Statistics in Medicine

Bayesian analysis of risk factors for anovulation

Journal Article

Abstract

Two algorithms for assessing ovulatory status using daily urinary levels of oestrogen and progesterone metabolites have been applied to non‐clinic‐based, free‐living populations of women. These relatively new methods for assessing ovarian function have been used to assess the potential adverse effects of occupational and environmental exposures, such as smoking, on the reproductive health of women. One algorithm has been validated against serum hormone measurements and gives good sensitivity and specificity for anovulation. However, a gold standard is generally not available in epidemiologic field studies in which these daily urine samples are collected. In this paper, we used Bayesian methods to estimate: (i) the probability of occurrence of anovulation, (ii) the sensitivity and specificity of the two algorithms, and (iii) the association between anovulation and smoking and other risk factors in the absence of a perfect test. We evaluated the two published algorithms for assessing ovulatory status, based on their cross‐classified results applied to one randomly selected cycle from each woman in a sample of 338 employed women. We first assumed that the algorithms were independent, conditional on ovulatory status. Then, we used a dependence model to allow for correlation between the results of the two algorithms. We implemented a Bayesian logistic regression analysis that allowed the outcome measurement to be partially imperfect. We incorporated the posterior distributions for algorithm accuracy obtained from the dependence model as prior distributions for this logistic regression model. Then, we compared the results with those obtained from a standard multiple logistic approach using the algorithm determination of ovulatory status as if it were perfect. Our results indicated that increasing physical activity was associated with a significantly increased risk of anovulation; and smokers had a potentially, but not statistically significant, increased occurrence of anovulation. Copyright © 2004 John Wiley & Sons, Ltd.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.