Med Decis Making - Expected value of sample information for multi-arm cluster randomized trials with binary outcomes.

Tópicos

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Resumo

Expected value of sample information (EVSI) measures the anticipated net benefit gained from conducting new research with a specific design to add to the evidence on which reimbursement decisions are made. Cluster randomized trials raise specific issues for EVSI calculations because 1) a hierarchical model is necessary to account for between-cluster variability when incorporating new evidence and 2) heterogeneity between clusters needs to be carefully characterized in the cost-effectiveness analysis model. Multi-arm trials provide parameter estimates that are correlated, which needs to be accounted for in EVSI calculations. Furthermore, EVSI is computationally intensive when the net benefit function is nonlinear, due to the need for an inner-simulation step. We develop a method for the computation of EVSI that avoids the inner simulation step for cluster randomized multi-arm trials with a binary outcome, where the net benefit function is linear in the probability of an event but nonlinear in the log-odds ratio parameters. We motivate and illustrate the method with an example of a cluster randomized 2 ? 2 factorial trial for interventions to increase attendance at breast screening in the UK, using a previously reported cost-effectiveness model. We highlight assumptions made in our approach, extensions to individually randomized trials and inclusion of covariates, and areas for further developments. We discuss computation time, the research-design space, and the ethical implications of an EVSI approach. We suggest that EVSI is a practical and appropriate tool for the design of cluster randomized trials.

Resumo Limpo

expect valu sampl inform evsi measur anticip net benefit gain conduct new research specif design add evid reimburs decis made cluster random trial rais specif issu evsi calcul hierarch model necessari account betweenclust variabl incorpor new evid heterogen cluster need care character costeffect analysi model multiarm trial provid paramet estim correl need account evsi calcul furthermor evsi comput intens net benefit function nonlinear due need innersimul step develop method comput evsi avoid inner simul step cluster random multiarm trial binari outcom net benefit function linear probabl event nonlinear logodd ratio paramet motiv illustr method exampl cluster random factori trial intervent increas attend breast screen uk use previous report costeffect model highlight assumpt made approach extens individu random trial inclus covari area develop discuss comput time researchdesign space ethic implic evsi approach suggest evsi practic appropri tool design cluster random trial

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