Decision-analytic models

Decision-analytic models are structured methods of incorporating probabilities and costs of likely events for expected therapeutic pathways and... 

Figure 24.2 The Markov decision-analytic model shows cost and cost-effectiveness evaluations for patients undergoing renal transplant [17]. a = cost b = cost per functioning graft c = cost per rejection-free clinical course.

Figure 24.4 The decision-analytic model shows the three strategies that were examined by Arnold and researchers [22] to evaluate the financial implications of the direct thrombin inhibitor argatroban for early treatment (<48 hours after thrombocytopenia onset), compared with delayed treatment, of heparin-induced thrombocytopenia (HIT) with or without thrombosis.

A variety of data sources are available to inform interactive programs, including prospective data sets, retrospective databases, expert opinion, and unpub-lished/published literature. Time horizon, that is, the length of time into the future considered in the analysis over which costs and outcomes are projected, is very important here [26]. For example, if a clinical trial or the published literature only report short-term results for a chronic condition, the outcomes may come into question. This is where decision-analytic models may come... 

Expert opinion is a source, frequently elicited by survey, that is used to obtain information where no or few data are available. For example, in our experience with a multicountry evaluation of health care resource utilization in atrial fibrillation, very few country-specific published data were available on this subject. Thus the decision-analytic model was supplemented with data from a physician expert panel survey to determine initial management approach (rate control vs. cardioversion) first-, second-, and third-line agents doses and durations of therapy type and frequency of studies that would be performed to initiate and monitor therapy type and frequency of adverse events, by body system and the resources used to manage them place of treatment and adverse consequences of lack of atrial fibrillation control and cost of these consequences, for example, stroke, congestive heart failure. This method may also be used in testing the robustness of the analysis [30]. 

In all analyses, there is uncertainty about the accuracy of the results that may be dealt with via sensitivity analyses [1, 2]. In these analyses, one essentially asks the question What if These allow one to vary key values over clinically feasible ranges to determine whether the decision remains the same, that is, if the strategy initially found to be cost-effective remains the dominant strategy. By performing sensitivity analyses, one can increase the level of confidence in the conclusions. Sensitivity analyses also allow one to determine threshold values for these key parameters at which the decision would change. For example, in the previous example of a Bayesian evaluation embedded in a decision-analytic model of pancreatic cancer, a sensitivity analysis (Fig. 24.6) was conducted to evaluate the relationship... 

Bree RL, Arnold RJ, Pettit KG, et al. Use of a decision-analytic model to support the use of a new oral US contrast agent in patients with abdominal pain. Acad Radiol 2001 8 234-42. 

Weinstein M, O Brien B, Hornberger J, et al. Principles of good practice of decision analytic modeling in health care evalnation Report of the ISPOR Task Force on Good Research Practices-Modeling Stndies. Value Health 2003 6 9-17. 

Data on antidepressant dmgs are available from a number of sources randomized, controlled clinical trials (RCTs) in both hospital and primary-care populations decision analytic models population-based naturalistic observational studies of usual... 

Decision analytic models, or simulation models of clinical decision analysis, usually involve the creation of a treatment decision/outcome tree based on a synthesis of expert opinion, sometimes using validated methods of canvassing opinion such as recruiting a Delphi panel (Hatziandreu et al, 1994 Einarson et al, 1995). The decision tree... 

Decision analytic models have been constmcted to compare the costs of TCAs with those of SSRIs and other compounds. These comparisons have included imipramine or amitriptyline versus paroxetine or sertraline (Stewart, 1994) imipramine versus paroxetine Qonsson and Bebbington, 1994 McFarland, 1994 Lapierre et al, 1995) fluoxetine versus amitriptyline, clomipramine, doxepin and imipramine (Le Pen et al, 1994) venlafaxine versus amitriptyline, desipramine. 

Apart from decision analytic models, pharmacoeconomic data for newer antidepressants are lacking. 

More generally, the presence or absence of good clinical data imposes limits on the economic evaluations that can be conducted. It is usually easier to undertake economic evaluations of drug therapies, where there are usually some reasonable clinical data, as compared with medical devices or procedures. However, even for drugs, the data from standard Phase 3 clinical trials are far from ideal. These trials are usually conducted under conditions that are atypical of normal practice. In addition, they are often of short duration and often fail to compare the most relevant alternatives for economic evaluation. This means that decision-analytic modeling is usually required as a supplement to, or alternative to, economic analysis alongside trials. 

Saab, S., DeRosa, V., Nieto, J., Durazo, F., Han, S., Roth, B. Costs and clinical outcomes of primary prophylaxis of variceal bleeding in patients with hepatic cirrhosis A decision analytic model. Amer. J. Gastroenterol. 2003 98 763-770... 

Perils RH, Ganz DA, Avorn J, et al. Pharmacogenetic testing in the clinical management of schizophrenia a decision-analytic model. J Clin Psychopharmacol 2005 25 427-34. 

Akehurst R, Anderson P, Brazier J, et al. Consensus Conference on Guidelines on Economic Modeling in Health Technology Assessment. Decision analytic modeling in economic evaluation of health technologies A consensus statement. Pharmacoeconomics 2000 17 443M44. 

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