Decision trees for identifying predictors of treatment effectiveness in clinical trials and its application to ovulation in a study of women with polycystic ovary syndrome | Zendy

Heping Zhang | Zendy; Richard S. Legro | Zendy; Jian Zhang | Zendy; L. Zhang | Zendy; Xiang Chen | Zendy; Hao Huang | Zendy; Peter R. Casson | Zendy; William D. Schlaff | Zendy; Michael P. Diamond | Zendy; Stephen A. Krawetz | Zendy; Christos Coutifaris | Zendy; Robert G. Brzyski | Zendy; Gregory M. Christman | Zendy; Nanette Santoro | Zendy; Esther Eisenberg | Zendy

Open Access

Decision trees for identifying predictors of treatment effectiveness in clinical trials and its application to ovulation in a study of women with polycystic ovary syndrome

Author(s) -

Heping Zhang,

Richard S. Legro,

Jian Zhang,

L. Zhang,

Xiang Chen,

Hao Huang,

Peter R. Casson,

William D. Schlaff,

Michael P. Diamond,

Stephen A. Krawetz,

Christos Coutifaris,

Robert G. Brzyski,

Gregory M. Christman,

Nanette Santoro,

Esther Eisenberg

Publication year - 2010

Publication title -

human reproduction

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.446

H-Index - 226

eISSN - 1460-2350

pISSN - 0268-1161

DOI - 10.1093/humrep/deq210

Subject(s) - polycystic ovary , randomized controlled trial , ovulation , clinical trial , medicine , gynecology , insulin resistance , hormone , insulin

Double-blind, randomized clinical trials are the preferred approach to demonstrating the effectiveness of one treatment against another. The comparison is, however, made on the average group effects. While patients and clinicians have always struggled to understand why patients respond differently to the same treatment, and while much hope has been held for the nascent field of predictive biomarkers (e.g. genetic markers), there is still much utility in exploring whether it is possible to estimate treatment efficacy based on demographic and baseline variables.

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