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- Cao, Qi, et al.
(author)
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Continuous-Time Semi-Markov Models in Health Economic Decision Making: An Illustrative Example in Heart Failure Disease Management
- 2016
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In: Medical decision making. - : SAGE PUBLICATIONS INC. - 0272-989X .- 1552-681X. ; 36:1, s. 59-71
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Journal article (peer-reviewed)abstract
- Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patients disease progression can often be obtained by assuming that the future state transitions do not depend only on the present state (Markov assumption) but also on the past through time since entry in the present state. Despite that these so-called semi-Markov models are still relatively straightforward to specify and implement, they are not yet routinely applied in health economic evaluation to assess the cost-effectiveness of alternative interventions. To facilitate a better understanding of this type of model among applied health economic analysts, the first part of this article provides a detailed discussion of what the semi-Markov model entails and how such models can be specified in an intuitive way by adopting an approach called vertical modeling. In the second part of the article, we use this approach to construct a semi-Markov model for assessing the long-term cost-effectiveness of 3 disease management programs for heart failure. Compared with a standard Markov model with the same disease states, our proposed semi-Markov model fitted the observed data much better. When subsequently extrapolating beyond the clinical trial period, these relatively large differences in goodness-of-fit translated into almost a doubling in mean total cost and a 60-d decrease in mean survival time when using the Markov model instead of the semi-Markov model. For the disease process considered in our case study, the semi-Markov model thus provided a sensible balance between model parsimoniousness and computational complexity.
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| 3. |
- Postmus, Douwe, et al.
(author)
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A trial-based economic evaluation of 2 nurse-led disease management programs in heart failure
- 2011
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In: American Heart Journal. - : Elsevier. - 0002-8703 .- 1097-6744. ; 162:6, s. 1096-1104
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Journal article (peer-reviewed)abstract
- Background Although previously conducted meta-analyses suggest that nurse-led disease management programs in heart failure (HF) can improve patient outcomes, uncertainty regarding the cost-effectiveness of such programs remains. Methods To compare the relative merits of 2 variants of a nurse-led disease management program (basic or intensive support by a nurse specialized in the management of patients with HF) against care as usual (routine follow-up by a cardiologist), a trial-based economic evaluation was conducted alongside the COACH study. Results In terms of costs per life-year, basic support was found to dominate care as usual, whereas the incremental cost-effectiveness ratio between intensive support and basic support was found to be equal to (sic)532,762 per life-year; in terms of costs per quality-adjusted life-year (QALY), basic support was found to dominate both care as usual and intensive support. An assessment of the uncertainty surrounding these findings showed that, at a threshold value of (sic)20,000 per life-year/(sic)20,000 per QALY, basic support was found to have a probability of 69/62% of being optimal against 17/30% and 14/8% for care as usual and intensive support, respectively. The results of our subgroup analysis suggest that a stratified approach based on offering basic support to patients with mild to moderate HF and intensive support to patients with severe HF would be optimal if the willingness-to-pay threshold exceeds (sic)45,345 per life-year/(sic)59,289 per QALY. Conclusions Although the differences in costs and effects among the 3 study groups were not statistically significant, from a decision-making perspective, basic support still had a relatively large probability of generating the highest health outcomes at the lowest costs. Our results also substantiated that a stratified approach based on offering basic support to patients with mild to moderate HF and intensive support to patients with severe HF could further improve health outcomes at slightly higher costs.
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| 4. |
- Postmus, Douwe, et al.
(author)
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The COACH risk engine : a multistate model for predicting survival and hospitalization in patients with heart failure
- 2012
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In: European Journal of Heart Failure. - : Oxford University Press (OUP): Policy B. - 1388-9842 .- 1879-0844. ; 14:2, s. 168-175
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Journal article (peer-reviewed)abstract
- Aims Several models for predicting the prognosis of heart failure (HF) patients have been developed, but all of them focus on a single outcome variable, such as all-cause mortality. The purpose of this study was to develop a multistate model for simultaneously predicting survival and HF-related hospitalization in patients discharged alive from hospital after recovery from acute HF. less thanbrgreater than less thanbrgreater thanMethods and results The model was derived in the COACH (Coordinating Study Evaluating Outcomes of Advising and Counseling in Heart Failure) cohort, a multicentre, randomized controlled trial in which 1023 patients were enrolled after hospitalization because of HF. External validation was attained with the FINN-AKVA (Finish Acute Heart Failure Study) cohort, a prospective, multicentre study with 620 patients hospitalized due to acute HF. The observed vs. predicted 18-month survival was 72.1% vs. 72.3% in the derivation cohort and 71.4% vs. 71.2% in the validation cohort. The corresponding values of the c statistic were 0.733 [95% confidence interval (CI) 0.705-0.761] and 0.702 (95% CI 0.663-0.744), respectively. The models accuracy in predicting HF hospitalization was excellent, with predicted values that closely resembled the values observed in the derivation cohort. less thanbrgreater than less thanbrgreater thanConclusion The COACH risk engine accurately predicted survival and various measures of recurrent hospitalization in (acute) HF patients. It may therefore become a valuable tool in improving and personalizing patient care and optimizing the use of scarce healthcare resources.
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