Journal Article

Studying Time to Pregnancy by Use of a Retrospective Design

Michael Joffe, Jane Key, Nicky Best, Niels Keiding, Thomas Scheike and Tina Kold Jensen

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 162, issue 2, pages 115-124
Published in print July 2005 | ISSN: 0002-9262
Published online July 2005 | e-ISSN: 1476-6256 | DOI:
Studying Time to Pregnancy by Use of a Retrospective Design

Show Summary Details


Biologic fertility can be measured using time to pregnancy (TTP). Retrospective designs, although lacking detailed timed information about behavior and exposure, are useful since they have a well-defined target population, often have good response rates, and are simpler and less expensive to conduct than prospective studies. This paper reviews retrospective TTP studies from a methodological viewpoint and shows how methodological problems can be avoided or minimized by appropriate study design, conduct, and analysis. Sensitivity analyses using data from four European retrospective TTP studies are presented to explore the issues. Although the identified biases tend to have small impacts, the effects are not systematic across studies, and sensitivity analyses are recommended routinely. Planning bias can be checked by comparing propensity to report contraceptive failures in different exposure groups. Medical intervention bias can be avoided by censoring and inclusion of unsuccessful pregnancy attempts. Truncation bias can be a serious problem if unrecognized, but it is avoidable with appropriate study design and/or analysis. Behavior change bias can be minimized by assessing the covariates at the beginning of unprotected intercourse. More complete inference is possible if the study design covers the whole population, not just those who achieve a pregnancy.

Keywords: data collection; fertility; infertility; questionnaires; reproduction; TTP, time to pregnancy

Journal Article.  6544 words.  Illustrated.

Subjects: Public Health and Epidemiology

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