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What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D
Stephen J Walters* and John E Brazier
Corresponding author:
Stephen J Walters
Sheffield Health Economics Group, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK
For all author emails, please .
Health and Quality of Life Outcomes 2003, 1:4&
doi:10.25-1-4
The electronic version of this article is the complete one and can be found online at:
Received:7 February 2003
Accepted:11 April 2003
Published:11 April 2003
& 2003 Walters and B licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
Background
The SF-6D is a new single summary preference-based measure of health derived from
the SF-36. Empirical work is required to determine what is the smallest change in
SF-6D scores that can be regarded as important and meaningful for health professionals,
patients and other stakeholders.
Objectives
To use anchor-based methods to determine the minimally important difference (MID)
for the SF-6D for various datasets.
All responders to the original SF-36 questionnaire can be assigned an SF-6D score
provided the 11 items used in the SF-6D have been completed. The SF-6D can be regarded
as a continuous outcome scored on a 0.29 to 1.00 scale, with 1.00 indicating "full
Anchor-based methods examine the relationship between an health-related quality of
life (HRQoL) measure and an independent measure (or anchor) to elucidate the meaning
of a particular degree of change. One anchor-based approach uses an estimate of the
MID, the difference in the QoL scale corresponding to a self-reported small but important
change on a global scale. Patients were followed for a period of time, then asked,
using question 2 of the SF-36 as our global rating scale, (which is not part of the
SF-6D), if there general health is much better (5), somewhat better (4), stayed the
same (3), somewhat worse (2) or much worse (1) compared to the last time they were
assessed. We considered patients whose global rating score was 4 or 2 as having experienced
some change equivalent to the MID. In patients who reported a worsening of health
(global change of 1 or 2) the sign of the change in the SF-6D score was reversed (i.e.
multiplied by minus one). The MID was then taken as the mean change on the SF-6D scale
of the patients who scored (2 or 4).
This paper describes the MID for the SF-6D from seven longitudinal studies that had
previously used the SF-36.
Conclusions
From the seven reviewed studies (with nine patient groups) the MID for the SF-6D ranged
from 0.010 to 0.048, with a weighted mean estimate of 0.033 (95% CI: 0.029 to 0.037).
The corresponding Standardised Response Means (SRMs) ranged from 0.11 to 0.48, with
a mean of 0.30 and were mainly in the "small to moderate" range using Cohen's criteria,
supporting the MID results. Using the half-standard deviation (of change) approach
the mean effect size was 0.051 (range 0.033 to 0.066). Further empirical work is required
to see whether or not this holds true for other patient groups and populations.
Introduction
Health Related Quality of Life (HRQoL) outcome measures are being increasingly used
in research trials, but less so in routine clinical practice. The interpretation of
HRQoL scores raises many issues. [-] The scales and instruments used may be unfamiliar to many clinicians and patients,
who may be uncertain of the meaning of the scale values and summary scores. []
Repeated experience and familiarity with a wide variety of physiological measures
such as blood pressure or forced expiratory volume, has allowed clinicians to make
meaningful interpretation of the results. [,] In contrast, the meaning of a change in score of x points on a HRQoL instrument is
less intuitively apparent, not only because the scale has unfamiliar units, but also
because health professionals seldom use HRQoL measures in routine clinical practice.
In clinical trials, where HRQoL instruments are being increasingly used as primary
outcome measures, it is simple to determine the statistical significance of a change
in HRQoL, but placing the magnitude of these changes in a context that is meaningful
for health professionals, patients and other stakeholders (Pharmaceutical and Medical
Device Developers, Insurance Payers, Regulators, Governments) has not been so easy.
Ascertaining the magnitude of change that corresponds to a minimal important difference
would help address this problem. [] So when determining an important change standard the perspective can influence the
assessment approach and the way in which an important difference is determined. [] The minimal important difference (MID), from the patient perspective, can be defined as "the smallest difference in score in the domain of interest which patients perceive
as beneficial and which would mandate, in the absence of troublesome side effects
and excessive cost, a change in the patient's management". []
Thus, individual change standards are needed to provide meaningful interpretation
of HRQoL intervention and treatment effects and to classify patients based on this
standard as improved, stable or declined. To date two broad strategies have been used
to interpret differences or changes in HRQoL following treatment: [] distribution based approaches – the effect size (ES); and anchor-based measures –
the minimum clinically important difference (MCID).
Distribution based approaches rely on relating the difference between treatment and
control groups to some measure of variability. The most popular approach uses Cohen's
[] standardised effect size, the mean change divided by the standard deviation to serve
as an "effect size index", that is suitable for sample size estimation. Cohen suggested
that standardised effect sizes of 0.2 to 0.5 should be regarded as "small", 0.5 to
0.8 as "moderate" and those above 0.8 as "large". Cohen's effect size may be influenced
by the degree of homogeneity or heterogeneity in the sample. Distribution-based methods
rely on expressing an effect in terms of the underlying distribution of the results.
Investigators may express effects in terms of between-person standard deviation units,
within-person standard deviation units, and the standard error of measurement. []
Four statistics commonly used to index responsiveness are: []
3. the standa []
4. the responsiveness statistic. []
The formula for these statistics are as follows, where D = raw sco
SE = standard err SD = standard deviation at time 1; SD* = standard
deviation of D; SD# = standard deviation of D among stable subjects (those who true
status is constant over time):
Paired t-statistics = D/SE
Effect size (ES) statistic = D/SD
Standardised response mean (SRM) = D/SD*
Responsiveness statistic = D/SD#
The paired t-statistic is best suited to pre-post assessments of interventions of known efficacy.
The effect size statistic relates change over time to the standard deviation of baseline
scores. The standardised response mean compares change to the standard deviation of
change. The responsiveness statistic looks at HRQoL change relative to variability
for clinically stable respondents. The effect size statistic ignores variation in
change entirely, the t-statistic ignores information about variation in scores for clinically stable respondents,
and the responsiveness statistic ignores information about variation in scores for
clinically unstable responders.
Anchor-based methods examine the relationship between an HRQoL measure and an independent
measure (or anchor) to elucidate the meaning of a particular degree of change. Thus
anchor-based approaches require an independent standard or anchor that is itself interpretable
and at least moderately correlated with the instrument being explored. [] One anchor-based approach uses an estimate of the MID, the difference on the HRQoL
scale corresponding to self-reported small but important change on a global scale.[]
Norman et al mention several problems with the global assessment of change including, that the
reliability and validity of the global scale has not been established and that the
judgement of change is psychologically difficult. [] Another limitation of the global rating is that is does not represent a criterion
or gold standard for assessment of change and yet we use the global rating as an anchor
to define small, medium and large changes. [,]
No single approach to interpretability is perfect. As Guyatt et al suggest the use of multiple strategies is likely to enhance the interpretability of
any particular instrument. [] Therefore we used both distribution and anchor-based approaches to try and establish
the interpretability of the SF-6D, a new single summary preference-based measure of
health derived from the SF-36.
The SF-36 is one of the most widely used HRQoL outcome measures in the world today.
It contains 36 questions measuring health across eight dimensions – physical functioning,
role limitation because of physical health, social functioning, vitality, bodily pain,
mental health, role limitation because of emotional problems and general health. Responses
to each question within a dimension are combined to generate a score from 0 to 100,
where 100 indicates "good health". [] Thus, the SF-36 generates a profile of HRQoL outcomes (on up to eight dimensions),
which makes statistical analysis and interpretation difficult. []
The developers of the SF-36 have suggested that using the general health dimension
a five-point difference (on the 0–100 scale) is the smallest score change achievable
by an individual and considered as 'clinically and socially relevant'. [] Angst et al found the MCID ranged from 3.3 to 5.3 points on the physical function dimension and
7.2 to 7.8 points on the bodily pain dimension in patients with osteoarthritis of
the hip or knee. [] Hays and Morales also provide information on what a clinically important difference
is for the SF-36 scales. They conclude that the MCID for the SF-36 is "typically in
the range of 3–5 points", although they also recommend caution in interpreting 3–5
points on the SF-36 dimensions as the MCID. []
The method of scoring the SF-36 is not based on preferences. The simple scoring algorithm
for the eight dimensions assumes equal intervals between the response choices, and
that all items are of equal importance, which may not be appropriate. The SF-6D is
a new single summary preference based or utility measure of health derived from the
SF36. [,] Empirical work is required to determine what is the smallest change in SF-6D scores
that can be regarded as important. We used anchor-based methods to determine the MID
for the SF-6D for various datasets.
The Questionnaire: SF-6D Health State Classification
The SF-36 was revised into a six-dimensional health state classification called the
SF-6D. The six dimensions are physical functioning, role limitations, social functioning,
pain, mental health and vitality. These six dimensions each have between two and six
levels. An SF-6D "health state" is defined by selecting one level from each dimension.
A total of 18,000 health states are thus defined. All responders to the original SF-36
questionnaire can be assigned SF-6D score provided the 11 items used in the six dimensions
of the SF-6D have been completed. The SF-6D preference-based measure can be regarded
as a continuous outcome scored on a 0.29 to 1.00 scale, with 1.00 indicating "full
health". [,]
The studies
The data used in this paper comes from seven longitudinal studies and (nine patient
groups), which used the SF-36 including randomised controlled trials, [] and observational studies. [-]
Global Rating of change (GRoC)
Patients were followed for a period of time, then asked, using question 2 of the SF-36
as our global rating of change scale, (which is not part of the SF-6D), if:
2. Compared to one year ago, how would you rate your health in general now?
(5) Much better now than one year ago
(4) Somewhat better now than one year ago.
(3) About the same
(2) Somewhat worse now than one year ago.
(1) Much worse now than one year ago
The original question 2 of the SF36 compares health now with one year ago. Depending
on the follow-up time we used a slightly modified version: e.g. health now compared
to three (or six) months ago.
Statistical Analysis
We examined the relationship between the global ratings of change question and changes
in SF-6D score, by calculating the change in SF-6D score from 1st to 2nd assessment for each patient. We considered patients whose GRoC score was 4 or 2 as
having experienced some change equivalent to the MID. In patients who reported a worsening
of health (GRoC of 1 or 2) the sign of the change in the SF-6D score was reversed
(i.e. multiplied by minus one). The MID was then taken as the mean change on the SF-6D
scale of the patients who scored (2 or 4).
Since the SF-6D is a continuous measure of effect we used meta-analytic methods to
estimate the weighted grand mean of the MID and to test the hypothesis of homogeneity
of MID across the nine studies. If there was no statistical evidence of lack of homogeneity,
a 95% confidence interval for the summary estimate of the MID was then calculated.
We also used a distribution-based approach and calculated a standardised response
mean (SRM). Since the standard error of the SRM is not defined we used bootstrap methods to
estimate 95% confidence intervals for the SRM. []
Global measures of change are typically highly correlated with the present state and
uncorrelated with the initial state. Any measure of change that reflects the unbiased
difference between the final and initial state, should show a positive correlation
with the final state and an equal negative correlation with the initial state. [] We therefore also calculated Pearson's product moment correlations between the GRoC
question and the baseline and follow-up SF-6D scores.
This paper describes the MID for the SF-6D from a variety of longitudinal studies,
with different patient groups and length of follow-up that had previously used the
SF-36 (Table ).
The nine longitudinal studies
shows that from the nine patient groups the MID for the SF-6D ranged from 0.010 to
0.048, with a mean 0.030 and a median 0.032. The wide confidence intervals for the
MID estimates, including negative values, reflect both the uncertainty in the estimates
and the small study sizes. The corresponding effect sizes (SRMs) ranged from 0.11
to 0.48, mean 0.30 and were mainly in the "small to moderate" range using Cohen's
criteria. Using a half-standard deviation of change approach the mean effect size
was 0.051 and ranged from 0.033 to 0.066. This suggests that the results obtained
through the MID method are reasonable and generally of similar size to the effect
size (SRM) estimates. It demonstrates that regardless of the method used, the actual
cut-off point for a clinically important difference is going to be in the same neighbourhood,
thereby making the particular method of approach less important.
Minimum Important Difference (MID's) and Effect Sizes (SRM's)
As expected since the MID and SRM both contain the mean change, Figure
shows there was a strong correlation (r = 0.70, p = 0.014) between the MID and SRM
estimates (see Figure ). There was no reliable evidence of an association between the MID and the time between
assessments (correlation r = 0.24, p = 0.54) in our nine studies.
There was no reliable statistical evidence of lack of homogeneity in the MID estimates
across the nine studies (χ2 = 13.41 on 8 df, p = 0.098). Therefore it seemed reasonable to combine the MID estimates
from the nine studies to produce an overall weighted grand mean MID estimate of 0.033
(95% CI: 0.029 to 0.037). Figure
shows a forest plot of the MID estimates and associated confidence limits for the
nine studies and the estimated combined overall weighted grand mean MID.
The combining of the "somewhat worse" and "somewhat better" groups assumes the two
cohorts are identical except for the sign. Table
suggests some evidence that the magnitude of the MID for those who improved and those
whose deteriorated is different, but this result was not statistically significant.
Magnitude of the MID by worse/better
shows the moderate correlations (mean 0.45, range: 0.18 to 0.57) were found between
response to global change (anchor) GRoC question and the SF-6D at follow-up across
the 9 studies. Lower correlations (mean 0.22, range: 0.01 to 0.41) were found between
the response to the GRoC question and the SF-6D score at initial assessment across
the nine studies.
Correlations between global health change scale and baseline and follow-up SF-6D scores.
Discussion
We used a five-point GRoC others have used seven or 14 points, which may be
more sensitive. [,] Although the designation of what GRoC suggests patients as fundamentally unchanged
and what GRoC suggests patients have experienced a small but important change is inevitably
subjective.
The reliability and validity of a single GRoC question has not been established. Multi-item
scales may have better reliability. Indeed if the single GRoC could be shown to have
superior measurement properties, then there is no reason not to simply use this a
measure of HRQoL. Although Wyrwich found moderate-to-substantial agreement between
the responses to question 2 of the SF-36 (weighted Kappa 0.64 – 0.73) at test and
re-test (1–4 days later) in a group of 241 patients with asthma, coronary artery disease,
congestive heart failure and COPD. This result provides some evidence of the usefulness
of retrospective GRoC as patient-perceived anchors for ascertaining important HRQoL
changes. []
The judgement of change is psychologically difficult. Patients must be able to quantify
both their present state and their initial state and then perform a mental subtraction.
Patients may be unable to recall their initial state, and the judgement is based on
their present state and working backwards. Any measure of change that reflects the
unbiased difference between the final and initial state, should show a positive correlation
with the final state and an equal negative correlation with the initial state. [] Our results found larger correlations between the global measures of change with
the present state (HRQoL) and far lower correlations with the initial state, supporting
this hypothesis.
The length of time between 1st and 2nd assessments was up to a year, which is far larger than the timeframes used in other
studies (e.g. 2 weeks for Jaeschke et al [] and 4 weeks for Juniper et al []). This may be a limitation of this study in the evaluation of clinical change, as
patients may have some difficulty recalling their previous state of health. However,
we found no reliable evidence of an association between the MID and the time between
assessments in our nine studies. Although preliminary results with two older adults
cohorts suggest some form of 'Response shift' and that the MID may not be constant
over time.
We combined the worse and better groups into one and assumed that the magnitude of
the MID for these two cohorts were identical except for the sign. We found no reliable
statistical evidence that the magnitude of the MID for those who improved and deteriorated
was different. This may be explained by the small sample sizes for some of the studies
and the low power to detect anything other than large differences in the mean changes.
Thus the small sample sizes can explain the lack of statistical significance of the
difference in MID between the worse and better groups, but not the overall size of
the observed mean difference, which for some studies was more than twice as great
in one group compared to the other.
our results require validation with alternative anchors or
multiple anchor methods. Other approaches to interpretating changes in HRQOL are available
including two similar distribution approaches, e.g. Jacobson's Reliable Change Index
[,] and Wyrwich's Standard Error of Measurement. [,]
The SF-6D is an example of a utility or preference-based measure of HRQoL. The primary
use of such measures is to adjust life years saved by quality for use in economic
evaluations and decision models. Preference-based health state scores or utilities
do not have natural units. Since health is a function of both length of life and quality
of life, the QALY (Quality-adjusted life year) has been developed in an attempt to
combine the value of these attributes into a single index number. If utilities are
multiplied by the amount of time spent in that particular health state then they become
QALYs (and are measured in units of time). QALYs allow for varying times spent in
different states by calculating an overall score for each patient. For the studies
where the follow-up is one year (e.g. the two older adults cohorts) the mean change
in utility scores over the one year can be directly interpreted as the MID for a QALY.
QALYs may have the potential to influence public policy and resource allocation decisions.
Results from other preference based measures, such as the 15D and Health Utilities
Index suggests a difference of 0.03 is considered the minimum clinically important
difference for sample size calculations. Finally, as Drummond suggests, in the case
of preference-based measures, if the ultimate objective is to influence resource allocation
decisions, then it is the difference in cost-effectiveness (e.g. incremental cost
per QALY) that is important, not the change in quality of life. Therefore changes
in the measure alone may not be of interest without also considering the cost of bringing
about such changes. []
Our findings are also limited in that a change in SF-6D score of 0.033 is important
when the instrument is used for examining within-patient changes, but this does not
necessarily mean that a difference of 0.033 will signify the MID when the instrument
is used to discriminate between patients.
Despite the absence of a gold standard (criterion) measure, establishing the mean
of any changes in a new measure like the SF-6D requires some sort of independent standard.
The GRoC represents one credible alternative. Whilst we have not established with
certainty a single best estimate of the MID for the SF-6D, our data suggest a plausible
range within which the MID probably falls. This information will be useful in the
interpreting SF-6D scores, both in individuals and in groups of patients participating
in trials. It will also be useful in the planning of new trials, as sample size depends
on the magnitude of the difference investigators consider important and are not willing
to risk failing to detect. []
Summary and Conclusions
From the nine reviewed studies the MID for the SF-6D ranged from 0.010 to 0.048, weighted
mean 0.033 (95% CI: 0.029 to 0.037). The corresponding SRMs ranged from 0.11 to 0.48,
mean 0.30 and were mainly in the "small to moderate" range using Cohen's criteria,
supporting the MID results. Using a half-standard deviation of change approach the
mean effect size was 0.051 and ranged from 0.033 to 0.066. This suggests that the
results obtained through the MID method are reasonable and generally of similar size
to the effect size (SRM) estimates. It demonstrates that regardless of the method
used, the actual cut-off point for a clinically important difference is going to be
in the same neighbourhood, thereby making the particular method of approach less important.
However, further empirical work is required to see whether or not these results hold
true for other patient groups and populations.
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