Med Decis Making - Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: an application to cluster randomized trials.

Tópicos

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Resumo

RPOSE: Multiple imputation (MI) has been proposed for handling missing data in cost-effectiveness analyses (CEAs). In CEAs that use cluster randomized trials (CRTs), the imputation model, like the analysis model, should recognize the hierarchical structure of the data. This paper contrasts a multilevel MI approach that recognizes clustering, with single-level MI and complete case analysis (CCA) in CEAs that use CRTs.METHODS: We consider a multilevel MI approach compatible with multilevel analytical models for CEAs that use CRTs. We took fully observed data from a CEA that evaluated an intervention to improve diagnosis of active labor in primiparous women using a CRT (2078 patients, 14 clusters). We generated scenarios with missing costs and outcomes that differed, for example, according to the proportion with missing data (10%-50%), the covariates that predicted missing data (individual, cluster-level), and the missingness mechanism: missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). We estimated incremental net benefits (INBs) for each approach and compared them with the estimates from the fully observed data, the "true" INBs.RESULTS: When costs and outcomes were assumed to be MCAR, the INBs for each approach were similar to the true estimates. When data were MAR, the point estimates from the CCA differed from the true estimates. Multilevel MI provided point estimates and standard errors closer to the true values than did single-level MI across all settings, including those in which a high proportion of observations had cost and outcome data MAR and when data were MNAR.CONCLUSIONS: Multilevel MI accommodates the multilevel structure of the data in CEAs that use cluster trials and provides accurate cost-effectiveness estimates across the range of circumstances considered.

Resumo Limpo

rpose multipl imput mi propos handl miss data costeffect analys cea cea use cluster random trial crts imput model like analysi model recogn hierarch structur data paper contrast multilevel mi approach recogn cluster singlelevel mi complet case analysi cca cea use crtsmethod consid multilevel mi approach compat multilevel analyt model cea use crts took fulli observ data cea evalu intervent improv diagnosi activ labor primipar women use crt patient cluster generat scenario miss cost outcom differ exampl accord proport miss data covari predict miss data individu clusterlevel missing mechan miss complet random mcar miss random mar miss random mnar estim increment net benefit inb approach compar estim fulli observ data true inbsresult cost outcom assum mcar inb approach similar true estim data mar point estim cca differ true estim multilevel mi provid point estim standard error closer true valu singlelevel mi across set includ high proport observ cost outcom data mar data mnarconclus multilevel mi accommod multilevel structur data cea use cluster trial provid accur costeffect estim across rang circumst consid

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