GEInfo: an R package for gene–environment interaction analysis incorporating prior information

Wang, Xiaoyan; Liu, Hongduo; Ma, Shuangge

doi:10.1093/bioinformatics/btac301

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…The propensity-score integrated power prior [17] and the composite likelihood method [18] are implemented in the R package psrwe [19]. The program is easy to use, including functions to implement each step of the process, starting with one function to estimate the propensity scores efalse(Xfalse), a function to create the stratification, and finally two functions to implement either the PS-integrated power prior or the composite likelihood, as desired.…”

Section: Real-world Evidence In Bayesian Clinical Trial Designsmentioning

confidence: 99%

Bayesian approaches to include real-world data in clinical studies

Müller

Chandra

Sarkar

2023

Phil. Trans. R. Soc. A.

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Randomized clinical trials have been the mainstay of clinical research, but are prohibitively expensive and subject to increasingly difficult patient recruitment. Recently, there is a movement to use real-world data (RWD) from electronic health records, patient registries, claims data and other sources in lieu of or supplementing controlled clinical trials. This process of combining information from diverse sources calls for inference under a Bayesian paradigm. We review some of the currently used methods and a novel non-parametric Bayesian (BNP) method. Carrying out the desired adjustment for differences in patient populations is naturally done with BNP priors that facilitate understanding of and adjustment for population heterogeneities across different data sources. We discuss the particular problem of using RWD to create a synthetic control arm to supplement single-arm treatment only studies. At the core of the proposed approach is the model-based adjustment to achieve equivalent patient populations in the current study and the (adjusted) RWD. This is implemented using common atoms mixture models. The structure of such models greatly simplifies inference. The adjustment for differences in the populations can be reduced to ratios of weights in such mixtures. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.

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