Med Decis Making - Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

Tópicos

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Resumo

CKGROUND: To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better.METHODS: We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting.RESULTS: For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results.CONCLUSIONS: Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets.

Resumo Limpo

ckground identifi bestfit input set use model calibr individu calibr target fit often combin singl goodnessoffit gof measur use set weight decis calibr process weight use influenc set model input identifi bestfit potenti lead differ health econom conclus present altern approach identifi bestfit input set base concept paretooptim set model input pareto frontier input set simultan fit calibr target well bettermethod demonstr pareto frontier approach calibr model simpl illustr markov model previous publish costeffect model transcathet aortic valv replac tavr model compar input set pareto frontier equal number bestfit input set accord possibl weightedsum gof score system compar health econom conclus aris differ definit bestfittingresult simpl model outcom evalu bestfit input set accord weightedsum gof scheme virtual nonoverlap costeffect plane result differ increment costeffect ratio ci v ci per qualityadjust lifeyear qali gain input set pareto frontier span region ci per qali gain tavr model yield similar resultsconclus choic generat summari gof score may result differ health econom conclus pareto frontier approach elimin need make choic use intuit transpar notion optim basi identifi bestfit input set

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