Optimizing sampling strategies for estimating quality-adjusted life years

Scott D. Ramsey, Ruth Etzioni, Andrea Troxel, Nicole Urban

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Accurate estimation of quality of life is critical to cost- effectiveness analysis. Nevertheless, development of sampling algorithms to maximize the accuracy and efficiency of estimated quality of life has received little consideration to date. This paper presents a method to optimize sampling strategies for estimating quality-adjusted life years. In particular, the authors address the questions of when to sample and how many observations to sample at each sampling time, assuming realistically that the sample variance of quality of life is not constant over time. The method is particularly useful for the design problems researchers face when time or research budget constraints limit the number of individuals that can be surveyed to estimate quality of life. The article focuses on cross-sectional sampling. The method proposed requires some knowledge of survival in the population of interest, the approximate variances in utilities at various points along the curve, and the general shape of the quality-adjusted survival curve. Such data are frequently available from disease registries, the literature, or previous studies.

Original languageEnglish (US)
Pages (from-to)431-438
Number of pages8
JournalMedical Decision Making
Volume17
Issue number4
DOIs
StatePublished - Oct 27 1997

Keywords

  • Cost-effectiveness
  • Cross-sectional sampling
  • Health-related quality of life
  • Quality-adjusted life years
  • Sampling
  • Survival
  • Utility
  • Variance

ASJC Scopus subject areas

  • Health Policy

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