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Hum. Reprod. Advance Access published online on September 30, 2004
Human Reproduction, doi:10.1093/humrep/deh475
© 2004 by European Society of Human Reproduction and Embryology
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Received December 17, 2002
Revised April 16, 2004
Accepted July 23, 2004

Article

The place of the crossover design in infertility trials: a maximum likelihood approach

Joseph McDonnell 1*, Angelique J. Goverde 2, and Jan P.W. Vermeiden 2

1 Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands; Division of Reproductive Endocrinology and Fertility, Institute for Endocrinology, Reproduction and Metabolism, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands
2 Division of Reproductive Endocrinology and Fertility, Institute for Endocrinology, Reproduction and Metabolism, Vrije Universiteit Medical Centre, Amsterdam, The Netherlands

* To whom correspondence should be addressed. E-mail: j.mcdonnell{at}vumc.nl.


  Abstract

BACKGROUND: For some years, there has been a debate as to the place of the crossover trial in assisted reproduction technology (ART). We aimed to investigate whether crossover and parallel designs result in different estimates of treatment effects. METHODS: We carried out computer simulation of cohorts of patients undergoing either intra-uterine insemination (IUI) or IVF under both parallel and crossover designs, under scenarios involving censoring and carryover effects. Results of the simulation were analysed using a maximum likelihood approach. RESULTS: No relevant difference was found between the designs. The crossover design resulted in slightly more pregnancies than the parallel design. Carryover effects may slightly distort the estimates of treatment effects. Crossover and parallel designs will produce essentially the same statistical estimates of treatment effect and percentage of pregnancies. The crossover design is an acceptable design in infertility research provided the data are analysed correctly.

Keywords: carryover effects; crossover design; infertility; maximum likelihood estimation; parallel design.
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