scholarly journals Optimal threshold estimator of a prognostic marker by maximizing a time-dependent expected utility function for a patient-centered stratified medicine

2016 ◽  
Vol 27 (6) ◽  
pp. 1847-1859 ◽  
Author(s):  
Etienne Dantan ◽  
Yohann Foucher ◽  
Marine Lorent ◽  
Magali Giral ◽  
Philippe Tessier
Keyword(s):  
Utility Function ◽  
Kidney Transplant ◽  
Expected Utility ◽  
Time Dependent ◽  
Optimal Threshold ◽  
Stratified Medicine ◽  
Quality Adjusted Life Years ◽  
Patient Centered ◽  
Life Years ◽  
Quality Adjusted Life

Defining thresholds of prognostic markers is essential for stratified medicine. Such thresholds are mostly estimated from purely statistical measures regardless of patient preferences potentially leading to unacceptable medical decisions. Quality-Adjusted Life-Years are a widely used preferences-based measure of health outcomes. We develop a time-dependent Quality-Adjusted Life-Years-based expected utility function for censored data that should be maximized to estimate an optimal threshold. We performed a simulation study to compare estimated thresholds when using the proposed expected utility approach and purely statistical estimators. Two applications illustrate the usefulness of the proposed methodology which was implemented in the R package ROCt ( www.divat.fr ). First, by reanalysing data of a randomized clinical trial comparing the efficacy of prednisone vs. placebo in patients with chronic liver cirrhosis, we demonstrate the utility of treating patients with a prothrombin level higher than 89%. Second, we reanalyze the data of an observational cohort of kidney transplant recipients: we conclude to the uselessness of the Kidney Transplant Failure Score to adapt the frequency of clinical visits. Applying such a patient-centered methodology may improve future transfer of novel prognostic scoring systems or markers in clinical practice.

The Ranking Properties of Healthy-Years Equivalents and Quality-Adjusted Life-Years Under Certainty and Uncertainty

1995 ◽  
Vol 11 (1) ◽  
pp. 40-48 ◽  
Author(s):  
Magnus Johannesson
Keyword(s):  
Expected Utility ◽  
The Other ◽  
Quality Adjusted Life Years ◽  
Health States ◽  
Trade Off ◽  
Individual Preferences ◽  
Life Years ◽  
Ex Ante ◽  
Quality Adjusted Life ◽  
Health Profiles

AbstractThis paper investigates the theoretical properties of healthy-years equivalents (HYEs) and quality-adjusted life-years (QALYs). A distinction is made between ex ante HYEs (EA-HYEs) and expected HYEs (EXP-HYEs) and between risk-neutral quality-adjusted life-years (RN-QALYs) and risk-adjusted quality adjusted life-years (RA-QALYs). In the case of certainty, HYEs always rank health profiles according to individual preferences, whereas QALYs only rank health profiles according to individual preferences if constant proportional trade-off holds for all health states and if additive independence of quality in different periods holds. In the case of uncertainty, EA-HYEs always rank risky health profiles the same way as expected utility. The assumptions needed for the other measures to rank risky health profiles the same way as expected utility are: risk neutrality with respect to healthy time for EXP-HYEs; risk neutrality with respect to time in all health states and additive independence of quality in different periods for RN-HYEs; and constant proportional risk posture with respect to time in all health states and additive independence of quality in different periods for RA-QALYs.

Identifying Public Health Problems

2018 ◽  
Author(s):  
Scott Burris ◽  
Micah L. Berman ◽  
Matthew Penn, and ◽  
Tara Ramanathan Holiday
Keyword(s):  
Public Health ◽  
Health Problems ◽  
Health Priorities ◽  
Disability Adjusted Life Years ◽  
Quality Adjusted Life Years ◽  
Disability Adjusted Life ◽  
Life Years ◽  
Study Designs ◽  
Quality Adjusted Life

Chapter 5 discusses the use of epidemiology to identify the source of public health problems and inform policymaking. It uses a case study to illustrate how researchers, policymakers, and practitioners detect diseases, identify their sources, determine the extent of an outbreak, and prevent new infections. The chapter also defines key measures in epidemiology that can indicate public health priorities, including morbidity and mortality, years of potential life lost, and measures of lifetime impacts, including disability-adjusted life years and quality-adjusted life years. Finally, the chapter reviews epidemiological study designs, differentiating between experimental and observational studies, to show how to interpret data and identify limitations.

Medicare’s Hospital Value-Based Purchasing Program Values Quality over QALYs

2021 ◽  
pp. 0272989X2110171
Author(s):  
Edward C. Norton ◽  
Jun Li ◽  
Anup Das ◽  
Andrew M. Ryan ◽  
Lena M. Chen
Keyword(s):  
Quality Of Care ◽  
Pay For Performance ◽  
Health Interventions ◽  
Quality Adjusted Life Years ◽  
Value Based Purchasing ◽  
Quality Performance ◽  
Life Years ◽  
Policy Adjustments ◽  
Quality Adjusted Life

Medicare’s Hospital Value-Based Purchasing Program (HVBP) is the first national pay-for-performance program to combine measures of quality of care with a measure of episode spending. We estimated the implicit tradeoffs between mortality reduction and spending reduction. To earn points in HVBP, a hospital can either lower mortality or reduce spending, creating a tradeoff between the 2 measures. We analyzed the quality performance and earned points of 2814 hospitals using publicly available data. We then quantified the tradeoffs between spending and mortality in terms of quality-adjusted life-years (QALYs). If incentives in the program were balanced, then the tradeoff between spending and QALYs should be comparable with those of high-value health interventions, roughly $50,000 to $200,000 per QALY. Instead, the tradeoff in HVBP was about $1.2 million per QALY. HVBP overvalues improvements in quality of care relative to spending reductions. We propose 2 possible policy adjustments that could improve incentives for hospitals to deliver high-value care.

Good, Fairness and QALYs

1988 ◽  
Vol 23 ◽  
pp. 57-73 ◽  
Author(s):  
John Broome
Keyword(s):  
Health Service ◽  
Quality Adjusted Life Years ◽  
Life Years ◽  
Quality Adjusted Life

Counting QALYs (quality adjusted life years) has been proposed as a way of deciding how resources should be distributed in the health service: put resources where they will produce the most QALYs. This proposal has encountered strong opposition. There has been a disagreement between some economists favouring QALYs and some philosophers opposing them. But the argument has, I think, mostly been at cross-purposes. Those in favour of QALYs point out what they can do, and those against point out what they can't. There need be no disagreement about this. What is needed is to sort out what is the proper domain of QALYs, and it may be possible to do this amicably. Then we may be able to get on with the more useful job of deciding how well QALYs perform within their domain. In this paper I shall try to accomplish the first task (sections II–IV), and make a start on the second (sections V–VIII).

Sign in / Sign up

Export Citation Format

Share Document