Med Decis Making - Assessing uncertainties surrounding combined endpoints for use in economic models.

Tópicos

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Resumo

CKGROUND: To increase power to detect a treatment effect, trials may combine multiple endpoints such as survival, myocardial infarctions, and strokes. When such trials are used to define the uncertainty associated with input parameters in an economic model, the output uncertainty will depend on the way in which the dependency between individual endpoints is modeled.OBJECTIVE: To develop a flexible approach to model the interrelationship between individual components of a combined endpoint.METHODS: A standard independent Dirichlet approach is compared with a dependent Dirichlet approach and logistic approaches. The logistic approaches use a link between the various endpoints by either an observed clinical variable (cholesterol) or a latent one. The logistic and Dirichlet methods are compared using 6 statin trials including 5 endpoints: myocardial infarction (MI), stroke, fatal MI, fatal stroke, and other cardiovascular death. The results are compared using the point estimates and uncertainty in a simplified cardiovascular model to calculate point estimates and uncertainty of estimated incremental life-years when applying probabilistic sensitivity analysis. The influence of the link between endpoints is tested by changing the prior in the logistic approaches.RESULTS: The dependent Dirichlet approach reduces uncertainty up to 29% and changes the point estimate by up to 28%. The logistic approach with uninformative priors does not affect the uncertainty and point estimates. When strong priors are used, the uncertainty margins get smaller (up to 49%) and point estimates vary more. Including information about cholesterol has limited impact.CONCLUSIONS: The logistic approaches offer a flexible way to reflect one's beliefs about the interrelationships between individual endpoints, potentially decreasing uncertainty margins. The approach works equally well with and without data concerning the underlying disease process.

Resumo Limpo

ckground increas power detect treatment effect trial may combin multipl endpoint surviv myocardi infarct stroke trial use defin uncertainti associ input paramet econom model output uncertainti will depend way depend individu endpoint modeledobject develop flexibl approach model interrelationship individu compon combin endpointmethod standard independ dirichlet approach compar depend dirichlet approach logist approach logist approach use link various endpoint either observ clinic variabl cholesterol latent one logist dirichlet method compar use statin trial includ endpoint myocardi infarct mi stroke fatal mi fatal stroke cardiovascular death result compar use point estim uncertainti simplifi cardiovascular model calcul point estim uncertainti estim increment lifeyear appli probabilist sensit analysi influenc link endpoint test chang prior logist approachesresult depend dirichlet approach reduc uncertainti chang point estim logist approach uninform prior affect uncertainti point estim strong prior use uncertainti margin get smaller point estim vari includ inform cholesterol limit impactconclus logist approach offer flexibl way reflect one belief interrelationship individu endpoint potenti decreas uncertainti margin approach work equal well without data concern under diseas process

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