Using quality of life measures in the clinical setting
BMJ 2001; 322 doi: https://doi.org/10.1136/bmj.322.7297.1297 (Published 26 May 2001) Cite this as: BMJ 2001;322:1297
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EDITOR -- In their sensible paper, Higginson and Carr list 8
potential uses of quality of life measures,[1] however, the authors
overlooked the prognostic role of quality of life. Indeed, quality of life
scores do not only retain a value as end points, rather they may be
important predictors of survival, especially in advanced cancer patients:
pre-treatment quality of life has been shown to correlate with survival in
studies of lung, breast cancer and colorectal cancer, multiple myeloma,
malignant melanoma, rheumatoid arthritis.[2-5] Preoperative quality of
life was also a predictive factor of survival after heart surgery.[6]
The reasons for the association between quality of life and survival are
still uncertain. Some authors believe that the biology of the disease
predominates over psychosocial variables, thus quality of life issues may
complete only inaccurate prognostic profiles. Some other authors suspect
that selected quality of life domains, such as pain, might reflect
patients' perception of disease severity and thus herald advancing
disease. At last, a true causal relationship might link quality of life
and psychological well-being to survival.
Was there a causal relationship between quality of life and outcome, one
would expect that also in early stages of cancer this would hold.[5]
Nevertheless, a larger variation of quality of life is likely to exist in
advanced cancer patients with respect to early cancer ones, therefore,
quality of life of the latter patients would hardly be included into the
prognostic profile. Thus validation of the prognostic value of quality of
life is hard.[5] Unluckily neither the prognostic nor the other proposed
uses of quality of life were fully validated,[1,7] consequently the
additional information about quality of life still does not alter clinical
decisions.
We fear that a long time will pass before a practical application of
quality of life tools unless a cooperative effort to conduct specific
studies is hastened.
Monia Marchetti,
research fellow
Giovanni Barosi,
head
Laboratory of Medical Informatics, IRRCS Policlinico San Matteo. Pavia.
Italy.
1. Higginson IJ, Carr AJ. Using quality of life measures in the
clinical setting. BMJ 2001;322:1297-300
2. Montazeri A, Milroy R, Hole D, McEwen J, Gills CR. Quality of life in
lung cancer patients as an important prognostic factor. Lung Cancer
2001;31:233-240.
3. Coates A, Thomson T, Mc Leod GR et al. Prognostic value of quality of
life scores in a trial of chemotherapy with or without interferon in
patients with metastatic melanoma. Eur J Cancer 1993;29A:1731-1734.
4. Earlam S, Glover C, Fordy C, Burke D. Allen-Mersh TG. Relationship
between tumor size, quality of life and survival in patients with
colorectal liver metastases. J Clin Oncol 1996;14:171-175.
5. Coates AS, Hurny C, Peterson HF, Bernhard J, Castiglione-Gertsch M,
Gelber RD, et al. Quality of life scores predict outcome in metastatic but
not early breast cancer. JCO 2000;18:3768-3774.
6. Chocron S, Etievent JP, Viel JF, Dussaucy A, Clement F, Kaili D, Yan Y.
Preoperative quality of life as a predictive factor of 3-year survival
after open heart operations. Ann Thorac Surg 2000;69:722-727.
7. Martin AJ, Stocker M. Quality-of-life assessment in health care
research and practice. Eval Health Prof 1998;21:141-156.
Competing interests: No competing interests
Although we read with interest the article by Higginson and Carr [1],
we see a serious cause for concern in what it seems to be the perpetuation
of the belief that responsiveness is an independent property needed by
quality of life (QoL) measures used in clinical practice.
To our knowledge, this credence has its roots in a non-proved
assumption stated in the 80’s by a group of authors from the McMaster
University [2,3]. The group mainly sustained the responsiveness definition
in a three-type compartmentation of QoL tests: discriminative (used to
distinguish between individuals), predictive, or evaluative (used to
measure longitudinal change) [2]. While demonstration of reliability and
validity would be sufficient for concluding that an instrument is useful
for description or prediction, instrument’s responsiveness (or the ability
to detect important clinical changes) was assumed to be necessary for
evaluation [2,3]. Despite being mere speculation, this opinion has become
very popular in the medical literature.
Setting aside this tripartite framework has been previously
criticised because of its oversimplified nature [4], we would like to go a
step further and put in doubt the entire responsiveness thesis.
According to principles of test theories [5], the responsiveness
assessment is nothing more than a standard validation procedure embedded
in a longitudinal design.
Validity is an overall evaluative judgement of the degree to which
empirical evidence and theoretical rationales support the adequacy of
interpretations on the basis of test scores. Conventional approaches to
validate a test are based on studies of expected performance differences
over time, across groups and settings, and in response to experimental
treatments and manipulations [6].
There are many sources of invalidity [5]. In our case is important to
be aware of the fact that markedly skewed distributions of scores (floor
effects referred by Higginson and Carr on the BMJ’s website), jeopardise
the validity of any test interpretation without regard of the research
study design (cross-sectional or longitudinal).
Imagine that you have a thermometer that only measures from 100.4 to
122 degrees F and some patients suffering fever from 102.2 to 104. You
will be capable of discriminating temperatures between subjects, but
unable to detect “clinically” important individual changes if all of them
return to a “normal” temperature around 96.8. It could be said that the
instrument was “valid” for discrimination but not “responsive” to detect
clinically significant longitudinal differences. If patients had moved
their temperature within the limits of the thermometer range, the
instrument would have been responsive to changes. The truth is that the
instrument is ONLY “valid” (and by extension “responsive”) in the range
from 100.4 to 122, but completely “invalid” (and by extension
“unresponsive”) below 100.4.
Responsiveness is no more than a new name for an old friend.
Prieto L1, Sacristan JA1, Casado A2, Gómez JC1
1. Spanish Group for the Study of Methodology in Clinical Research.
Madrid, Spain.
2. Iberoamerican Cochrane Centre. Barcelona, Spain.
References
1. Higginson IJ, Carr AJ. Using quality of life measures in the
clinical setting. BMJ 2001; 322: 1297-300.
2. Kirshner B, Guyatt G. A methodological framework for assessing health
indices. J Chron Dis 1985; 38: 27-36.
3. Guyatt G, Walter S, Norman G. Measuring change over time: assessing the
usefulness of evaluative instruments J Clin Epidemiol 1987; 40: 171-8.
4. Williams JI, Naylor CD. How should health status measures be assessed?
Cautionary notes on Procrustean frameworks. J Clin Epidemiol 1992; 42:
1347-51
5. Nunnally JC, Bernstein IH. Psychometric theory (3rd ed.). New York:
McGraw-Hill, 1994.
6. Messick S. Validity of psychological assessment: validation of
inferences from persons' responses and performances as scientific inquiry
into score meaning. Am Psychologist 1995; 50: 741-9.
Competing interests: No competing interests
Quality of life measures in oncology practice-getting some early answers?
Dear Editor,
We read with interest the comprehensive article by Higginson and Carr (1).
We feel that in oncology, there is accumulating evidence suggesting
positive answers to some questions posed in the paper, that is whether
standard quality of life measures can be used in individual patients and
whether they will have any impact on the process of patient care.
We used a standard cancer-specific questionnaire (EORTC QLQ-C30),
administered via computer touch-screen system, in oncology clinics and
showed that individual results corresponded well to the patient medical
records, symptoms, toxicity from treatment and overall disease course (2).
Our results suggested that this quality of life questionnaire could be
used to measure symptoms and functioning, and monitor individual patients
over time. In addition, the quality of life results supplemented available
information on patient functioning, emotional distress and non-specific
symptoms (fatigue, insomnia), thus suggesting a potential for alerting
medical professionals to a wider range of problems. This potential was
confirmed when the same questionnaire was used in oncology practice in two
different studies (3,4). When clinicians were informed of their patients'
quality of life results, more issues and problems were discussed during
the subsequent clinical encounter, suggesting a positive influence on
doctor-patient communication. As pointed out by Higginson and Carr, there
is still no evidence of an impact on patient outcomes in terms of
satisfaction with care and improved quality of life, but these were single
interventions at one point of time. Patient benefits are more likely to
be seen with routine, regular use of quality of life measures overtime
during treatment and follow-up. Such longitudinal studies are being
performed in oncology and their results are eagerly awaited.
The interpretation of quality of life results of individual cancer
patients remains a challenge. Some data is available to suggest what might
constitute a clinically important difference, but this is more informative
for comparisons of groups than individuals (5). We adopted an approach
where quality of life scores were presented in a clear graphic format
together with normative data (2). In this format each patient served as
his /her own control and changes over time were easy to identify.
Clinicians were trained to understand the severity of the problems
suggested by the different scores and were informed that the numerical
scores had a certain degree of error. Thus any suspected problems from
the quality of life measure should serve only as an indication for a
subsequent clinical assessment and should aid, rather than replace doctor-
patient interactions.
References
1. Higginson IJ, Carr AJ. Using quality of life measures in the
clinical setting. BMJ 2001; 322(7297):1297-1300.
2. Velikova G, Wright P, Smith AB, Stark D, Perren T, Brown J et al.
Self-reported quality of life of individual cancer patients: concordance
of results with disease course and medical records. J Clin Oncol 2001;
19(7):2064-2073.
3. Detmar SB, Aaronson NK. Quality of life assessment in daily
clinical oncology practice: a feasibility study. Eur J Cancer 1998; 34
(8):1181-1186..
4. Taenzer P, Bultz BD, Carlson LE, Speca M, DeGagne T, Olson K et
al. Impact of computerized quality of life screening on physician
behaviour and patient satisfaction in lung cancer outpatients. Psychooncol
2000; 9(3):203-213.
5. Osoba D, Rodrigues G, Myles J, Zee B, Pater J. Interpreting the
significance of changes in health-related quality-of-life scores. J Clin
Oncol 1998; 16(1):139-144.
Corresponding author:
Dr Galina Velikova
ICRF Clinical Research Fellow / Honorary Specialist Registrar
ICRF Clinical Centre, Cancer Medicine Research Unit, St James's Hospital,
Leeds LS9 7TF
e-mail: csjgv@leeds.ac.uk
A.B. Smith
Senior Scientific Officer
ICRF Clinical Centre, Cancer Medicine Research Unit, St James's Hospital,
Leeds LS9 7TF
E.P. Wright
Senior Scientific Officer
ICRF Clinical Centre, Cancer Medicine Research Unit, St James's Hospital,
Leeds LS9 7TF
Prof. P. Selby
Professor of Cancer Medicine
Director of ICRF Clinical Centre, Cancer Medicine Research Unit, St
James's Hospital, Leeds LS9 7TF
No competing interests.
the paper journal.
Competing interests: No competing interests