A Bayesian dose-response meta-analysis model: A simulations study and application.

Hamza, Tasnim; Cipriani, Andrea; Furukawa, Toshi A; Egger, Matthias; Orsini, Nicola; Salanti, Georgia (2021). A Bayesian dose-response meta-analysis model: A simulations study and application. (In Press). Statistical Methods in Medical Research, p. 962280220982643. SAGE Publications (UK and US) 10.1177/0962280220982643

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Dose-response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose-response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose-response model implemented in a Bayesian framework. We develop our model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. To allow maximum flexibility, the dose-response association is modelled using restricted cubic splines. We implement these models in R using JAGS and we compare our approach to the one-stage dose-response meta-analysis model in a simulation study. We found that the Bayesian dose-response model with binomial likelihood has lower bias than the Bayesian model with normal likelihood and the frequentist one-stage model when studies have small sample size. When the true underlying shape is log-log or half-sigmoid, the performance of all models depends on choosing an appropriate location for the knots. In all other examined situations, all models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of serotonin-specific reuptake inhibitor (SSRI) antidepressant drugs. All models suggest that the dose-response curve increases between zero dose and 30-40 mg of fluoxetine-equivalent dose, and thereafter shows small decline. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approach and offers maximum flexibility to accommodate features of the data.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

Graduate School:

Graduate School for Health Sciences (GHS)

UniBE Contributor:

Hamza, Tasnim A. A.; Egger, Matthias and Salanti, Georgia

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0962-2802

Publisher:

SAGE Publications (UK and US)

Language:

English

Submitter:

Doris Kopp Heim

Date Deposited:

04 Feb 2021 20:28

Last Modified:

05 Feb 2021 01:35

Publisher DOI:

10.1177/0962280220982643

PubMed ID:

33504274

Uncontrolled Keywords:

Clusters antidepressants hierarchical model one-stage model random effects

BORIS DOI:

10.48350/151952

URI:

https://boris.unibe.ch/id/eprint/151952

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