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A comparison of frequentist and Bayesian approaches to the Personalised Randomised Controlled Trial (PRACTical)—design and analysis considerations

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Titel: A comparison of frequentist and Bayesian approaches to the Personalised Randomised Controlled Trial (PRACTical)—design and analysis considerations
Autoren: Jackson, Holly, Shou, Yiyun, Azad, Nur Amira Binte Mohamed, Chua, Jing Wen, Perez, Rebecca Lynn, Wang, Xinru, De Kraker, Marlieke, Mo, Yin
Quelle: BMC Med Res Methodol
BMC Medical Research Methodology, Vol 25, Iss 1, Pp 1-14 (2025)
Verlagsinformationen: Springer Science and Business Media LLC, 2024.
Publikationsjahr: 2024
Schlagwörter: Anti-Bacterial Agents / therapeutic use, Trial design, Medicine (General), Statistical simulation, Randomized Controlled Trials as Topic / methods, Research, Bayes Theorem, Antibiotic treatment, Bayesian, R5-920, Research Design, Humans, Infectious diseases, Computer Simulation, Personalised randomisation, Precision Medicine / methods, PRACTical
Beschreibung: Background Multiple treatment options frequently exist for a single medical condition with no single standard of care (SoC), rendering a classic randomised trial comparing a specific treatment to a control treatment infeasible. A novel design, the personalised randomised controlled trial (PRACTical), allows individualised randomisation lists and borrows information across patient subpopulations to rank treatments against each other without comparison to a SoC. We evaluated standard frequentist analysis with Bayesian analyses, and developed a novel performance measure, utilising the precision in treatment coefficient estimates, for treatment ranking. Methods We simulated trial data to compare four targeted antibiotic treatments for multidrug resistant bloodstream infections as an example. Four patient subgroups were simulated based on different combinations of patient and bacteria characteristics, which required four different randomisation lists with some overlapping treatments. The primary outcome was binary, using 60-day mortality. Treatment effects were derived using frequentist and Bayesian analytical approaches, with logistic multivariable regression. The performance measures were: probability of predicting the true best treatment, and novel proxy variables for power (probability of interval separation) and type I error (probability of incorrect interval separation). Several scenarios with varying treatment effects and sample sizes were compared. Results The Bayesian model using a strong informative prior, was the most likely to predict the true best treatment (\(\:{P}_{best}\ge\:80\%\)) and gave the largest probability of interval separation (reaching a maximum of \(\:{P}_{IS}=96\%\)), at a given sample size. Both Bayesian and frequentist methods had a low probability of incorrect interval separation (\(\:{P}_{IIS}
Publikationsart: Article
Other literature type
Dateibeschreibung: application/pdf
Sprache: English
ISSN: 1471-2288
DOI: 10.1186/s12874-025-02537-x
DOI: 10.21203/rs.3.rs-5002621/v1
Zugangs-URL: https://doaj.org/article/04d2348d85a744f8b9d2f35b5f44cdcc
Rights: CC BY NC ND
CC BY
Dokumentencode: edsair.doi.dedup.....3e887658ce18123e5bb613113427f6c5
Datenbank: OpenAIRE
Beschreibung
Abstract:Background Multiple treatment options frequently exist for a single medical condition with no single standard of care (SoC), rendering a classic randomised trial comparing a specific treatment to a control treatment infeasible. A novel design, the personalised randomised controlled trial (PRACTical), allows individualised randomisation lists and borrows information across patient subpopulations to rank treatments against each other without comparison to a SoC. We evaluated standard frequentist analysis with Bayesian analyses, and developed a novel performance measure, utilising the precision in treatment coefficient estimates, for treatment ranking. Methods We simulated trial data to compare four targeted antibiotic treatments for multidrug resistant bloodstream infections as an example. Four patient subgroups were simulated based on different combinations of patient and bacteria characteristics, which required four different randomisation lists with some overlapping treatments. The primary outcome was binary, using 60-day mortality. Treatment effects were derived using frequentist and Bayesian analytical approaches, with logistic multivariable regression. The performance measures were: probability of predicting the true best treatment, and novel proxy variables for power (probability of interval separation) and type I error (probability of incorrect interval separation). Several scenarios with varying treatment effects and sample sizes were compared. Results The Bayesian model using a strong informative prior, was the most likely to predict the true best treatment (\(\:{P}_{best}\ge\:80\%\)) and gave the largest probability of interval separation (reaching a maximum of \(\:{P}_{IS}=96\%\)), at a given sample size. Both Bayesian and frequentist methods had a low probability of incorrect interval separation (\(\:{P}_{IIS}
ISSN:14712288
DOI:10.1186/s12874-025-02537-x