Med Decis Making - Linear regression metamodeling as a tool to summarize and present simulation model results.

Tópicos

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Resumo

CKGROUND / OBJECTIVE: Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results.METHODS: We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs.RESULTS: The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values.CONCLUSIONS: Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Resumo Limpo

ckground object model lack tool systemat clear present complex model result includ sensit analys object propos linear regress metamodel tool increas transpar decis analyt model better communic resultsmethod use simplifi cancer cure model demonstr approach model comput lifetim cost benefit treatment option cancer patient simul cohort probabilist sensit analysi psa regress model outcom standard input paramet valu set regress analys use regress coeffici describ measur sensit analys includ threshold paramet sensit analys also compar result psa determinist fullfactori onefactoratatim designsresult regress intercept repres estim basecas outcom coeffici describ relat paramet uncertainti model defin simpl relationship comput averag increment net benefit intervent metamodel produc output similar tradit determinist way way sensit analys reliabl sinc use paramet valuesconclus linear regress metamodel simpl yet power tool can assist model communic model characterist sensit analys

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