Med Decis Making - Cost-Effectiveness Uncertainty Analysis Methods: A Comparison of One-Way Sensitivity, Analysis of Covariance, and Expected Value of Partial Perfect Information.

Tópicos

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Resumo

JECTIVES: To compare model input influence on incremental net monetary benefit (INMB) across 3 uncertainty methods: 1) 1-way sensitivity analysis; 2) probabilistic analysis of covariance (ANCOVA); and 3) expected value of partial perfect information (EVPPI).METHODS: In a preliminary model, we used a published cost-effectiveness model and assumed ?20,000 per quality-adjusted life-year (QALY) willingness-to-pay (Case 1: lower decision uncertainty) and ?8000/QALY willingness-to-pay (Case 2: higher decision uncertainty). We conducted 1-way sensitivity, ANCOVA (10,000 Monte Carlo draws), and EVPPI for each model input (1000 inner and 1000 outer draws). We ranked inputs based on influence of INMB and compared input ranks across methods within case using Spearman's rank correlation. We replicated this approach in 3 follow-up models: an additional linear model, a less linear model with uncorrelated inputs, and a less linear model with correlated inputs.RESULTS: In the preliminary model, lower and higher decision uncertainty cases had the same top 3 influential parameters across uncertainty methods. The 2 most influential inputs contributed 78% and 49% of variation in outcome based on ANCOVA for lower decision uncertainty and higher decision uncertainty cases, respectively. In the follow-up models, input rank order correlations were higher across uncertainty methods in the linear model compared with both of the less linear models.CONCLUSIONS: Evidence across models suggests influential input rank agreement between 1-way and more advanced uncertainty analyses for relatively linear models with uncorrelated parameters but less agreement for less linear models. Although each method provides unique information, the additional resources needed to generate and communicate advanced analyses should be weighed, especially when outcome decision uncertainty is low. For less linear models or those with correlated inputs, performing and reporting deterministic and probabilistic uncertainty analyses appear prudent and conservative.

Resumo Limpo

jectiv compar model input influenc increment net monetari benefit inmb across uncertainti method way sensit analysi probabilist analysi covari ancova expect valu partial perfect inform evppimethod preliminari model use publish costeffect model assum per qualityadjust lifeyear qali willingnesstopay case lower decis uncertainti qali willingnesstopay case higher decis uncertainti conduct way sensit ancova mont carlo draw evppi model input inner outer draw rank input base influenc inmb compar input rank across method within case use spearman rank correl replic approach followup model addit linear model less linear model uncorrel input less linear model correl inputsresult preliminari model lower higher decis uncertainti case top influenti paramet across uncertainti method influenti input contribut variat outcom base ancova lower decis uncertainti higher decis uncertainti case respect followup model input rank order correl higher across uncertainti method linear model compar less linear modelsconclus evid across model suggest influenti input rank agreement way advanc uncertainti analys relat linear model uncorrel paramet less agreement less linear model although method provid uniqu inform addit resourc need generat communic advanc analys weigh especi outcom decis uncertainti low less linear model correl input perform report determinist probabilist uncertainti analys appear prudent conserv

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