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Lessons learned from measuring health-related quality of life in oncology.

Osoba, D·1994·Journal of Clinical Oncology

Abstract

PURPOSE: Measurement of health-related quality of life (HQL) is becoming more common in oncology, particularly in clinical trials. The purpose of this report is to review our progress and assess whether we are learning anything new from these studies. DESIGN: The subject was reviewed by a literature search and selection of available literature in the English language. The results emerged from this review. RESULTS: Six lessons that emerged from measurement of HQL in oncology were chosen for emphasis. These are (1) HQL is a multidimensional construct and should be measured with multidimensional instruments, (2) observers are poor judges of how patients feel about their HQL, (3) it is possible to achieve high rates of compliance in the collection of self-report HQL data, (4) aggressive therapy may result in improved HQL, (5) symptoms are associated with quantifiable disruptions in HQL, and (6) pretreatment HQL may be predictive of on-treatment HQL and of survival. CONCLUSION: None of the six points chosen for emphasis was known with certainty as little as 10 years ago. Although other points for emphasis may be chosen, those emphasized in this review could have a major impact on how we judge success in future clinical trials.

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Metadata

Authors
Osoba, D.
Venue
Journal of Clinical Oncology
Published
1994-03
Volume
12
Issue
3
Pages
608-616
ISSN
  • 0732-183X (ISSN-L)
  • 1527-7755
OpenAlex
W2256770509
Cited by
381
References
0
FWCI
5.77
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Lessons learned from measuring health-related quality of life in oncology. https://app.cochat.ai/papers/10.1200/jco.1994.12.3.608
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Osoba, D. (1994). Lessons learned from measuring health-related quality of life in oncology. Journal of Clinical Oncology. https://doi.org/10.1200/jco.1994.12.3.608

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.