A dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines.

Hamza, Tasnim; Furukawa, Toshi A; Orsini, Nicola; Cipriani, Andrea; Iglesias, Cynthia P; Salanti, Georgia (2022). A dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines. (In Press). Statistical methods in medical research, p. 9622802211070256. Sage Publications 10.1177/09622802211070256

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Network meta-analysis has been used to answer a range of clinical questions about the preferred intervention for a given condition. Although the effectiveness and safety of pharmacological agents depend on the dose administered, network meta-analysis applications typically ignore the role that drugs dosage plays in the results. This leads to more heterogeneity in the network. In this paper, we present a suite of network meta-analysis models that incorporate the dose-effect relationship using restricted cubic splines. We extend existing models into a dose-effect network meta-regression to account for study-level covariates and for groups of agents in a class-effect dose-effect network meta-analysis model. We apply our models to a network of aggregate data about the efficacy of 21 antidepressants and placebo for depression. We find that all antidepressants are more efficacious than placebo after a certain dose. Also, we identify the dose level at which each antidepressant's effect exceeds that of placebo and estimate the dose beyond which the effect of antidepressants no longer increases. When covariates were introduced to the model, we find that studies with small sample size tend to exaggerate antidepressants efficacy for several of the drugs. Our dose-effect network meta-analysis model with restricted cubic splines provides a flexible approach to modelling the dose-effect relationship in multiple interventions. Decision-makers can use our model to inform treatment choice.

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., Salanti, Georgia

Subjects:

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

ISSN:

1477-0334

Publisher:

Sage Publications

Language:

English

Submitter:

Pubmed Import

Date Deposited:

25 Feb 2022 09:29

Last Modified:

05 May 2023 00:11

Publisher DOI:

10.1177/09622802211070256

PubMed ID:

35200062

Uncontrolled Keywords:

Evidence synthesis dose-response meta-regression multiple treatments splines

BORIS DOI:

10.48350/166031

URI:

https://boris.unibe.ch/id/eprint/166031

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