Analyzing multiply matched cohort studies with two different comparison groups: application to pregnancy rates among HIV+ women.
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.
Division of Biostatistics, Yale University, New Haven, Connecticut 06520, USA. yl97@omega.med.yale.edu
Yan
Y
Li
Daniel
D
Zelterman
Brian W C
BW
Forsyth
D001699:Biometry; D015331:Cohort Studies; D003237:Connecticut / Q000453:epidemiology; D005260:Female; D015658:HIV Infections / Q000150:complications* / Q000453:epidemiology*; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D011247:Pregnancy; D011251:Pregnancy Complications, Infectious / Q000453:epidemiology*; D019966:Substance-Related Disorders / Q000150:complications