Original articles
Comparative responsiveness of generic and specific quality-of-life instruments

https://doi.org/10.1016/S0895-4356(02)00537-1Get rights and content

Abstract

We assessed the relative responsiveness of generic and specific quality of life instruments in 43 randomized controlled trials that compared head-to-head 31 generic and 84 specific instruments. Using weighted effect size as the metric of responsiveness, we assessed the impact of instrument type, disease category, and magnitude of underlying therapeutic effect on responsiveness, and assessed the responsiveness of specific instruments relative to the corresponding domains of generic measures. In studies with a nonzero therapeutic effect, specific instruments (mean = 0.57) were significantly more responsive than generic instruments (mean = 0.39, P = .01), and than the corresponding domains of generic instruments (mean = 0.40, P = .03). Studies with low, medium, and high therapeutic effects showed a corresponding gradation in responsiveness differences between specific and generic instruments. We conclude that, overall, specific instruments are more responsive than generic tools, and that investigators may come to misleading conclusions about relative instrument responsiveness if they include studies in which the magnitude of the underlying therapeutic effect is zero.

Introduction

Assessing change over time in health-related quality of life (HRQL) requires instruments capable of capturing any changes that, even if small, are important to patients. This instrument property, referred to as responsiveness, guides researchers' choice of HRQL measures for clinical trials.

Many commentators have suggested that targeted disease-specific or condition-specific HRQL instruments are likely to be more responsive than general or generic instruments, whose strengths include breadth and applicability across conditions and interventions. This intuitive view is based on the ability of specific instruments to focus on health aspects that are important to a specific patient group, as reflected by inclusion of multiple items in each relevant domain. Investigators cannot, however, apply specific instruments across conditions or diverse populations, making comparisons between these different populations impossible. As a result, researchers often have to use a combination of specific and generic instruments to achieve responsiveness and comparability.

Despite the prevailing wisdom, specific instruments have often proved no more responsive than generic instruments. For example, a specific instrument for patients undergoing knee replacement [1], an elder-specific instrument with individualized response items [2], an epilepsy-specific tool [3], and arthritis-specific instruments [4] proved no more responsive than generic tools or analogous generic domains. If, in general, specific instruments proved no more responsive than corresponding domains of generic instruments, the latter would suffice to assess impact of interventions, reducing respondent burden and simplifying outcome assessment. Thus, HRQL investigators are likely to find a comprehensive and unbiased assessment of the relative responsiveness of generic and specific instruments of use.

Murawski and Miederhoff synthesized published data on responsiveness of generic and specific instruments using effect size as a measure of responsiveness [5]. They found specific instruments more responsive than generic tools in studies in which both types of instruments were applied to the same patients but not when comparing studies that used only specific instruments to studies that used only generic instruments. Although providing useful data, this study has several limitations.

First, Murawski and Miederhoff included in the analysis nonrandomized and uncontrolled studies. Improvement in HRQL may result from a combination of natural history, placebo, the impact of the measurement process itself, and intervention effects. The psychologic dimensions of HRQL tools are particularly vulnerable to placebo effects, and effects of the measurement process itself [6]. It is possible that the relative responsiveness of generic and specific measures differs in observational studies and randomized trials; our interest is in the latter category of studies.

Second, Murawski and Miederhoff included studies of interventions without a clear effect on HRQL. Such studies contribute random error to the comparison of generic and specific measures, making differences in responsiveness more difficult to detect. Third, the relative responsiveness of generic and specific instruments may differ across types of interventions or disease categories, and these authors did not address this issue. Fourth, it is not clear how the investigators dealt with studies with multiple interventions. Finally, they did not include the Short Form-36 (SF-36), one of the most widely used generic measures.

Therefore, we undertook an analysis of the comparative responsiveness of generic and specific HRQL instruments used in randomized trials. Our systematic approach included clearly defined eligibility criteria for candidate studies, a comprehensive collection of randomized controlled trials (RCTs) that provided head-to-head comparisons of generic and specific instruments; and a methodology that allowed us to focus on trials with a nonzero underlying treatment effect. We hypothesized that: (1) specific HRQL instruments are more responsive than generic tools; (2) specific instruments are more responsive than corresponding generic-derived domains; (3) including studies in which there was no difference between treatment and control groups blunts the relative impact of instrument type on responsiveness.

Section snippets

Methods

We conceptualized HRQL as the patients' subjective experience of health status, and categorized instruments as generic if they were multiple- or single-item tools, applicable to a broad range of patient groups, diseases, and interventions and encompassing all relevant HRQL domains. We considered instruments to be specific if they targeted a particular patient group, disease, intervention or HRQL domain, regardless of the type of study in which the instruments were used. We excluded from the

Included and excluded studies

Out of 87 potentially eligible randomized controlled trials, 40 satisfied eligibility criteria and the articles, or subsequent correspondence with the authors, provided the necessary information for our analysis 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46. Among the 40 articles included, two 18, 42 contained reports on two or more studies. Therefore, the number of studies included in

Discussion

Our data demonstrate that, in randomized trials with a true underlying therapeutic effect, specific instruments are more responsive to change than generic instruments and than analogous domains of generic instruments. The results support the notion that researchers need to use a combination of specific and generic instruments to ensure responsiveness, breadth, and comparability across populations. The results, however, may or may not be generalizable to observational studies.

A key aspect of the

Acknowledgements

Drs. Geoff Norman and Stephen Walter provided invaluable assistance with statistical analyses. We thank the following authors who kindly responded to our request for additional information: Charles Goldstein, Ann Jacoby, Andreas Laupacis, J. Lonnqvist, George Torrance, Peter Tugwell, Ingela Wiklund, and Cindy Wong. Lisa Buckingham's skillful data management is appreciated.

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