Efficient Logistic Regression Designs under an Imperfect Population Identifier

作者：Albert Paul S^*; Liu Aiyi; Nansel Tonja

来源：Biometrics, 2014, 70(1): 175-184.

DOI：10.1111/biom.12106

• Case-control designs
• Diagnostic accuracy
• Epidemiologic designs
• Misclassification
• Measurement error

Motivated by actual study designs, this article considers efficient logistic regression designs where the population is identified with a binary test that is subject to diagnostic error. We consider the case where the imperfect test is obtained on all participants, while the gold standard test is measured on a small chosen subsample. Under maximum-likelihood estimation, we evaluate the optimal design in terms of sample selection as well as verification. We show that there may be substantial efficiency gains by choosing a small percentage of individuals who test negative on the imperfect test for inclusion in the sample (e.g., verifying 90% test-positive cases). We also show that a two-stage design may be a good practical alternative to a fixed design in some situations. Under optimal and nearly optimal designs, we compare maximum-likelihood and semi-parametric efficient estimators under correct and misspecified models with simulations. The methodology is illustrated with an analysis from a diabetes behavioral intervention trial.

• 出版日期2014-3