Research Article

Quality adjusted survival analysis with repeated quality of life measures

  1. Paul P. Glasziou1,*,
  2. Bernard F. Cole2,
  3. Richard D. Gelber3,
  4. Jorgen Hilden4,
  5. R. John Simes5

Article first published online: 4 DEC 1998

DOI: 10.1002/(SICI)1097-0258(19980615)17:11<1215::AID-SIM844>3.0.CO;2-Y

Issue

Statistics in Medicine

Statistics in Medicine

Volume 17, Issue 11, pages 1215–1229, 15 June 1998

Additional Information

How to Cite

Glasziou, P. P., Cole, B. F., Gelber, R. D., Hilden, J. and Simes, R. J. (1998), Quality adjusted survival analysis with repeated quality of life measures. Statistics in Medicine, 17: 1215–1229. doi: 10.1002/(SICI)1097-0258(19980615)17:11<1215::AID-SIM844>3.0.CO;2-Y

Author Information

  1. 1

    Department of Social & Preventive Medicine, University of Queensland Medical School, Herston, Qld 4006, Australia

  2. 2

    Center for Statistical Science, Department of Community Health, Brown University, Providence, RI 02912, U.S.A.

  3. 3

    Department of Biostatistics, Dana-Farber Cancer Institute, Harvard School of Public Health and Harvard Medical School, 44 Binney Street, Boston, MA 02115, U.S.A.

  4. 4

    Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, Copenhagen N, 2200, Denmark

  5. 5

    NHMRC Clinical Trials Centre, Building A27, University of Sydney, NSW 2006, Australia

*Department of Social & Preventive Medicine, University of Queensland Medical School, Herston, Qld 4006, Australia

Publication History

  1. Issue published online: 4 DEC 1998
  2. Article first published online: 4 DEC 1998
  3. Manuscript Accepted:
  4. Manuscript Received:

Funded by

  • American Cancer Society. Grant Number: PBR-53D

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Abstract

One way of examining trade-offs between quantity and quality of life (QOL) is to combine them into a single measure such as quality-adjusted life year (QALY). If censoring occurs, then estimation presents some difficulties. One approach, known as Q-TWiST, is to define a series of health states, use a ‘partitioned’ survival analysis to calculate the average time in each state, and then weight each state according to its quality of life to calculate QALYs. Such health-state models, however, are unhelpful when the transitions between health states are unclear or if they do not adequately reflect variations in quality of life. We therefore examine an alternative analysis to be used when repeated measures of quality of life are available from individual patients in a clinical trial. The method proceeds by separating quality of life and survival, that is, dQALY/dt=S(t)Q(t), where S(t) is the survival curve, estimated from the standard Kaplan–Meier method, and Q(t) is the quality of life function, derived from individual repeated measures of quality of life. We derive single health-state (QALY) and multiple health-state (Q-TWiST) models and illustrate the approach by comparing different durations of adjuvant chemotherapy for breast cancer. © 1998 John Wiley & Sons, Ltd.

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