Using Survival Analysis to Improve Estimates of Life Year Gains in Policy Evaluations Rachel Meacock, MSc, Matt Sutton, PhD, Søren Rud Kristensen, PhD, Mark Harrison, PhD Background. Policy evaluations taking a lifetime horizon have converted estimated changes in short-term mortality to expected life year gains using general population life expectancy. However, the life expectancy of the affected patients may differ from the general population. In trials, survival models are commonly used to extrapolate life year gains. The objective was to demonstrate the feasibil- ity and materiality of using parametric survival models to extrapolate future survival in health care policy evalua- tions. Methods. We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivat- ing example. We first used the cohort of patients admit- ted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a dif- ference-in-differences framework to estimate the life year gains associated with the program using general popula- tion life expectancy and survival models. Patient-level data from Hospital Episode Statistics was utilized for patients admitted to hospitals in England for pneumonia between 1 April 2007 and 31 March 2008 and between 1 April 2009 and 31 March 2010, and linked to death records for the period from 1 April 2007 to 31 March 2011. Results. In our cohort of patients, using parametric survival models rather than general population life expectancy figures reduced the estimated mean life years remaining by 30% (9.19 v. 13.15 years, respectively). However, the estimated mean life year gains associated with the program are larger using survival models (0.380 years) compared to using general population life expec- tancy (0.154 years). Conclusions. Using general popula- tion life expectancy to estimate the impact of health care policies can overestimate life expectancy but underesti- mate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of esti- mated survival and program impact considerably. Key words: survival analysis; economics (health); cost- effectiveness analysis; pay for performance. (Med Decis Making XXXX;XX:xx–xx) T he effects of health care policies and programs should be evaluated in terms of their impact on health outcomes, as is now standard practice for all new health care technologies. This impact can be composed of effects on both the quality and length of life. Length of life is a key outcome for cost- effectiveness analysis, either in isolation when calculating costs per life years gained or when com- bined with quality of life experienced in these years to estimate quality-adjusted life years (QALYs). This is the approach favored by governmental agen- cies in a number of countries including the UK, Canada, Australia, the Netherlands, and Sweden. 1–3 In this article, we focus on the methodology for esti- mating the impact on length of life. As full survival data are rarely available, the eva- luation problem faced can be broken down into 2 key aspects: estimating the effect of the policy on mortality, and evaluating the long-term gains in life years associated with this effect on mortality. 4–6 Policy evaluations attempting to take a lifetime hor- izon can use administrative data sets to estimate changes in short-term mortality and subsequently convert these to projected gains in life years using published estimates of life expectancy for the gen- eral population. Examples include measuring National Health Service (NHS) productivity, 7,8 esti- mating the National Institute for Health and Care Excellence (NICE) decision threshold, 9 and analyz- ing the cost-effectiveness of pay-for-performance programs. 10 The approach taken in previous work has been to estimate the impact of a program in terms of changes in the probability of mortality within 30 days, assessed as a binary outcome. 7,8,10 Estimated Ó The Author(s) 2016 Reprints and permission: http://www.sagepub.com/journalsPermissions.nav DOI: 10.1177/0272989X16654444 ORIGINAL ARTICLE MEDICAL DECISION MAKING/MON–MON XXXX 1