Inference using conditional logistic regression with missing covariates.
Lipsitz SR. Parzen M. Ewell M.
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. firstname.lastname@example.org
When there are many nuisance parameters in a logistic regression model, a popular method for eliminating these nuisance parameters is conditional logistic regression. Unfortunately, another common problem in a logistic regression analysis is missing covariate data. With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates and then use conditional logistic regression, often called a complete-case analysis. In this article, we derive a modified conditional logistic regression that is appropriate with covariates that are missing at random. Performing a conditional logistic regression with only the complete cases is convenient with existing statistical packages, but it may give bias if missingness is not completely at random.
Comparing correlated areas under the ROC curves of two diagnostic tests in the presence of verification bias.
Department of Medicine, Indiana University School of Medicine, Indianapolis 46202-5119, USA. email@example.com
To assess relative accuracies of two diagnostic tests, we often compare the areas under the receiver operating characteristic (ROC) curves of these two tests in a paired design. Standard methods for analyzing data from a paired design require that every patient tested has the known disease status. In practice, however, some of the patients with test results may not have verified disease status. Any analysis using only verified cases may result in verification bias. In this paper, we propose a verification bias correction procedure for comparing areas under ROC curves under the missing-at-random (MAR) assumption. We also develop an approach for testing the validity of the MAR assumption. Finally, we use the proposed method to compare the relative accuracies of MRI and CT in evaluation of pancreatic cancer.