Modeling Pregnancy Outcomes Through Sequentially Nested Regression Models

Xuan Bi, Long Feng, Cai Li and Heping Zhang

Journal of the American Statistical Association, 2022, vol. 117, issue 538, 602-616

Abstract: The polycystic ovary syndrome (PCOS) is a most common cause of infertility among women of reproductive age. Unfortunately, the etiology of PCOS is poorly understood. Large-scale clinical trials for pregnancy in polycystic ovary syndrome (PPCOS) were conducted to evaluate the effectiveness of treatments. Ovulation, pregnancy, and live birth are three sequentially nested binary outcomes, typically analyzed separately. However, the separate models may lose power in detecting the treatment effects and influential variables for live birth, due to decreased sample sizes and unbalanced event counts. It has been a long-held hypothesis among the clinicians that some of the important variables for early pregnancy outcomes may continue their influence on live birth. To consider this possibility, we develop an l0 -norm based regularization method in favor of variables that have been identified from an earlier stage. Our approach explicitly bridges the connections across nested outcomes through computationally easy algorithms and enjoys theoretical guarantee of estimation and variable selection. By analyzing the PPCOS data, we successfully uncover the hidden influence of risk factors on live birth, which confirm clinical experience. Moreover, we provide novel infertility treatment recommendations (e.g., letrozole vs. clomiphene citrate) for women with PCOS to improve their chances of live birth. Supplementary materials for this article are available online.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2021.2006666 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:117:y:2022:i:538:p:602-616

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2021.2006666

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().