Dealing with Time in Health Economic Evaluation: Methodological Issues and Recommendations for Practice
PharmacoEconomics, ISSN: 1179-2027, Vol: 33, Issue: 12, Page: 1255-1268
2015
- 61Citations
- 197Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations61
- Citation Indexes59
- 59
- CrossRef55
- Policy Citations2
- Policy Citation2
- Captures197
- Readers197
- 197
Article Description
Time is an important aspect of health economic evaluation, as the timing and duration of clinical events, healthcare interventions and their consequences all affect estimated costs and effects. These issues should be reflected in the design of health economic models. This article considers three important aspects of time in modelling: (1) which cohorts to simulate and how far into the future to extend the analysis; (2) the simulation of time, including the difference between discrete-time and continuous-time models, cycle lengths, and converting rates and probabilities; and (3) discounting future costs and effects to their present values. We provide a methodological overview of these issues and make recommendations to help inform both the conduct of cost-effectiveness analyses and the interpretation of their results. For choosing which cohorts to simulate and how many, we suggest analysts carefully assess potential reasons for variation in cost effectiveness between cohorts and the feasibility of subgroup-specific recommendations. For the simulation of time, we recommend using short cycles or continuous-time models to avoid biases and the need for half-cycle corrections, and provide advice on the correct conversion of transition probabilities in state transition models. Finally, for discounting, analysts should not only follow current guidance and report how discounting was conducted, especially in the case of differential discounting, but also seek to develop an understanding of its rationale. Our overall recommendations are that analysts explicitly state and justify their modelling choices regarding time and consider how alternative choices may impact on results.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84948383369&origin=inward; http://dx.doi.org/10.1007/s40273-015-0309-4; http://www.ncbi.nlm.nih.gov/pubmed/26105525; http://link.springer.com/10.1007/s40273-015-0309-4; https://dx.doi.org/10.1007/s40273-015-0309-4; https://link.springer.com/article/10.1007/s40273-015-0309-4; https://link.springer.com/content/pdf/10.1007%2Fs40273-015-0309-4.pdf; http://link.springer.com/article/10.1007%2Fs40273-015-0309-4
Springer Science and Business Media LLC
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