Search

The prevalence of depression and the high costs associated with its treatment have increased interest in pharmacoeconomic evaluations of drug treatment, particularly in the 1990s as the use of selective serotonin (5-hydroxytryptamine; 5-HT) reuptake inhibitors (SSRIs) expanded substantially. This review presents results from specific studies representing the key study designs used to address the pharmacoeconomics of SSRI use: retrospective administrative database analyses, clinical decision analysis models, and randomised clinical trials. Methodological considerations in interpreting results are highlighted. In retrospective administrative database analyses, most comparisons have been made between SSRIs and tricyclic antidepressants (TCAs). A few studies have addressed differences between SSRIs. The studies focused on healthcare cost (to payer) and cost-related outcomes (e.g. treatment duration, drug switching). Although SSRIs are generally associated with higher drug acquisition costs than are TCAs, total healthcare costs are at least offset, if not decreased, by reductions in costs associated with use of SSRIs. Although studies from the early 1990s show some advantage for fluoxetine, the results are limited by use of data from shortly after the introduction of paroxetine and sertraline; studies from the mid- 1990s on that compare drugs within the SSRI class show general equivalence in terms of cost. Important methodological advances are occurring in retrospective studies, with selection bias and other design limitations being addressed statistically. Clinical decision analysis models permit flexibility in terms of ability to specify different alternative treatment scenarios and varying durations. Sensitivity analysis aids interpretability, although model inputs are limited by data availability. Results from short term (1 year duration or less) studies comparing SSRIs and TCAs suggest that SSRIs are more cost effective or that there is no difference. Longer term studies (lifetime Markov models) focus more on the impact of maintenance antidepressant therapy and show more mixed results, generally favouring SSRIs over TCAs. The results indicate that the effect of SSRIs is mainly through prevention of relapse. Important assumptions of these models include fewer serious adverse effects and lower treatment discontinuation rates with SSRIs. Naturalistic clinical trials provide greater generalisability than traditional randomised clinical trials. One naturalistic trial found that nearly half of TCA-treated patients switched to another antidepressant within 6 months; only 20% of SSRI-treated patients switched. Cost differences between groups were minimal. These studies indicate few differences in medical costs, depression outcomes and health-related quality of life between TCAs and fluoxetine, although fewer fluoxetine-treated patients switched treatment. [ABSTRACT FROM AUTHOR]

Objective: 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.Design and Setting: 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 and Participants: 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.Methods: 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.Main Outcome Measures and Results: 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.Conclusions: 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. [ABSTRACT FROM AUTHOR]