The purpose of the current review is to describe the common pitfalls in design and statistical analysis of reproductive medicine studies. It serves to guide both authors and reviewers toward reducing the incidence of spurious statistical results and erroneous conclusions.
The large amount of data gathered in IVF cycles leads to problems with multiplicity, multicollinearity, and over fitting of regression models. Furthermore, the use of the word ‘trend’ to describe nonsignificant results has increased in recent years. Finally, methods to accurately account for female age in infertility research models are becoming more common and necessary.
The pitfalls of study design and analysis reviewed provide a framework for authors and reviewers to approach clinical research in the field of reproductive medicine. By providing a more rigorous approach to study design and analysis, the literature in reproductive medicine will have more reliable conclusions that can stand the test of time.
aReproductive Medicine Associates of Florida, IVI-RMA Global
bDepartment of Obstetrics and Gynecology, College of Medicine, University of Central Florida, Florida
cProgram in Reproductive Endocrinology and Infertility, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Maryland, USA
Correspondence to George Patounakis, MD, PhD, Reproductive Medicine Associates of Florida, IVI-RMA Global, 400 Colonial Center Parkway, Suite 150, Lake Mary, FL 32746, USA. Tel: +1 407 804 9670; e-mail: email@example.com