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Analysis of Multiple-cycle Data From Couples Undergoing In Vitro Fertilization: Methodologic Issues and Statistical Approaches

Missmer, Stacey A.a,b,c; Pearson, Kimberly R.d; Ryan, Louise M.d; Meeker, John D.e; Cramer, Daniel W.a,b; Hauser, Russf,g

doi: 10.1097/EDE.0b013e31821b5351
Reproductive: Original Article

The number of in vitro fertilization (IVF) cycles in the United States increased from fewer than 46,000 in 1995 to more than 120,000 in 2005. IVF and other assisted reproductive technology (ART) data are routinely collected and used to identify outcome predictors. However, researchers do not always make full use of the data due to their complexity. Design approaches have included restriction to first-cycle attempts only, which reduces power and identifies effects only of those factors associated with initial success. Many statistical techniques have been used or proposed for analysis of IVF data, ranging from simple t tests to sophisticated models designed specifically for IVF. We applied several of these methods to data from a prospective cohort of 2687 couples undergoing ART from 1994 through 2003. Results across methods are compared and the appropriateness of the various methods is discussed with the intent to illustrate methodologic validity. We observed a remarkable similarity of coefficient estimates across models. However, each method for dealing with multiple cycle data relies on assumptions that may or may not be expected to hold in a given IVF study. The robustness and reported magnitude of effect for individual predictors of IVF success may be inflated or attenuated due to violation of statistical assumptions, and should always be critically interpreted. Given that risk factors associated with IVF success may also advance our understanding of the physiologic processes underlying conception, implantation, and gestation, the application of valid methods to these complex data is critical.


From the aDepartment of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; bDepartment of Epidemiology, Harvard School of Public Health, Boston, MA; cChanning Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; dDepartment of Biostatistics, Harvard School of Public Health, Boston, MA; eDepartment of Environmental Health Sciences, University of Michigan, Ann Arbor, MI; fDepartment of Environmental Health, Harvard School of Public Health, Boston, MA; gVincent Memorial Obstetrics and Gynecology Service, Andrology Laboratory and In Vitro Fertilization Unit, Massachusetts General Hospital, Boston, MA.

Submitted 9 May 2008; accepted 10 January 2011.

Supported by the National Institutes of Health (grants HD32153 and ES13967).

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Correspondence: Stacey Missmer, Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital; 75 Francis St, Boston, MA 02115. E-mail:

© 2011 Lippincott Williams & Wilkins, Inc.