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Proceedings of the AMIA Annual Fall Symposium logoLink to Proceedings of the AMIA Annual Fall Symposium
. 1996:992–996.

Computer-assisted quality of life assessment for clinical trials.

L A Lenert 1, J C Hornberger 1
PMCID: PMC2233008  PMID: 8947779

Abstract

A patient's quality of life is difficult to assess. Most methods designed to evaluate quality of life for clinical trials are time consuming, subject to varied interpretation among patients and assessors, and apply scales with only a limited relationship to utility theory. We have developed a HyperCard program, incorporating animated graphics, to (1) improve the speed of collection and collation of data, (2) improve understanding between patients and assessors, and (3) incorporate utility theory-based assessments. We assessed the quality of life of 25 patients with end-stage renal disease receiving hemodialysis using a traditional paper-based presentation and, eight months later, repeated the assessment with a computer-assisted presentation. Each presentation incorporated five techniques for evaluating quality of life: the Campbell Index of Well-being, the Kaplan-Bush Index of Well-being, categorical scaling, standard gamble, and time tradeoff. The computer program improved the speed of collation of data for statistical analysis, provided a consistent interface among patients for utility assessments, and showed stable reporting of patient's well-being. These findings suggest that further development of computer-assisted assessments of patients' quality of life for clinical trials is warranted.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Bursztajn H., Hamm R. M. The clinical utility of utility assessment. Med Decis Making. 1982;2(2):161–165. doi: 10.1177/0272989X8200200207. [DOI] [PubMed] [Google Scholar]
  2. Churchill D. N., Torrance G. W., Taylor D. W., Barnes C. C., Ludwin D., Shimizu A., Smith E. K. Measurement of quality of life in end-stage renal disease: the time trade-off approach. Clin Invest Med. 1987 Jan;10(1):14–20. [PubMed] [Google Scholar]
  3. Evans R. W., Rader B., Manninen D. L. The quality of life of hemodialysis recipients treated with recombinant human erythropoietin. Cooperative Multicenter EPO Clinical Trial Group. JAMA. 1990 Feb 9;263(6):825–830. [PubMed] [Google Scholar]
  4. Hornberger J. C., Redelmeier D. A., Petersen J. Variability among methods to assess patients' well-being and consequent effect on a cost-effectiveness analysis. J Clin Epidemiol. 1992 May;45(5):505–512. doi: 10.1016/0895-4356(92)90099-9. [DOI] [PubMed] [Google Scholar]
  5. Read J. L., Quinn R. J., Berwick D. M., Fineberg H. V., Weinstein M. C. Preferences for health outcomes. Comparison of assessment methods. Med Decis Making. 1984;4(3):315–329. doi: 10.1177/0272989X8400400307. [DOI] [PubMed] [Google Scholar]
  6. Torrance G. W. Toward a utility theory foundation for health status index models. Health Serv Res. 1976 Winter;11(4):349–369. [PMC free article] [PubMed] [Google Scholar]
  7. Tversky A., Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science. 1974 Sep 27;185(4157):1124–1131. doi: 10.1126/science.185.4157.1124. [DOI] [PubMed] [Google Scholar]
  8. Tversky A., Kahneman D. The framing of decisions and the psychology of choice. Science. 1981 Jan 30;211(4481):453–458. doi: 10.1126/science.7455683. [DOI] [PubMed] [Google Scholar]

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