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Analyzing multiply matched cohort studies with two different comparison groups: application to pregnancy rates among
Yan Li1, Daniel Zelterman, Brian W C Forsyth
1Division of Biostatistics, Yale University, New Haven, Connecticut 06520, USA. yl97@omega.med.yale.edu
Abstract:
We develop a new statistical method to analyze multiply matched cohort studies with two different comparison groups. We employ a linear-logistic model to describe the underlying log-odds ratios and use a conditional likelihood approach to conduct inference. Under the assumption of homogeneous log-odds ratios, we provide methods to construct both asymptotic and exact confidence regions of the two log-odds ratios in a simple case. We propose a score test to evaluate the assumption of homogeneous log-odds ratios across strata. While our methods are general, we develop them around a specific application, namely, the study of pregnancy rates in HIV-infected women. Our analyses suggest that HIV infection is associated with a decrease in pregnancy rates and that this decrease in fertility becomes significant after accounting for illicit drug use.
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