Med Decis Making - Cohort decomposition for Markov cost-effectiveness models.

Tópicos

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Resumo

Cohort analysis is a widespread tool for computing expected costs and quality-adjusted life years (QALYs) in Markov models for medical cost-effectiveness analyses. Although not always explicitly identified, such models commonly have multiple simple factors, or components. In these, a health state consists of a multiple component vector, one component for each factor, and arbitrary combinations of components are possible. The authors show here that when the model does not assume any probabilistic dependence among these factors, then a standard cohort analysis may be decomposed into several independent cohort analyses, one for each factor, and the results may be combined to produce desired expected costs and QALYs. These single-factor cohort analyses are not only simpler but also computationally more efficient. The authors derive the appropriate formulas for this cohort decomposition in discrete time and give several examples of their use based on published cost-effectiveness analyses. Explicitly identifying the simple factors of which a model is composed allows these factors to be portrayed graphically. Graphical depiction of the simple factors that comprise a model reduces model complexity, makes model formulation easier and more transparent, and thereby facilitates peer inspection and critique.

Resumo Limpo

cohort analysi widespread tool comput expect cost qualityadjust life year qali markov model medic costeffect analys although alway explicit identifi model common multipl simpl factor compon health state consist multipl compon vector one compon factor arbitrari combin compon possibl author show model assum probabilist depend among factor standard cohort analysi may decompos sever independ cohort analys one factor result may combin produc desir expect cost qali singlefactor cohort analys simpler also comput effici author deriv appropri formula cohort decomposit discret time give sever exampl use base publish costeffect analys explicit identifi simpl factor model compos allow factor portray graphic graphic depict simpl factor compris model reduc model complex make model formul easier transpar therebi facilit peer inspect critiqu

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