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

Bomee Kim, Yun Ji Jang, Hae Ram Cho, So Yeon Kim, Ji Eun Jeong, Mi Kyoung Shim, Myeong Gyu Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)691-699
Number of pages9
JournalClinical and Translational Science
Volume15
Issue number3
DOIs
StatePublished - Mar 2022

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