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

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

Source: Human Reproduction, Volume 25, Number 10, 18 October 2010 , pp. 2612-2621(10)

Publisher: Oxford University Press

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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.

Keywords: PCOS; ovulation induction; decision trees; treatment effectiveness

Document Type: Research article

DOI: http://dx.doi.org/10.1093/humrep/deq210

Publication date: 2010-10-18

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Human Reproduction features full-length, peer-reviewed papers reporting original research, clinical case histories, as well as opinions and debates on topical issues. Papers published cover the scientific and medical aspects of reproductive physiology and pathology, endocrinology, andrology, gonad function, gametogenesis, fertilization, embryo development, implantation, pregnancy, genetics, genetic diagnosis, oncology, infectious disease, surgery, contraception, infertility treatment, psychology, ethics and social issues. The highest scientific and editorial standard is maintained throughout the journal along with a rapid rate of publication.

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

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