Abstract
Infertility is a global public health issue and various treatments are available. In vitro fertilization (IVF) is an increasingly common treatment method, but accurately assessing the success of IVF programs has proven challenging since they consist of multiple cycles. We present a double robust semiparametric method that incorporates machine learning to estimate the probability of success (i.e., delivery resulting from embryo transfer) of a program of at most four IVF cycles in the French Devenir Apr`es Interruption de la FIV (DAIFI) study and several simulation studies, controlling for time-dependent confounders. We find that the probability of success in the DAIFI study is 50% (95% confidence interval [0.48, 0.53]), therefore approximately half of future participants in a program of at most four IVF cycles can expect a delivery resulting from embryo transfer.
Disciplines
Biostatistics
Suggested Citation
Chambaz, Antoine; Rose, Sherri; Bouyer, Jean; and van der Laan, Mark J., "Targeted Learning of The Probability of Success of An In Vitro Fertilization Program Controlling for Time-dependent Confounders" (October 2012). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 299.
https://biostats.bepress.com/ucbbiostat/paper299