A GEE Approach for Estimating Correlation Coefficients Involving Left-censored Variables

by Jingli Song, Huiman X. Barnhart and Robert H. Lyles

Journal of Data Science, v.2, no.3, 245-257

Abstract

HIV (Human Immunodeficiency Virus) researchers are often concerned with the correlation between HIV viral load measurements and CD4+ lymphocyte counts. Due to the lower limits of detection (LOD) of the available assays, HIV viral load measurements are subject to left-censoring. Motivated by these considerations, the maximum likelihood (ML) method under normality assumptions was recently proposed for estimating the correlation between two continuous variables that are subject to left-censoring. In this paper, we propose a generalized estimating equations (GEE) approach as an alternative to estimate such a correlation coefficient. We investigate the robustness to the normality assumption of the ML and the GEE approaches via simulations. An actual HIV data example is used for illustration.

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