Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools
Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2021-11-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/3820 |
| _version_ | 1797813938336825344 |
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| author | Sebastian Weber Yue Li John W. Seaman III Tomoyuki Kakizume Heinz Schmidli |
| author_facet | Sebastian Weber Yue Li John W. Seaman III Tomoyuki Kakizume Heinz Schmidli |
| author_sort | Sebastian Weber |
| collection | DOAJ |
| description | Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical data. In this paper, we introduce the package RBesT (R Bayesian evidence synthesis tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by Markov chain Monte Carlo (MCMC). The MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors. The paper first introduces the framework of robust Bayesian evidence synthesis in this setting and then explains how RBesT facilitates the derivation and evaluation of an informative MAP prior from historical control data. In addition we describe how the meta-analytic framework relates to further applications including probability of success calculations. |
| first_indexed | 2024-03-13T08:00:07Z |
| format | Article |
| id | doaj.art-69c05d11bda5431eb8eb6654b602e961 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-03-13T08:00:07Z |
| publishDate | 2021-11-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-69c05d11bda5431eb8eb6654b602e9612023-06-01T18:48:04ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602021-11-0110013210.18637/jss.v100.i193636Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis ToolsSebastian Weber0Yue Li1John W. Seaman III2Tomoyuki Kakizume3Heinz Schmidli4https://orcid.org/0000-0003-0934-1066Novartis Pharma AGNovartis Pharma AGNovartis Pharma AGNovartis Pharma K.K.Novartis Pharma AGUse of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical data. In this paper, we introduce the package RBesT (R Bayesian evidence synthesis tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by Markov chain Monte Carlo (MCMC). The MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors. The paper first introduces the framework of robust Bayesian evidence synthesis in this setting and then explains how RBesT facilitates the derivation and evaluation of an informative MAP prior from historical control data. In addition we describe how the meta-analytic framework relates to further applications including probability of success calculations.https://www.jstatsoft.org/index.php/jss/article/view/3820bayesian inferenceclinical trialextrapolationhistorical controloperating characteristicspriorprobability of successrobust analysis |
| spellingShingle | Sebastian Weber Yue Li John W. Seaman III Tomoyuki Kakizume Heinz Schmidli Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools Journal of Statistical Software bayesian inference clinical trial extrapolation historical control operating characteristics prior probability of success robust analysis |
| title | Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools |
| title_full | Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools |
| title_fullStr | Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools |
| title_full_unstemmed | Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools |
| title_short | Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools |
| title_sort | applying meta analytic predictive priors with the r bayesian evidence synthesis tools |
| topic | bayesian inference clinical trial extrapolation historical control operating characteristics prior probability of success robust analysis |
| url | https://www.jstatsoft.org/index.php/jss/article/view/3820 |
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