title: Dealing with Time in Health Economic Evaluation: Methodological Issues and Recommendations for Practice.
abstract: 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.
authors.affiliation: Department of Health Policy and Management, School of Medicine, Trinity College Dublin, Dublin, Ireland. jfomahon@tcd.ie.
authors.forename: James F
authors.initials: JF
authors.lastname: O'Mahony
authors.affiliation: School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, Australia. a.newall@unsw.edu.au.
authors.forename: Anthony T
authors.initials: AT
authors.lastname: Newall
authors.affiliation: Department of Biostatistics, Erasmus MC, PO Box 2040, 3000 CA, Rotterdam, The Netherlands. j.vanrosmalen@erasmusmc.nl.
authors.forename: Joost
authors.initials: J
authors.lastname: van Rosmalen
mesh_terms: D000367:Age Factors; D015331:Cohort Studies; D003198:Computer Simulation; D003365:Costs and Cost Analysis / Q000379:methods*; D003661:Decision Support Techniques; D003695:Delivery of Health Care / Q000191:economics* / Q000379:methods / Q000706:statistics & numerical data; D004470:Economics, Medical / Q000706:statistics & numerical data*; D006801:Humans; D018803:Models, Economic; D013997:Time Factors