Estimating causal effects from multiple cycle data in studies of in vitro fertilization.

Prospective studies of reproductive outcomes frequently record data at multiple cycles. For example, studies of in vitro fertilization and embryo transfer (IVF-ET) follow women or couples for possibly several IVF cycles and record outcomes such as pregnancy status and embryo implantation. Several time-varying covariates, such as age and diagnostic markers, typically are available as well. When attention is focused on measurement of exposure effects, the use of multiple cycle data poses several complications. If the study is observational, the exposure probability may depend on subject characteristics. Moreover, attrition rates in IVF-ET can be substantial, and the attrition process can be expected to depend heavily on prior outcome. In fact, both success (pregnancy) and failure (lack of embryo implantations) can be prognostic of dropout. In this paper, we illustrate the use of causal modeling for multiple cycle data. Key assumptions are reviewed, and inference based on weighted estimating equations is described in detail. The methods are applied to a study of the effects of hydrosalpinx among women with tubal disease undergoing IVF-ET.