Statistical Planning and Inference
- 出版年：2008
- 期刊代码：36_03783758
- 类别：mt
- 出版时间：1 February 2008
- 卷：138
- 期：2
- 页码：528-538
- 文件大小：208 K
摘要
There is a large literature on estimation under misclassification. The present paper reviews epidemiologic inference under misclassification in the multiway contingency-table setting, and addresses a few controversial issues. In the 1990s, claims of inefficiency of early closed-form estimators of odds ratios under misclassification arose from misapplication of the estimators to studies with internal validation. In reality, these estimators are maximum likelihood (ML) and hence efficient under the external-validation assumptions used for their derivation. For the internal-validation case, a new closed-form estimator is derived that incorporates the nondifferentiality constraint into the predictive-value (“direct” or “inverse-matrix”) estimator. Results are presented in a general framework that applies to misclassification in models for multiway tables, and that allows the target parameter to be any measure of association or effect.