Estimation of Quality-Adjusted Life Expectancy of Oral Cancer Patients: Integration of Lifetime Survival With Repeated Quality-of-Life Measurements

Chia Hua Chung, Tsuey Hwa Hu, Jung Der Wang, Jing Shiang Hwang

Research output: Contribution to journalArticle

Abstract

Background: Quality-adjusted life year is widely applied nowadays, which consider both survival and quality of life (QoL). When most diseases are becoming chronic, it is imperative to quantify the overall health impact of a disease in lifetime perspective. Objective: The purpose of this study is to introduce methods for estimating quality-adjusted life expectancy (QALE) and loss of QALE in patients with a disease or specific conditions. Methods: The QALE of an index cohort can be represented as the integration of the product of lifetime survival function and mean QoL function. We introduce a robust extrapolation approach for estimating lifetime survival function and propose an approach for estimating lifetime mean QoL function for studies with limited follow-up. The best part of the proposed method is that the survival data and QoL data can be collected separately. A cohort of patients with a specific condition can be identified by databases that regularly collect data for the control of diseases, and their survival status is verified by linking to a mortality registry. Although nationwide QoL data are not available, researchers can implement a relative short-term follow-up interview on a random sample of patients to collect QoL data. For demonstration, we applied the proposed methods to estimate QALE and loss of QALE of oral cancer patients. Results: The estimates (95% confidence interval) of QALE for oral cancer patients were 11.0 (10.5-11.6) and 14.2 (12.7-15.5) quality-adjusted life years (QALYs) for men and women, respectively. The estimates of loss of QALE for the male and female patients with oral cancer were 14.4 (13.8-14.9) and 7.5 (6.2-9.0) QALYs, respectively. Conclusions: The methods for estimating QALE and loss of QALE can be applied to economic evaluation of cancer control, including screening.

Original languageEnglish
Pages (from-to)59-65
Number of pages7
JournalValue in Health Regional Issues
Volume21
DOIs
Publication statusPublished - 2020 May

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Mouth Neoplasms
Life Expectancy
Quality of Life
Survival
Quality-Adjusted Life Years
Cancer
Life expectancy
Quality of life
Cost-Benefit Analysis
Registries

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance (miscellaneous)
  • Pharmacology, Toxicology and Pharmaceutics (miscellaneous)
  • Health Policy

Cite this

@article{c1755cef88f54463b9f6a03f61f211e6,
title = "Estimation of Quality-Adjusted Life Expectancy of Oral Cancer Patients: Integration of Lifetime Survival With Repeated Quality-of-Life Measurements",
abstract = "Background: Quality-adjusted life year is widely applied nowadays, which consider both survival and quality of life (QoL). When most diseases are becoming chronic, it is imperative to quantify the overall health impact of a disease in lifetime perspective. Objective: The purpose of this study is to introduce methods for estimating quality-adjusted life expectancy (QALE) and loss of QALE in patients with a disease or specific conditions. Methods: The QALE of an index cohort can be represented as the integration of the product of lifetime survival function and mean QoL function. We introduce a robust extrapolation approach for estimating lifetime survival function and propose an approach for estimating lifetime mean QoL function for studies with limited follow-up. The best part of the proposed method is that the survival data and QoL data can be collected separately. A cohort of patients with a specific condition can be identified by databases that regularly collect data for the control of diseases, and their survival status is verified by linking to a mortality registry. Although nationwide QoL data are not available, researchers can implement a relative short-term follow-up interview on a random sample of patients to collect QoL data. For demonstration, we applied the proposed methods to estimate QALE and loss of QALE of oral cancer patients. Results: The estimates (95{\%} confidence interval) of QALE for oral cancer patients were 11.0 (10.5-11.6) and 14.2 (12.7-15.5) quality-adjusted life years (QALYs) for men and women, respectively. The estimates of loss of QALE for the male and female patients with oral cancer were 14.4 (13.8-14.9) and 7.5 (6.2-9.0) QALYs, respectively. Conclusions: The methods for estimating QALE and loss of QALE can be applied to economic evaluation of cancer control, including screening.",
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Estimation of Quality-Adjusted Life Expectancy of Oral Cancer Patients : Integration of Lifetime Survival With Repeated Quality-of-Life Measurements. / Chung, Chia Hua; Hu, Tsuey Hwa; Wang, Jung Der; Hwang, Jing Shiang.

In: Value in Health Regional Issues, Vol. 21, 05.2020, p. 59-65.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Estimation of Quality-Adjusted Life Expectancy of Oral Cancer Patients

T2 - Integration of Lifetime Survival With Repeated Quality-of-Life Measurements

AU - Chung, Chia Hua

AU - Hu, Tsuey Hwa

AU - Wang, Jung Der

AU - Hwang, Jing Shiang

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AB - Background: Quality-adjusted life year is widely applied nowadays, which consider both survival and quality of life (QoL). When most diseases are becoming chronic, it is imperative to quantify the overall health impact of a disease in lifetime perspective. Objective: The purpose of this study is to introduce methods for estimating quality-adjusted life expectancy (QALE) and loss of QALE in patients with a disease or specific conditions. Methods: The QALE of an index cohort can be represented as the integration of the product of lifetime survival function and mean QoL function. We introduce a robust extrapolation approach for estimating lifetime survival function and propose an approach for estimating lifetime mean QoL function for studies with limited follow-up. The best part of the proposed method is that the survival data and QoL data can be collected separately. A cohort of patients with a specific condition can be identified by databases that regularly collect data for the control of diseases, and their survival status is verified by linking to a mortality registry. Although nationwide QoL data are not available, researchers can implement a relative short-term follow-up interview on a random sample of patients to collect QoL data. For demonstration, we applied the proposed methods to estimate QALE and loss of QALE of oral cancer patients. Results: The estimates (95% confidence interval) of QALE for oral cancer patients were 11.0 (10.5-11.6) and 14.2 (12.7-15.5) quality-adjusted life years (QALYs) for men and women, respectively. The estimates of loss of QALE for the male and female patients with oral cancer were 14.4 (13.8-14.9) and 7.5 (6.2-9.0) QALYs, respectively. Conclusions: The methods for estimating QALE and loss of QALE can be applied to economic evaluation of cancer control, including screening.

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