Using Survival Analysis to Improve Estimates of Life Year Gains in Policy Evaluations

Rachel Meacock, Matt Sutton, Søren Rud Kristensen, Mark Harrison*

*Kontaktforfatter for dette arbejde

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    Resumé

    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 feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. Methods. We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population 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 expectancy (0.154 years). Conclusions. Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably.

    OriginalsprogEngelsk
    TidsskriftMedical Decision Making
    Vol/bind37
    Udgave nummer4
    Sider (fra-til)415-426
    Antal sider12
    ISSN0272-989X
    DOI
    StatusUdgivet - 1. jan. 2017

    Fingeraftryk

    Survival Analysis
    Life Expectancy
    Population
    Health Policy
    Incentive Reimbursement
    Delivery of Health Care
    Death Certificates
    England
    Cost-Benefit Analysis

    Citer dette

    Meacock, Rachel ; Sutton, Matt ; Kristensen, Søren Rud ; Harrison, Mark. / Using Survival Analysis to Improve Estimates of Life Year Gains in Policy Evaluations. I: Medical Decision Making. 2017 ; Bind 37, Nr. 4. s. 415-426.
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    abstract = "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 feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. Methods. We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population 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 expectancy (0.154 years). Conclusions. Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably.",
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    author = "Rachel Meacock and Matt Sutton and Kristensen, {S{\o}ren Rud} and Mark Harrison",
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    Using Survival Analysis to Improve Estimates of Life Year Gains in Policy Evaluations. / Meacock, Rachel; Sutton, Matt; Kristensen, Søren Rud; Harrison, Mark.

    I: Medical Decision Making, Bind 37, Nr. 4, 01.01.2017, s. 415-426.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - Using Survival Analysis to Improve Estimates of Life Year Gains in Policy Evaluations

    AU - Meacock, Rachel

    AU - Sutton, Matt

    AU - Kristensen, Søren Rud

    AU - Harrison, Mark

    PY - 2017/1/1

    Y1 - 2017/1/1

    N2 - 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 feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. Methods. We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population 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 expectancy (0.154 years). Conclusions. Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably.

    AB - 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 feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. Methods. We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population 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 expectancy (0.154 years). Conclusions. Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably.

    KW - cost-effectiveness analysis

    KW - economics (health)

    KW - pay for performance

    KW - survival analysis

    UR - http://www.scopus.com/inward/record.url?scp=85019105033&partnerID=8YFLogxK

    U2 - 10.1177/0272989X16654444

    DO - 10.1177/0272989X16654444

    M3 - Journal article

    VL - 37

    SP - 415

    EP - 426

    JO - Medical Decision Making

    JF - Medical Decision Making

    SN - 0272-989X

    IS - 4

    ER -