Reconciling decision models with the real world. An application to anaemia of renal failure

The choice of evidence used in decision modelling of healthcare interventions divides analysts into 2 groups: (i) those who favour randomised clinical trial (RCT) data; and (ii) those who prefer 'real world' data. This preference may have serious consequences if the end result is to inform healthcare policy. This paper uses Medicare coverage of epoetin-alpha [erythropoietin (EPO)] as a case study to illustrate a technique which can be used to overcome some of the bias inherent in RCT data while avoiding some of the common pitfalls associated with the use of observational data.

Cost analysis of 2 treatments for anaemia of renal failure primarily in an outpatient setting is modelled in a decision tree. This method can be used to analyse healthcare interventions or policies in any setting.

Patients with nontransplanted end-stage renal disease (ESRD) who received either EPO or blood transfusion for treatment of anaemia at any time during the 1-year study period (July 1989 to June 1990) were included in the sample.

Outcome effects in the natural setting are decomposed into 2 parts: a treatment effect and a population effect. This is then extended to the special case of policy analysis. Logistic and multiple regression are used to estimate branch probabilities and payoffs, respectively, for 2 treatment options.

Under standard methods of decision analysis, an increase of $US7032 per patient following EPO coverage is observed. With the decomposition technique, the policy effect is estimated to be less, $US6172, the difference coming from the population effect.

Failure to remove population effects from observed outcome effects may lead to biased decision-making. Although not directly observable, the population effect can be imputed from secondary data. The decomposition and imputting technique allows for a more meaningful interpretation of the results for the purpose of policy analysis.