Symposium on Quality of Life in Cancer Patients
Assessing Meaningful Change in Quality of Life Over Time: A Users' Guide for Clinicians

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The objective of this article is to help clinicians interpret trial-based quality of life (QOL) changes over time. We address a series of questions and provide guidelines that are fundamental to assessing and interpreting change. The issues addressed are as follows: (1) What are the characteristics of the population for whom changes in QOL are reported? (2) Is the QOL questionnaire reliable, valid, and responsive to change? (3) Are the timing and frequency of assessments adequate? (4) Is the study adequately powered? (5) How are multiple QOL outcomes addressed in analyses? (6) How are multiple time points handled? (7) Can alternative explanations account for the observed change or lack of observed change (eg, handling of missing data, survival differences, and changes in patient's QOL perspective over time)? and (8) How is statistical significance translated into meaningful change? These guidelines will support clinicians in reviewing the clinical trial literature, which in turn can help them use the data in the treatment decision process.

Section snippets

WHAT ARE THE CHARACTERISTICS OF THE POPULATION FOR WHOM CHANGES IN QOL ARE REPORTED?

It is particularly important to determine whether the characteristics of the population reported in an article are similar to the characteristics of the patient(s) you are treating. To make appropriate use of published QOL data, one must first examine the research sample in terms of key variables, such as disease characteristics (eg, tumor type, tumor localization, stage, extent, duration, performance status), treatment characteristics (eg, type, intensity, duration), sociodemographic and

IS THE QOL QUESTIONNAIRE RELEVANT, RELIABLE, VALID, AND RESPONSIVE TO CHANGE FOR THE OUTCOME OF MOST INTEREST?

Determining the appropriateness of a QOL instrument requires attention to the research objectives of the study, specification of the rationale for the QOL part of the study, identification of the relevant domains of QOL, disease and patient population characteristics, psychometric characteristics (ie, reliability and validity) of the QOL instrument, and practical considerations (ie, respondent burden, language translations for multiethnic or multicountry studies)5, 6, 7, 8, 9, 10 (see also

ARE THE TIMING AND FREQUENCY OF ASSESSMENTS ADEQUATE?

The timing and frequency of QOL assessments in a particular study should be based on the study objective, characteristics and natural course of the disease, treatment regimen, and expected effects of the treatment.31 At least baseline and 1 follow-up QOL assessment are needed to evaluate change. Some potential considerations when evaluating the adequacy of the timing and frequency of assessments are outlined in Table 2. For example, the timing of the QOL assessments should be similar, if not

IS THE STUDY ADEQUATELY POWERED FOR DETECTING MEANINGFUL CHANGE?

Suppose the results of a clinical trial do not warrant the conclusion that the treatment arms differ significantly over time. Such a statement would mean only that evidence was insufficient to exclude the possibility that the results were caused by chance (ie, reject the null hypothesis). It does not necessarily imply that there is no meaningful difference over time between the treatment arms. For example, if the sample size is too small, the results are unlikely to be statistically

HOW ARE MULTIPLE QOL OUTCOMES ADDRESSED IN THE ANALYSES?

Quality-of-life instruments often measure multiple domains (ie, physical, social, and psychological functioning and well-being). Given the many statistical tests required, significant results will occur by chance. No consensus exists on the best approach to dealing statistically with multiplicity in QOL end points.9, 33 Methods for handling multiplicity in statistical analysis include the following: (1) identification of a few primary QOL end points,33, 37 (2) use of summary measures,38 (3)

HOW ARE MULTIPLE TIME POINTS HANDLED?

Most commonly, a wealth of QOL data is collected in a clinical trial. Not only do QOL questionnaires measure multiple domains, they are also administered at several points over time. The first question is whether the authors have presented these data in ways that are meaningful, simple, intuitively comprehensible, and suitable to answer clinically relevant questions. The choice of the tabular or graphical presentation depends on the trial objective and the problems inherent in the data. For

Are Dissimilar Baseline Characteristics Adequately Accounted For?

Despite random assignment of patients to treatment arms, occasionally there are pretreatment differences in demographic or clinical variables between groups. For example, assessing QOL in a subset of patients rather than in the entire trial population could be confounded by the a priori imbalance between treatment groups. In this case, analyzing between-arm change scores with (repeated-measure) analysis of variance is inappropriate. The problem is that the baseline score most likely contains

IS OBSERVED SURVIVAL DIFFERENCE COMBINED WITH QOL IN EVALUATING CHANGE?

Clinical trials in oncology often have significant rates of mortality, and any study that does not account for differential survival may overestimate the treatment effect on QOL. Survival represents an important clinical benefit of treatment, but the extent and nature of missing observations due to mortality complicate the measurement of change in QOL and the statistical analyses. Summary health outcome indices, such as quality-adjusted life-years (QALYs), quality- adjusted time without

DID THE PATIENT'S QOL PERSPECTIVE CHANGE OVER TIME?

Sometimes articles report paradoxical or counterintuitive findings. For example, patients may report a stable QOL over time, whereas their clinical health status deteriorates considerably.61, 62 These findings might be interpreted as resulting from changes in the patient's internal standards, values, and/or the conceptualization of QOL during the disease trajectory. These changes, which are inherent to the process of accommodating to the illness, are referred to as response shifts.63, 64

HOW IS STATISTICAL SIGNIFICANCE TRANSLATED INTO MEANINGFUL CHANGE?

If a clinical trial shows statistically significant changes in QOL outcomes over time, the key question is the extent to which these results are clinically meaningful. The authors must provide some guidance to readers about the clinical relevance or importance of their results. Justification needs to be provided for the definition of meaningful change. For example, clinically meaningful results may be defined as a particular proportion of patients achieving a predefined degree of benefit (see

CONCLUSION

In recommending the most optimal treatment to their patients, clinicians need to consider how alternative treatments might affect their patients’ QOL during treatment and beyond. The challenge is to evaluate the clinical meaningfulness of the reported change or lack of observed change in QOL data obtained in clinical trials. The checklist provided in Table 3 provides guidelines to help clinicians critically assess and interpret longitudinal QOL data reported in the literature, which in turn can

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    This project was supported in part by Public Health Service grants CA25224, CA37404, CA15083, CA35269, CA35113, CA35272, CA52352, CA35103, CA37417, CA63849, CA35448, CA35101, CA35195, CA35415, and CA35103. The entire Symposium on the Clinical Significance of Quality-of-Life Measures in Cancer Patients will be available for purchase as a bound booklet from the Proceedings Editorial Office at a later date.

    Clinical Significance Consensus Meeting Group contributors: Neil Aaronson, PhD, Division of Psychosocial Research and Epidemiology, Cancer Institute, Amsterdam, the Netherlands; Ivan Barofsky, PhD, Johns Hopkins University School of Medicine, Baltimore, Md; Rick Berzon, PhD, Boehringer Ingelheim Pharmaceuticals, Ridgefield, Conn; Amy Bonomi, MPH, Center for Health Studies, MacColl Insitute for Healthcare Innovation, Seattle, Wash; Monika Bullinger, PhD, University of Hamburg, Hamburg, Germany; Joseph C. Cappelleri, PhD, MPH, Global Research and Development, Pfizer Inc, Groton, Conn; David Cella, PhD, Center on Outcomes, Research and Education, Evanston Northwestern Healthcare, Northwestern University, Evanston, Ill; Diane L. Fairclough, DrPH, Colorado Health Outcomes, University of Colorado Health Sciences Center, Denver; Carol Estwing Ferrans, PhD, RN, College of Nursing, University of Illinois at Chicago; Marlene H. Frost, PhD, RN, Women's Cancer Program, Mayo Clinic, Rochester, Minn; Gordon H. Guyatt, MD, Department of Clinical Epidemiology and Biostatistics, McMaster University and Health Sciences Center, Hamilton, Ontario; Ron D. Hays, PhD, Departments of Medicine and Health Services Research, UCLA, Los Angeles, Calif; Patrick Marquis, MD, MBA, Mapi Values, Boston, Mass; Geoff Norman, PhD, McMaster University, Hamilton, Ontario; David Osoba, MD, QOL Consulting, West Vancouver, British Columbia; Teresa Rummans, MD, Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minn; Charles Scott, PhD, American College of Radiology, Philadelphia, Pa; Jeff A. Sloan, PhD, Department of Health Sciences Research, Mayo Clinic, Rochester, Minn; Tara Symonds, PhD, Outcomes Research Department, Pfizer Ltd, Sandwich, Kent, United Kingdom; Claudette Varricchio, DSN, RN, Division of Cancer Prevention, National Cancer Institute, Bethesda, Md; Gilbert Wong, MD, Division of Pain Medicine, Department of Anesthesiology, Mayo Clinic, Rochester, Minn; Albert Wu, MD, Department of Health Policy and Management, Bloomberg School of Hygiene and Public Health, Johns Hopkins University, Baltimore, Md; Kathleen Wyrwich, PhD, Department of Research Methodology, Saint Louis University, St Louis, Mo.

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