Methodological Approaches to Analyzing IVF Data with Multiple Cycles

Study Question: Which methodological approaches are most appropriate for analyzing IVF data with multiple cycles in the context of a binary outcome?

Summary Answer: Both mixed effect models and generalized estimating equation (GEE) modeling approaches can account for multiple IVF cycles and may reduce bias over first-cycle only approaches, but CIs were narrowest with cluster-weighted generalized estimating equation models (CWGEE).

What Is Known Already: There is a lack of consensus among investigators regarding how to best incorporate data from multiple cycles and whether to present odds or risks in the analysis of IVF data. Failure to account for correlated outcomes within individuals and informative cluster size may lead to invalid CIs and biased estimates.

Study Design, Size, Duration: The Environment and Reproductive Health (EARTH) Study is an ongoing prospective cohort study of subfertile couples conducted at an academic medical center. This cohort was established in 2004 and follows couples seeking treatment for infertility throughout the course of their treatment and pregnancy.

Participants/materials, Setting, Methods: Women aged 18-46 years enrolled in the EARTH Study from 2004 to 2017 who initiated at least one IVF cycle were eligible. Cycle initiation was defined as beginning ovulation induction with the intent to progress through an IVF or ICSI cycle. This analysis included 442 women undergoing 642 cycles who met the study inclusion criteria. We compared the results and interpretations of log-binomial and logistic models restricting to the first cycle, as well as mixed effects models, unweighted GEE models, and CWGEE models including all cycles. This analysis was conducted for two distinct exposures: maternal age at cycle initiation, and maternal preconception urinary concentrations of di(2-ethylhexyl) phthalate (DEHP) metabolites (previously reported to be associated with a decreased probability of live birth).

Main Results And The Role Of Chance: In general, the CIs were widest for mixed effects models and narrowest for CWGEE models. Further, in models evaluating the sum of urinary concentrations of DEHP metabolites (∑DEHP, available for 91% of women), the point estimates were surprisingly different between the first-cycle and multiple-cycle models. We observed significant associations between maternal age and live birth in all models. However, we observed no associations between ∑DEHP and live birth.

Limitations, Reasons For Caution: This analysis was limited to an example dataset in which the true effect of any exposure is unknown. While this allows us to observe model performance in the context of real data, future analyses should be conducted within simulated datasets under various assumptions to further evaluate the appropriateness of each approach. In addition, we did not address differential loss to follow-up in our statistical approaches.

Wider Implications Of The Findings: The use of CWGEE models should be more widely considered in the analysis of IVF data with multiple cycles per woman. The CWGEE approach is computationally simple, addresses non-ignorable (informative) cluster size, and is robust against mis-specification of the underlying covariance structure. Among the methods compared in this analysis, CWGEE models generally yielded the narrowest CIs, possibly indicating the most precise estimates. We also stress the importance of estimating risks rather than odds in the analysis of IVF data.

Study Funding/competing Interest(s): The project was funded by Grants (R01ES022955, R01ES009718, and P30ES000002) from the National Institutes of Health. None of the authors has any conflicts of interest to declare.