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Chapter 2 - Identifying risk factors for cognitive change in the Women's Health Initiative: a neural networks approach

from Section 1 - Estrogens and cognition: perspectives and opportunities in the wake of the Women's Health Initiative Memory Study

Published online by Cambridge University Press:  06 July 2010

Eef Hogervorst
Affiliation:
Loughborough University
Victor W. Henderson
Affiliation:
Stanford University, California
Robert B. Gibbs
Affiliation:
University of Pittsburgh
Roberta Diaz Brinton
Affiliation:
University of Southern California
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Summary

Editors' introduction

The Women's Health Initiative Memory Study (WHIMS) has been much criticized and it has been suggested that subgroups of women exist for whom hormone therapy (HT) might improve or impair cognitive function. Possible modifying variables could be age, smoking, body mass index, and menopausal symptoms. These were included in artificial neural networks (ANN) analyses, which allow testing of complex non-linear higher order interactions of variables to predict outcomes. Artificial neural networks analyses without hidden units could predict responders and non-responders to treatment as well as logistic regression models that included only main effects, which indicated that higher order interactions were not necessary and did not add to the value of the models. There seemed to be no subgroups (e.g., older women who smoke and have a high body mass) for whom HT has a worse or better effect on cognitive function over time. This study also showed that cross-validation is essential in building robust models with many independent variables and should be applied as a standard technique in complex multivariate analyses.

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Chapter
Information
Hormones, Cognition and Dementia
State of the Art and Emergent Therapeutic Strategies
, pp. 11 - 24
Publisher: Cambridge University Press
Print publication year: 2009

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