Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers
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PRACTICAL APPLICATION
Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers Risha Gidwani1,2,3 · Louise B. Russell4,5
© This is a U.S government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2020, corrected publication 2020
Abstract This tutorial presents practical guidance on transforming various typesof information published in journals, or available online from government and other sources, into transition probabilities for use in state-transition models, including cost-effectiveness models. Much, but not all, of the guidance has been previously published in peer-reviewed journals. Our purpose is to collect it in one location to serve as a stand-alone resource for decision modelers who draw most or all of their information from the published literature. Our focus is on the technical aspects of manipulating data to derive transition probabilities. We explain how to derive model transition probabilities from the following types of statistics: relative risks, odds, odds ratios, and rates. We then review the well-known approach for converting probabilities to match the model’s cycle length when there are two health-state transitions and how to handle the case of three or more health-state transitions, for which the twostate approach is not appropriate. Other topics discussed include transition probabilities for population subgroups, issues to keep in mind when using data from different sources in the derivation process, and sensitivity analyses, including the use of sensitivity analysis to allocate analyst effort in refining transition probabilities and ways to handle sources of uncertainty that are not routinely formalized in models. The paper concludes with recommendations to help modelers make the best use of the published literature.
1 Introduction A set of health states, or events, and the probabilities of transitioning from onestate to others during a specified period of time (“transition probabilities”) are the fundamental building blocks of decision models. A state-transition model to evaluate cancer interventions, for example, might start with the Cancer-Free state and proceed through Local, Regional, and * Risha Gidwani [email protected] 1
Department of Health Management and Policy, UCLA School of Public Health, Los Angeles, CA, USA
2
Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, CA, USA
3
Center for Innovation To Implementation, VA Palo Alto Health Care System, Menlo Park, CA, USA
4
Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
5
Center for Health Incentives and Behavioral Economics and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, USA
Metastatic disease to Death. Transition probabilities would describe the probabilities of moving from Cancer-Free to Local Cancer, from Local to Regional, from Re
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