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Methodological approaches to analyzing IVF data with multiple cycles.

Jennifer Yland1, Carmen Messerlian2, Lidia Mínguez-Alarcón2

  • 1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Human Reproduction (Oxford, England)
|December 22, 2018
PubMed
Summary
This summary is machine-generated.

Cluster-weighted generalized estimating equation (CWGEE) models offer the most precise estimates for analyzing multiple in vitro fertilization (IVF) cycles, providing narrower confidence intervals (CIs) than other methods.

Keywords:
ARTIVFclustered datainfertilityinformative cluster sizeresearch methods

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Area of Science:

  • Reproductive epidemiology
  • Biostatistics
  • Infertility research

Background:

  • Lack of consensus exists on analyzing in vitro fertilization (IVF) data with multiple cycles.
  • Failure to account for correlated outcomes and informative cluster size can lead to biased estimates and invalid confidence intervals (CIs).

Purpose of the Study:

  • To compare methodological approaches for analyzing IVF data with multiple cycles and a binary outcome.
  • To evaluate the performance of different statistical models in the context of reproductive health studies.

Main Methods:

  • The Environment and Reproductive Health (EARTH) Study cohort (2004-2017) included 442 women undergoing 642 IVF cycles.
  • Compared log-binomial, logistic (first-cycle only), mixed-effects models, unweighted generalized estimating equation (GEE), and cluster-weighted GEE (CWGEE) models.
  • Exposures included maternal age and urinary di(2-ethylhexyl) phthalate (DEHP) metabolites.

Main Results:

  • Cluster-weighted generalized estimating equation (CWGEE) models yielded the narrowest confidence intervals (CIs), suggesting more precise estimates.
  • Maternal age was significantly associated with live birth across all models.
  • No significant association was observed between DEHP metabolites and live birth.

Conclusions:

  • Cluster-weighted generalized estimating equation (CWGEE) models are recommended for analyzing multiple IVF cycle data due to their ability to handle informative cluster size and provide precise estimates.
  • Estimating risks rather than odds is crucial in IVF data analysis.