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Pearson, Kimberly Hope

Among 2245 women, those who experienced a chemical pregnancy that failed to progress to a clinically recognized pregnancy or a spontaneous abortion on their first IVF cycle were more likely to discontinue IVF treatment than those whose first cycle ended prior to embryo transfer or who did not have a positive pregnancy test following transfer. However, among women who did continue to a second IVF cycle, those who had at least a chemical pregnancy on the first cycle were more likely to have a live birth on the second attempt than those women who had failed prior to conception in the first cycle (34% success rate compared to 21%, respectively).

The number of in vitro fertilization (IVF) cycles in the U.S. 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 its 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 utilized or proposed for analysis of IVF data, ranging from simple t-tests to sophisticated models designed specifically for IVF. We apply 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.