Original Article
An integrated method to determine meaningful changes in health-related quality of life

https://doi.org/10.1016/j.jclinepi.2004.04.004Get rights and content

Abstract

Objective

We describe an integrated method for determining meaningful change in health-related quality of life (HRQOL) that combines information from anchor-based and distribution-based methods and illustrate this method using data aggregated from weight loss studies.

Study design and setting

A total of 1476 participants in weight loss studies were evaluated at baseline and at 6 months using the Impact of Weight on Quality of Life-Lite (IWQOL-Lite). Severity of baseline impairment was determined by comparing scores with those obtained from a normative sample of 534 normal/overweight individuals. The precision of the IWQOL-Lite was evaluated using standard error of measurement corrected for regression to the mean. Weight loss was used as an anchor for evaluating changes in IWQOL-Lite scores.

Results

Change in HRQOL varied as a function of weight loss and baseline severity of HRQOL. Using this integrated method, an improvement of 7.7 to 12 points (depending on baseline severity) on IWQOL-Lite total score is considered meaningful.

Conclusion

Meaningful change in HRQOL can be determined using an integrated method that (1) combines information from anchor-based and distribution-based methods, (2) reconciles discrepancies between these two methods, and (3) adjusts for baseline severity and regression to the mean. This method may be applied to other types of HRQOL measures and conditions.

Introduction

There is growing interest in the field of obesity on health-related quality of life (HRQOL) [1], [2], [3], [4]. Measures of HRQOL are increasingly being used to evaluate treatments for obesity, to make therapeutic decisions about the initiation and type of obesity treatment, and to allocate clinical and research resources.

Obesity has been consistently linked to impairments in important aspects of HRQOL, including physical health, emotional well-being, and psychosocial functioning [5], [6], [7]. Further, weight loss has been associated with improvements in HRQOL [8], [9], [10], [11]. However, little work has examined whether these observed improvements in HRQOL after weight loss are meaningful.

It has long been recognized that traditional statistical methods that are used to evaluate treatment efficacy are inadequate for addressing issues of the clinical significance of the effects of that treatment [12], [13], [14], [15]. As noted by Jacobson and Truax [15], “Whether a treatment effect exists in the statistical sense has little to do with the clinical significance of the effect.” Statistical effects are those that occur beyond some level of chance. In contrast, the clinical significance of that effect refers to the benefits derived from that treatment, its impact upon the patient, and its implications for treatment of the patient [15], [16], [17].

A number of methods have been proposed for establishing clinical significance in HRQOL [17]. These methods can be broadly classified as anchor-based methods or distribution-based methods. Anchor-based methods [18] compare changes in HRQOL outcome with other measures or known phenomena that have clinical relevance. Lydick and Epstein [18] have likened this to establishing the construct validity of a measure. Examples of anchors that have been used include global ratings of change [16], [19] and comparison to normative populations [15]. We have suggested the term “criterion-referenced change” to refer to meaningful change established using anchor-based methods [17]. Anchor-based methods do not take into account the measurement precision of the instrument. Consequently, these methods provide no information about the range of change that would be expected by random variation alone.

In contrast to anchor-based methods, distribution-based methods use some statistical property of the sample or the instrument to establish clinically meaningful change. Examples of distribution-based techniques include effect size [20], reliable change index [15], and standard error of measurement (SEM) [11], [21]. Several distribution-based methods (e.g., reliable change index, SEM) take into account the measurement precision of the instrument. We have suggested the term “precision-referenced change” to describe meaningful change established using distribution-based measures of instrument precision [17]. A disadvantage of distribution-based methods is that there are few agreed-upon benchmarks for establishing clinically significant change. In addition, distribution-based methods alone do not provide a good sense of the clinical relevance of the observed change.

In our recent review [17], we identified the need for an integrated system of determining meaningful change that combines information from anchor-based and distribution-based methods and takes other relevant factors, such as baseline impairment in HRQOL and regression to the mean (RTM), into account. We are aware of only two previous attempts to combine anchor-based and distribution-based methods for determining meaningful change in HRQOL. Cella et al. [22] used anchor-based and distribution-based information to determine meaningful change in HRQOL among cancer patients. Concordance rates between these two methods were found to be high, supporting the validity of their established cutoffs. No guidelines were provided for resolving discrepancies in classification between these methods when they did occur. Jacobson and Truax [15] describe a method that integrates anchor-based and distribution-based approaches to determine meaningful change. As the anchor-based criteria, they propose comparing post-treatment functioning to characteristics of a known functional population or a known dysfunctional population of relevance. As the distribution-based measure, they propose the reliable change index for determining whether the change after treatment exceeds the limits of random variation. An individual is considered to be improved (or deteriorated) only when they meet the anchor-based criteria and distribution-based criteria for establishing clinically meaningful change. Thus, information from both of these methods is used to establish cutoffs to determine meaningful change.

Several investigators have raised the question of whether individuals with more severe impairments in HRQOL require a greater change to be considered meaningful than those with less severe impairments [17], [23], [24]. Few studies have examined this issue directly. Stratford et al. [25] reported that patients with more severe low back pain required greater change to be considered “clinically important” than those with less severe pain. We have previously reported [26] that among obese individuals experiencing comparable weight loss, those with more severe initial impairments in HRQOL reported greater improvement than those with less severe impairments. Finally, in a longitudinal study of individuals losing and subsequently regaining at least 5% of their initial weight, Engel et al. [27] reported that those with more severe initial impairments in HRQOL experienced greater improvements during weight loss and greater deterioration during weight regain than those with less severe impairments. Taken together, these suggest the importance of considering baseline HRQOL impairments in establishing cutoffs for determining meaningful change.

A second issue of relevance, related to that of baseline severity, is RTM. RTM is a statistical error-based artifact describing the tendency of extreme scores to become less extreme at follow-up. RTM is typically established by showing a correlation between baseline distance from the mean (typically a normative mean) and subsequent change. When present, RTM has implications for establishing criteria for determining meaningful change because individuals with more extreme scores improve, on average, more than individuals with less extreme scores. Several distribution-based methods have been proposed that take RTM into account [28], [29], [30]. All of these methods share the feature that different thresholds are established for determining meaningful change based on the initial distance from the normative mean.

The purpose of this article is to describe and illustrate an integrated method of determining meaningful change in HRQOL in response to weight loss treatment. Similar to the methods described by Cella et al. [22] and Jacobson and Truax [15], the current method combines information from anchor-based and distribution-based techniques. In addition, this integrated method takes into account the severity of baseline impairment in HRQOL and RTM and provides a systematic method for resolving discrepancies between anchor-based and distribution-based classification.

Section snippets

Subjects

Subjects were 1,476 (1,101 women, 375 men) obese (body mass index [BMI] ≥30) individuals participating in one of eight weight loss studies. These studies included an open-label trial combining phentermine-fenfluramine and dietary counseling (n = 181) [11]; four double-blind, placebo-controlled trials of sibutramine (n = 469) [31]; a naturalistic weight loss study in a managed care setting (n = 337) [32]; a double-blind, placebo-controlled trial of bupropion (n = 232) [33]; and a randomized controlled

Demographics

The sample consisted of 1,101 women (74.6%) and 375 men (25.4%). The average age of the participants was 47.5 (SD = 10.6) with a range of 19 to 79 years. The average BMI for women was 37.0 (SD = 5.3, range = 30–63) and for men was 36.1 (SD = 5.2, range = 30–67). Ethnic background information was not available for the majority of participants.

Baseline HRQOL impairment

The mean IWQOL-Lite total score for the normative sample was 94.7 (SD = 7.6). Comparison of baseline IWQOL-Lite scores in the current sample to the normative mean

Discussion

We describe and illustrate an integrated method for determining meaningful change in HRQOL that combines information from anchor-based and distribution-based methods. This integrated method takes into account measurement precision, external reference information, baseline HRQOL impairment, and RTM. Although we use weight loss and obesity-specific quality of life to illustrate this method in the current example, this method provides a general framework that can easily be adapted to other

Acknowledgements

We acknowledge the contributions of Amy Phillips and Talat Ashraf from Abbott Laboratories; Julie Porter, Marsha Raebel, and Douglas Conner of Kaiser Permanente of Colorado; the Obesity Research Network; Stan Heshka; Weight Watchers International; and Guilford Hartley from Hennepin County Medical Center. Financial support for this project was provided by Bristol-Myers Squibb, Princeton, New Jersey.

References (49)

  • H.M. Sullivan et al.

    Swedish obese subjects (SOS): an intervention study of obesity. Baseline evaluation of health and psychosocial functioning in the first 1743 subjects examined

    Int J Obes

    (1993)
  • L. Kawachi

    Physical and psychological consequences of weight gain

    J Clin Psychol

    (1999)
  • M. de Zwaan et al.

    Two measures of health related quality of life in morbid obesity

    Obes Res

    (2002)
  • J.M. Rippe et al.

    Improved psychological well-being, quality of life, and health practices in moderately overweight women participating in a 12-week structured weight loss program

    Obes Res

    (1998)
  • J.T. Fine et al.

    A prospective study of weight change and health-related quality of life in women

    JAMA

    (1999)
  • K.R. Fontaine et al.

    Impact of weight loss on health-related quality of life

    Qual Life Res

    (1999)
  • R.L. Kolotkin et al.

    The relationship between health-related quality of life and weight loss

    Obes Res

    (2001)
  • D.H. Barlow

    On the relation of clinical research to clinical practices: current issue, new directions

    J Consult Clin Psychol

    (1981)
  • W.H. Yeaton et al.

    Critical dimensions in the choice and maintenance of successful treatments: strength, integrity, and effectiveness

    J Consult Clin Psychol

    (1981)
  • N.S. Jacobson et al.

    Clinical significance: a statistical approach to defining meaningful change in psychotherapy research

    J Consult Clin Psychol

    (1991)
  • R.D. Crosby et al.

    Defining clinically meaningful change in health-related quality of life

    J Clin Epidemiol

    (2003)
  • F. Lydick et al.

    Interpretation of quality of life changes

    Qual Life Res

    (1993)
  • L.E. Kazis et al.

    Effect sizes for interpreting changes in health status

    Med Care

    (1989)
  • C.A. McHorney

    Generic health measurement: past accomplishments and a measurement paradigm for the 21st century

    Ann Intern Med

    (1997)
  • Cited by (126)

    • Eating self-efficacy as predictor of long-term weight loss and obesity-specific quality of life after sleeve gastrectomy: A prospective cohort study

      2019, Surgery for Obesity and Related Diseases
      Citation Excerpt :

      Overall, obesity-specific quality of life improved significantly in this cohort of patients treated with sleeve gastrectomy. At 55 months, the mean IWQOL-Lite score was 83.5 (CI: 79.7, 87.3), slightly below the level of 91.8 ± 12.0 reported for the U.S. general population [31]. To our knowledge, long-term quality of life outcome after sleeve gastrectomy has not been adressed in larger cohorts previously [23,32].

    View all citing articles on Scopus

    Portions of this paper were presented at the 12th European Congress on Obesity, Helsinki, Finland, May 2003.

    View full text