Causal Indicators in Quality of Life Research

Quality of Life (QOL) questionnaires contain two different types of items. Some items, such as assessments of symptoms of disease, may be called causal indicators because the occurrence of these symptoms can cause a change in QOL. A severe state of even a single symptom may suffice to cause impairme...

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Bibliographic Details
Published in:Quality of life research 1997-07, Vol.6 (5), p.393-406
Main Authors: Fayers, P. M., Hand, D. J., K. Bjordal, M. Groenvold
Format: Article
Language:English
Subjects:
Anxiety
Biological and medical sciences
Breast cancer
Breast Neoplasms - rehabilitation
Denmark
Depressive disorders
Diarrhea
Female
Head and Neck Neoplasms - rehabilitation
Humans
Medical sciences
Miscellaneous
Modeling
Models, Psychological
Norway
Outcome Assessment (Health Care)
Psychometrics
Public health. Hygiene
Public health. Hygiene-occupational medicine
Quality of Life
Questionnaires
Surveys and Questionnaires
Symptoms
Vomiting
Citations: Items that cite this one
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Summary:Quality of Life (QOL) questionnaires contain two different types of items. Some items, such as assessments of symptoms of disease, may be called causal indicators because the occurrence of these symptoms can cause a change in QOL. A severe state of even a single symptom may suffice to cause impairment of QOL, although a poor QOL need not necessarily imply that a patient suffers from all the symptoms. Other items, for example anxiety and depression, can be regarded as effect indicators which reflect the level of QOL. These indicators usually have a more uniform relationship with QOL, and therefore a patient with poor QOL is likely to have low scores on all effect indicators. In extreme cases it may seem intuitively obvious which items are causal and which are effect indicators, but often it is less clear. We propose a model which includes these two types of indicators and show that they behave in markedly different ways. Formal quantitative methods are developed for distinguishing them. We also discuss the impact of this distinction upon instrument validation and the design and analysis of summary subscales.
ISSN:0962-9343
1573-2649
DOI:10.1023/A:1018491512095