To Be or Not to Be? That is Conception! Managing In Vitro Fertilization Programs

Edward H. Kaplan, Avner Hershlag, Alan H. DeCherney and Gady Lavy
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Edward H. Kaplan: Yale School of Organization and Management, Box 1A, New Haven, Connecticut 06520
Avner Hershlag: Department of Internal Medicine, Yale School of Medicine, 333 Cedar Street, New Haven, Connecticut 06510 and Department of Operations Research, Yale University. Box 162, New Haven, Connecticut 06520
Alan H. DeCherney: Department of Obstetrics and Gynecology, North Shore University Hospital-Cornell University Medical College, 300 Community Drive, Manhasset, New York 11030
Gady Lavy: Department of Obstetrics and Gynecology, Yale School of Medicine, 333 Cedar Street, New Haven, Connecticut 06510

Management Science, 1992, vol. 38, issue 9, 1217-1229

Abstract: The performance of in vitro fertilization-embryo transfer (IVF-ET) programs is summarized typically as the average probability of achieving pregnancy per cycle. Variation in conception probabilities across women reduces the usefulness of such an aggregate measure. More relevant is the conditional probability of achieving pregnancy on a given cycle following a number of failed IVF-ET attempts. We construct a model that accurately describes 1,257 treatment cycles performed at Yale over 571 different women. The model assumes a split population, where some women can never conceive via IVF-ET, while the remaining women have identical and constant per cycle probabilities of conception. This model produces estimates that are highly consistent with the data, and suggests that continuing treatment beyond some threshold number of cycles is not efficacious. Recognizing this, we determine cutoffs beyond which treatment should not continue for IVF-ET programs with fixed capacities. We also consider cutoff policies where program participants may belong to one of several different split populations, detailing the case of two groups. Finally, we show how one may reduce the average time in treatment (including waiting time) considerably with minimal impact on the probability of achieving pregnancy.

Keywords: in vitro fertilization; infertility; probabilistic modeling; queueing theory (search for similar items in EconPapers)
Date: 1992
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