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Methods for quality-of-life studies

M A Testa1, J F Nackley

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.

Annual Review of Public Health
|January 1, 1994
PubMed
Summary

This study reviews methods for measuring patient quality of life in health research. It emphasizes robust study design and statistical principles for reliable and sensitive outcome assessment in clinical trials and health economics.

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Psychometrics

Background:

  • Quality of life (QoL) is a multidimensional construct requiring diverse research methods for measurement.
  • Conceptualization of QoL influences measurement complexity and study design.
  • Reliable measurement of unobservable constructs necessitates established methods in tests and measurement, survey research, psychometrics, and sociometrics.

Purpose of the Study:

  • To outline methodologies for assessing patient quality-of-life outcomes in health research.
  • To highlight the importance of psychometric properties and statistical principles in QoL measurement.
  • To address the growing need for QoL data in pharmacoeconomic models.

Main Methods:

  • Review of established research methodologies in quality-of-life assessment.
  • Discussion of psychometric properties (stability, internal consistency, responsiveness, sensitivity) for QoL scales.
  • Emphasis on statistical and epidemiological principles for study design, including power, effect sizes, and longitudinal data analysis.

Main Results:

  • QoL measurement requires careful consideration of conceptualization, reporter variability, and administration methods.
  • Scale adequacy as a dependent variable depends on stability, internal consistency, responsiveness, and sensitivity.
  • Robust study design must account for statistical power, effect sizes, longitudinal data, and individual heterogeneity.

Conclusions:

  • Methodologies for QoL research should integrate principles from clinical research, epidemiology, biostatistics, economics, and behavioral science.
  • Accurate estimation of QoL parameters is crucial for pharmacoeconomic models and health-care reform.
  • Future development should build upon existing foundations to enhance QoL outcome assessment.

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