Subgroup identification of early preterm birth (ePTB): informing a future prospective enrichment clinical trial design
dc.contributor.author | Zhang, Chuanwu | |
dc.contributor.author | Garrard, Lili | |
dc.contributor.author | Keighley, John | |
dc.contributor.author | Carlson, Susan E. | |
dc.contributor.author | Gajewski, Byron J. | |
dc.date.accessioned | 2017-09-01T19:11:51Z | |
dc.date.available | 2017-09-01T19:11:51Z | |
dc.date.issued | 2017-01 | |
dc.identifier.citation | Zhang, C., Garrard, L., Keighley, J., Carlson, S., & Gajewski, B. (2017). Subgroup identification of early preterm birth (ePTB): informing a future prospective enrichment clinical trial design. BMC Pregnancy and Childbirth, 17, 18. http://doi.org/10.1186/s12884-016-1189-0 | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/24891 | |
dc.description | A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml. | |
dc.description.abstract | Background: Despite the widely recognized association between the severity of early preterm birth (ePTB) and its related severe diseases, little is known about the potential risk factors of ePTB and the sub-population with high risk of ePTB. Moreover, motivated by a future confirmatory clinical trial to identify whether supplementing pregnant women with docosahexaenoic acid (DHA) has a different effect on the risk subgroup population or not in terms of ePTB prevalence, this study aims to identify potential risk subgroups and risk factors for ePTB, defined as babies born less than 34 weeks of gestation. Methods: The analysis data (N = 3,994,872) were obtained from CDC and NCHS’ 2014 Natality public data file. The sample was split into independent training and validation cohorts for model generation and model assessment, respectively. Logistic regression and CART models were used to examine potential ePTB risk predictors and their interactions, including mothers’ age, nativity, race, Hispanic origin, marital status, education, pre-pregnancy smoking status, pre-pregnancy BMI, pre-pregnancy diabetes status, pre-pregnancy hypertension status, previous preterm birth status, infertility treatment usage status, fertility enhancing drug usage status, and delivery payment source. Results: Both logistic regression models with either 14 or 10 ePTB risk factors produced the same C-index (0.646) based on the training cohort. The C-index of the logistic regression model based on 10 predictors was 0.645 for the validation cohort. Both C-indexes indicated a good discrimination and acceptable model fit. The CART model identified preterm birth history and race as the most important risk factors, and revealed that the subgroup with a preterm birth history and a race designation as Black had the highest risk for ePTB. The c-index and misclassification rate were 0.579 and 0.034 for the training cohort, and 0.578 and 0.034 for the validation cohort, respectively. Conclusions: This study revealed 14 maternal characteristic variables that reliably identified risk for ePTB through either logistic regression model and/or a CART model. Moreover, both models efficiently identify risk subgroups for further enrichment clinical trial design. | en_US |
dc.publisher | BioMed Central | en_US |
dc.rights | © The Author(s). 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0 | en_US |
dc.subject | Early preterm birth | en_US |
dc.subject | Risk factor | en_US |
dc.subject | Interaction | en_US |
dc.subject | Classification and regression tree | en_US |
dc.subject | Logistic regression | en_US |
dc.subject | Enrichment trial design | en_US |
dc.title | Subgroup identification of early preterm birth (ePTB): informing a future prospective enrichment clinical trial design | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Zhang, Chuanwu | |
kusw.kuauthor | Keighley, John | |
kusw.kuauthor | Gajewski, Byron | |
kusw.kuauthor | Carlson, Susan | |
kusw.kudepartment | Biostatistics | en_US |
kusw.kudepartment | Dietetics and Nutrition | en_US |
dc.identifier.doi | 10.1186/s12884-016-1189-0 | en_US |
kusw.oaversion | Scholarly/refereed, publisher version | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.identifier.pmid | PMC5223445 | en_US |
dc.rights.accessrights | openAccess |
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