Estimation of mean quality adjusted survival time
โ Scribed by L. Z. Shen; E. Pulkstenis; M. Hoseyni
- Book ID
- 101238620
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 124 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
โฆ Synopsis
In clinical studies, we often consider not only patients' survival time, but also their quality of life. Quality adjusted life years (QALY) is an integrated measure of medical outcome that combines a patient's quantity and quality of life. Estimation of mean QALY for a group of patients is complicated by the fact that some patients are censored. The conventional approach is to obtain the Kaplan}Meier estimate of the survival function associated with individual QALYs and then use the area under the Kaplan}Meier curve as an estimate of mean QALY. Glasziou, Simes and Gelber showed that this method is biased because censoring at the nominal time scale is informative for predicting unobserved QALYs. In this paper, we propose a methodology for consistent estimation of mean QALY. Simulation studies are conducted to investigate the relative performance of the new method and the conventional method.
๐ SIMILAR VOLUMES
To compare both mortality and quality of life (QOL) across different illnesses, we propose an estimator to calculate the expected quality adjusted survival (QAS) by multiplying the QOL into the survival function. While the survival function can be determined by the usual life table method, the QOL d
The expected quality-adjusted survival (QAS) for an index population with a speci"c disease can be estimated by summing the product of the survival function and the mean quality of life function of the population. In many follow-up studies with heavy censoring, the expected QAS may not be well estim