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Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models

This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We collected data on 819 clinical trials performed on p...

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Autores principales: Kim, Bomee, Jang, Yun Ji, Cho, Hae Ram, Kim, So Yeon, Jeong, Ji Eun, Shim, Mi Kyoung, Kim, Myeong Gyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932703/
https://www.ncbi.nlm.nih.gov/pubmed/34735737
http://dx.doi.org/10.1111/cts.13187
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author Kim, Bomee
Jang, Yun Ji
Cho, Hae Ram
Kim, So Yeon
Jeong, Ji Eun
Shim, Mi Kyoung
Kim, Myeong Gyu
author_facet Kim, Bomee
Jang, Yun Ji
Cho, Hae Ram
Kim, So Yeon
Jeong, Ji Eun
Shim, Mi Kyoung
Kim, Myeong Gyu
author_sort Kim, Bomee
collection PubMed
description This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We collected data on 819 clinical trials performed on pregnant women and intervention studies using at least one drug as intervention from 2009 to 2018 from ClinicalTrials.gov. The Cox proportional hazard model and DeepSurv were used to develop models that predict clinical trial completion. The concordance index (C‐index) was used to evaluate the predictive performance. The Cox proportional hazard model revealed that a sample size of n ≥ 329 (hazard ratio [HR] = 0.53), very high human development index (HDI) country (HR = 0.28), abortion (HR = 3.30), labor (HR = 2.16), and iron deficiency anemia (HR = 2.29) were significantly related to the probability of clinical trial completion (all p value < 0.01). The C‐index of the model development dataset and test dataset were 0.72 and 0.73, respectively. DeepSurv model consisted of one hidden layer with 16 nodes. DeepSurv showed the C‐index comparable to the Cox proportional hazard model. The C‐index of the training dataset and test dataset were 0.76 and 0.72, respectively. Further a nomogram that calculate a probability of clinical trial completion at 1 year, 3 years, and 5 years was developed. Both the Cox proportional hazard model and DeepSurv yielded sufficient predicting performance. We hope that this study will contribute to the execution of future clinical trials in pregnant women.
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spelling pubmed-89327032022-03-24 Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models Kim, Bomee Jang, Yun Ji Cho, Hae Ram Kim, So Yeon Jeong, Ji Eun Shim, Mi Kyoung Kim, Myeong Gyu Clin Transl Sci Research This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We collected data on 819 clinical trials performed on pregnant women and intervention studies using at least one drug as intervention from 2009 to 2018 from ClinicalTrials.gov. The Cox proportional hazard model and DeepSurv were used to develop models that predict clinical trial completion. The concordance index (C‐index) was used to evaluate the predictive performance. The Cox proportional hazard model revealed that a sample size of n ≥ 329 (hazard ratio [HR] = 0.53), very high human development index (HDI) country (HR = 0.28), abortion (HR = 3.30), labor (HR = 2.16), and iron deficiency anemia (HR = 2.29) were significantly related to the probability of clinical trial completion (all p value < 0.01). The C‐index of the model development dataset and test dataset were 0.72 and 0.73, respectively. DeepSurv model consisted of one hidden layer with 16 nodes. DeepSurv showed the C‐index comparable to the Cox proportional hazard model. The C‐index of the training dataset and test dataset were 0.76 and 0.72, respectively. Further a nomogram that calculate a probability of clinical trial completion at 1 year, 3 years, and 5 years was developed. Both the Cox proportional hazard model and DeepSurv yielded sufficient predicting performance. We hope that this study will contribute to the execution of future clinical trials in pregnant women. John Wiley and Sons Inc. 2021-11-17 2022-03 /pmc/articles/PMC8932703/ /pubmed/34735737 http://dx.doi.org/10.1111/cts.13187 Text en © 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research
Kim, Bomee
Jang, Yun Ji
Cho, Hae Ram
Kim, So Yeon
Jeong, Ji Eun
Shim, Mi Kyoung
Kim, Myeong Gyu
Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models
title Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models
title_full Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models
title_fullStr Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models
title_full_unstemmed Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models
title_short Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models
title_sort predicting completion of clinical trials in pregnant women: cox proportional hazard and neural network models
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932703/
https://www.ncbi.nlm.nih.gov/pubmed/34735737
http://dx.doi.org/10.1111/cts.13187
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