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

Heping Zhang, Richard S. Legro, Jeffrey Zhang, Leon Zhang, Xiang Chen, Hao Huang, Peter R. Casson, William D. Schlaff, Michael Peter Diamond, Stephen A. Krawetz, Christos Coutifaris, Robert G. Brzyski, Gregory M. Christman, Nanette Santoro, Esther Eisenberg

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

BACKGROUND: 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. METHODS: The pregnancy in polycystic ovary syndrome (PPCOS) study was a prospective, multi-center, randomized clinical trial comparing three ovulation induction regimens: clomiphene citrate (CC), metformin and the combination of the two. There were 446 women who ovulated in response to the treatments among the entire 626 participants. In this report, we focus on the 418 women who received CC (alone or combined with metformin) to determine if readily available baseline physical characteristics and/or easily obtainable baseline measures could be used to distinguish treatment effectiveness in stimulating ovulation. We used a recursive partitioning technique and developed a node-splitting rule to build decision tree models that reflected within-node and within-treatment responses. RESULTS: Overall, the combination of CC plus metformin resulted in an increased incidence of ovulation compared with CC alone. This is particularly so in women with relatively larger left ovarian volumes (≥19.5 cubic cm), and a left ovarian volume <19.5 cubic cm was related to treatment outcomes for all subsequent nodes. Women who were older, who had higher baseline insulin, higher waist-to-hip circumference ratio or higher sex hormone-binding globulin levels had better ovulatory rates with CC alone than with the combination of CC plus metformin. CONCLUSIONS: Polycystic ovary syndrome (PCOS) is a phenotypically diverse condition. Both baseline laboratory and clinical parameters can predict the ovulatory response in women with PCOS undergoing ovulation induction. Without a priori hypotheses with regard to any predictors, the observation regarding left ovary volume is novel and worthy of further investigation and validation.

Original languageEnglish (US)
Pages (from-to)2612-2621
Number of pages10
JournalHuman Reproduction
Volume25
Issue number10
DOIs
StatePublished - Oct 2010
Externally publishedYes

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Keywords

  • PCOS
  • decision trees
  • ovulation induction
  • treatment effectiveness

ASJC Scopus subject areas

  • Reproductive Medicine
  • Obstetrics and Gynecology

Cite this

Zhang, H., Legro, R. S., Zhang, J., Zhang, L., Chen, X., Huang, H., Casson, P. R., Schlaff, W. D., Diamond, M. P., Krawetz, S. A., Coutifaris, C., Brzyski, R. G., Christman, G. M., Santoro, N., & Eisenberg, E. (2010). 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. Human Reproduction, 25(10), 2612-2621. https://doi.org/10.1093/humrep/deq210