Gains in Life Expectancy from Medical Interventions — Standardizing Data on Outcomes

Wright, Janice C.; Weinstein, Milton C.

doi:10.1056/nejm199808063390606

Cited by 314 publications

(176 citation statements)

References 39 publications

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“…For instance, we quantify the benefits of annual PSA screening. The results in Table 8 illustrate the benefits are significantly greater than the benefit per person of some well-known population-based prevention programs such as vaccination against measles and rubella, which has an estimated benefit per person of 0.008 QALYs (Wright and Weinstein 1998). Therefore, our results indicate that there is a potentially significant benefit from PSA screening policies based on the risk of prostate cancer relative to other public health interventions.…”

Section: Discussionmentioning

confidence: 80%

Optimization of Prostate Biopsy Referral Decisions

Zhang

Denton

Balasubramanian

et al. 2012

M&SOM

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Prostate cancer is the most common solid tumor in American men and is screened for using prostate-specific antigen (PSA) tests. We report on a non-stationary partially observable Markov decision process (POMDP) for prostate biopsy referral decisions. The core states are the patients' prostate cancer related health states, and PSA test results are the observations. Transition probabilities and rewards are inferred from the Mayo Clinic Radical Prostatectomy Registry (MCRPR) and the medical literature. The objective of our model is to maximize expected quality-adjusted life years (QALYs). We solve the POMDP model to obtain an age and belief (probability of having prostate cancer) dependent optimal biopsy referral policy. We also prove a number of structural properties including the existence of a control-limit type policy for the biopsy referral decision. Our empirical results demonstrate a non-decreasing belief threshold in age, and we provide sufficient conditions under which PSA screening should be discontinued for older patients. Finally, the benefits of screening under the optimal biopsy referral policy are estimated, and sensitivity analysis is used to prioritize the model parameters that would benefit from additional data collection.

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Section: Discussionmentioning

confidence: 80%

Optimization of Prostate Biopsy Referral Decisions

Zhang

Denton

Balasubramanian

et al. 2012

M&SOM

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“…The main purpose of the model is to estimate the likely occurrence of the major diabetes-related complications over a lifetime for patients with specified prognostic risk factors, in order to calculate health outcomes such as life expectancy or quality-adjusted life expectancy. While increases in mean life expectancy are widely recognised as a useful measure of benefit [26], it is also important to encapsulate the potential impact of complications on quality of life. This model uses the quality-adjusted life-years (QALY) method to adjust length of life for quality of life by assigning a value or health utility, on a scale on which 0 represents death and 1 represents full health, for each year of life.…”

Section: Methodsmentioning

confidence: 99%

A model to estimate the lifetime health outcomes of patients with Type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68)

et al. 2004

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Aims/hypothesis. The aim of this study was to develop a simulation model for Type 2 diabetes that can be used to estimate the likely occurrence of major diabetes-related complications over a lifetime, in order to calculate health economic outcomes such as qualityadjusted life expectancy. Methods. Equations for forecasting the occurrence of seven diabetes-related complications and death were estimated using data on 3642 patients from the United Kingdom Prospective Diabetes Study (UKPDS). After examining the internal validity, the UKPDS Outcomes Model was used to simulate the mean difference in expected quality-adjusted life years between the UKPDS regimens of intensive and conventional blood glucose control. Results. The model's forecasts fell within the 95% confidence interval for the occurrence of observed events during the UKPDS follow-up period. When the model was used to simulate event history over patients' lifetimes, those treated with a regimen of conventional glucose control could expect 16.35 undiscounted quality-adjusted life years, and those receiving treatment with intensive glucose control could expect 16.62 quality-adjusted life years, a difference of 0.27 (95% CI: −0.48 to 1.03). Conclusions/interpretations. The UKPDS OutcomesModel is able to simulate event histories that closely match observed outcomes in the UKPDS and that can be extrapolated over patients' lifetimes. Its validity in estimating outcomes in other groups of patients, however, remains to be evaluated. The model allows simulation of a range of long-term outcomes, which should assist in informing future economic evaluations of interventions in Type 2 diabetes. The centres of the UKPDS are listed at the end of the paper Conflict of interest. Several authors (as indicated above) are employed by the University of Oxford. This paper describes and places in the public domain a simulation model that we have called the UKPDS Outcomes Model. All of the information necessary to reproduce the UKPDS Outcomes Model is provided in this article, but it is conceivable that a future user with a commercial interest in the UKPDS Outcomes Model might prefer to use the software already created by University programmers. Depending on the nature of the proposed use of the UKPDS Outcomes Model, the University of Oxford might charge a fee in this case.

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“…The convention adopted by medical economists is to measure clinical benefits as the number of added life-years (LYs). 4 This measure of benefits is most useful when the primary therapeutic goal is to prolong life expectancy. Because many therapies are used primarily to improve quality of life rather than to increase longevity, a broader measure of benefits is the quality-adjusted life-year (QALY).…”

Section: Analysis Methodsmentioning

confidence: 99%