ML-Estimation in a Case-Control Study with Measurement Error in the Risk Factor. A Comparison: External Validation versus Repeated Measurements

Schuster, G. (1996): ML-Estimation in a Case-Control Study with Measurement Error in the Risk Factor. A Comparison: External Validation versus Repeated Measurements. Collaborative Research Center 386, Discussion Paper 43

Abstract

First we show briefly the effects of using the ordinary estimator for the logarithm of the odds ratio in a case-control study with binary risk factor when we have misclassification in the risk factor. Then external validation and repeated measurements, which are two broad strategies to correct for misclassification, are introduced. For both of these models the ML-estimates and their asymptotic variances are derived. Under the assumption that both models have the same costs, the asymptotic variances are compared for two cases. We choose first equal subsample sizes and then optimal subsample sizes. Simulation studies have been carried out in order to get an impression of the probability that the estimates are well defined and of how large the sample sizes have to be so that the asymptotic variances are good approximations.

• Item Type: Paper
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English