Power priors with entropy balancing weights in data augmentation of partially controlled randomized trials
In pediatric or orphan diseases, there are many instances where it is unfeasible to conduct randomized and controlled clinical trials. This is due in part to the difficulty of enrolling a sufficient number of patients over a reasonable time period to meet adequate statistical power to demonstrate th...
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Published in | Journal of biopharmaceutical statistics Vol. 32; no. 1; pp. 4 - 20 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
Taylor & Francis
02.01.2022
Taylor & Francis Ltd |
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Abstract | In pediatric or orphan diseases, there are many instances where it is unfeasible to conduct randomized and controlled clinical trials. This is due in part to the difficulty of enrolling a sufficient number of patients over a reasonable time period to meet adequate statistical power to demonstrate the treatment efficacy. One solution to reduce the sample size or expedite the trial timeline is to complement the current trial with real-world data. To this end, several propensity score-based methods have been developed to create defined groups of patients that are controlled for confounding based on a set of measured covariates at baseline. However, balance checking on the measured covariates and tweaks to the propensity score models is usually inevitable to achieve the joint balance across all covariates. To mitigate this iterative procedure, we utilize the entropy balancing weighting technique which focuses on balancing the covariates of subjects between the experimental and control groups directly and augments the current trial with the external control data via a power prior. The finite-sample properties of the proposed method are assessed via simulations in the context of asymmetrically randomized controlled trials where only a small portion of patients are randomized to the control group. Other methods such as covariate-balancing propensity score (CBPS) and propensity score matching (PSM) and weighting (PSW) are also compared to provide context on the operating characteristics of the proposed method. |
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AbstractList | In pediatric or orphan diseases, there are many instances where it is unfeasible to conduct randomized and controlled clinical trials. This is due in part to the difficulty of enrolling a sufficient number of patients over a reasonable time period to meet adequate statistical power to demonstrate the treatment efficacy. One solution to reduce the sample size or expedite the trial timeline is to complement the current trial with real-world data. To this end, several propensity score-based methods have been developed to create defined groups of patients that are controlled for confounding based on a set of measured covariates at baseline. However, balance checking on the measured covariates and tweaks to the propensity score models is usually inevitable to achieve the joint balance across all covariates. To mitigate this iterative procedure, we utilize the entropy balancing weighting technique which focuses on balancing the covariates of subjects between the experimental and control groups directly and augments the current trial with the external control data via a power prior. The finite-sample properties of the proposed method are assessed via simulations in the context of asymmetrically randomized controlled trials where only a small portion of patients are randomized to the control group. Other methods such as covariate-balancing propensity score (CBPS) and propensity score matching (PSM) and weighting (PSW) are also compared to provide context on the operating characteristics of the proposed method. |
Author | Yu, Guanglei Bian, Yuanyuan Gamalo, Margaret |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35072583$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1002/pst.2172 10.1111/1468-0262.00442 10.1002/pst.1879 10.1111/j.1467-985X.2007.00527.x 10.1214/ss/1009212673 10.1111/rssb.12027 10.1080/10543406.2020.1730877 10.1038/bcj.2016.84 10.1093/pan/mpr025 |
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SubjectTerms | asymmetrical randomization Bayesian-augmented control Child Entropy entropy balancing Humans power prior Propensity Score Randomized Controlled Trials as Topic Research Design Sample Size |
Title | Power priors with entropy balancing weights in data augmentation of partially controlled randomized trials |
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