Modeling Pregnancy Outcomes through Sequentially Nested Regression Models.

Xuan Bi, Long Feng, Cai Li, Heping Zhang

Abstract read

In one paragraph

Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xuan BiXuan Bi, Long Feng and Cai Li were postdoctoral associates, and Heping Zhang is Susan Dwight Bliss Professor, Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520. Xuan Bi, Long Feng and Cai Li contributed equally to this work. Xuan Bi is Assistant Professor, Carlson School of Management, University of Minnesota. Long Feng is Assistant Professor, School of Data Science, City University of Hong Kong. Cai Li is Assistant Professor, Department of Biostatistics, St. Jude Children's Research Hospital. The authors wish to thank the Reproductive Medicine Network for sharing the dataset. This work is supported in part by grants U10HD055925 from the National Institutes of Health.

Long FengXuan Bi, Long Feng and Cai Li were postdoctoral associates, and Heping Zhang is Susan Dwight Bliss Professor, Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520. Xuan Bi, Long Feng and Cai Li contributed equally to this work. Xuan Bi is Assistant Professor, Carlson School of Management, University of Minnesota. Long Feng is Assistant Professor, School of Data Science, City University of Hong Kong. Cai Li is Assistant Professor, Department of Biostatistics, St. Jude Children's Research Hospital. The authors wish to thank the Reproductive Medicine Network for sharing the dataset. This work is supported in part by grants U10HD055925 from the National Institutes of Health.

Cai LiXuan Bi, Long Feng and Cai Li were postdoctoral associates, and Heping Zhang is Susan Dwight Bliss Professor, Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520. Xuan Bi, Long Feng and Cai Li contributed equally to this work. Xuan Bi is Assistant Professor, Carlson School of Management, University of Minnesota. Long Feng is Assistant Professor, School of Data Science, City University of Hong Kong. Cai Li is Assistant Professor, Department of Biostatistics, St. Jude Children's Research Hospital. The authors wish to thank the Reproductive Medicine Network for sharing the dataset. This work is supported in part by grants U10HD055925 from the National Institutes of Health.

Heping ZhangXuan Bi, Long Feng and Cai Li were postdoctoral associates, and Heping Zhang is Susan Dwight Bliss Professor, Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520. Xuan Bi, Long Feng and Cai Li contributed equally to this work. Xuan Bi is Assistant Professor, Carlson School of Management, University of Minnesota. Long Feng is Assistant Professor, School of Data Science, City University of Hong Kong. Cai Li is Assistant Professor, Department of Biostatistics, St. Jude Children's Research Hospital. The authors wish to thank the Reproductive Medicine Network for sharing the dataset. This work is supported in part by grants U10HD055925 from the National Institutes of Health.

Funding

Data Coordination Center for the RMNU10HD055925 · NICHD · YALE UNIVERSITY · PI ZHANG, HEPING · 2007 to 2019

$39.2M

NICHD NIH HHS U10 HD055925

6 · The paper itself

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

Indexed as

Infertility studyℓ0 penalizationSequentially nested binary outcomeVariable selection

Identifiers

PMID36090951

DOI10.1080/01621459.2021.2006666

PMCPMC9454338

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.