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Analyzing Multiply Matched Cohort Studies with Two Different Comparison Groups: Application to Pregnancy Rates among HIV+ Women
Author(s) -
Li Yan,
Zelterman Daniel,
Forsyth Brian W. C.
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/1541-0420.00073
Subject(s) - odds , odds ratio , statistics , confidence interval , pregnancy , logistic regression , mathematics , inference , medicine , cohort , homogeneous , demography , obstetrics , computer science , combinatorics , biology , artificial intelligence , genetics , sociology
Summary . 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.