Abstract: In vitro fertilization (IVF) is an increasingly common method of assisted reproductive technology. IVF studies provide an ideal opportunity to identify and assess clinical and demographic factors along with environmental exposures that may impact IVF success rates. The main challenge in analysis of data resulting from IVF studies is the presence of multiple hierarchically-ordered outcomes per individual, resulting from both multiple opportunities for pregnancy loss within a single IVF cycle in addition to multiple IVF cycles. To date, most evaluations of IVF studies do not make use of full data due to its complex structure. In this paper, we develop statistical methodology for analysis of IVF data with multiple cycles and possibly multiple failure types observed for each individual. We develop a general methodology based on a generalized linear modeling formulation that allows implementation of various types of models including shared frailty models, failure specific frailty models, and transitional models. We apply our methodology to an analysis of the data from IVF study conducted by the Brigham and Women's Hospital, Massachusetts. We also summarize the performance of our proposed methods based on a simulation study.