Minimal changes in health status questionnaires: Distinction between minimally detectable change and minimally important change

Abstract

Changes in scores on health status questionnaires are difficult to interpret. Several methods to determine minimally important changes (MICs) have been proposed which can broadly be divided in distribution-based and anchor-based methods. Comparisons of these methods have led to insight into essential differences between these approaches. Some authors have tried to come to a uniform measure for the MIC, such as 0.5 standard deviation and the value of one standard error of measurement (SEM). Others have emphasized the diversity of MIC values, depending on the type of anchor, the definition of minimal importance on the anchor, and characteristics of the disease under study. A closer look makes clear that some distribution-based methods have been merely focused on minimally detectable changes. For assessing minimally important changes, anchor-based methods are preferred, as they include a definition of what is minimally important. Acknowledging the distinction between minimally detectable and minimally important changes is useful, not only to avoid confusion among MIC methods, but also to gain information on two important benchmarks on the scale of a health status measurement instrument. Appreciating the distinction, it becomes possible to judge whether the minimally detectable change of a measurement instrument is sufficiently small to detect minimally important changes.

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Health and Quality of Life Outcomes
Open Access
Commentary
Minimal changes in health status questionnaires: distinction
between minimally detectable change and minimally important
change
Henrica C de Vet*, Caroline B Terwee, Raymond W Ostelo,
Heleen Beckerman, Dirk L Knol and Lex M Bouter
Address: EMGO Institute, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands
Email: Henrica C de Vet* - hcw.devet@vumc.nl; Caroline B Terwee - cb.terwee@vumc.nl; Raymond W Ostelo - r.ostelo@vumc.nl;
Heleen Beckerman - h.beckerman@vumc.nl; Dirk L Knol - d.knol@vumc.nl; Lex M Bouter - lm.bouter@vumc.nl
* Corresponding author
Abstract
Changes in scores on health status questionnaires are difficult to interpret. Several methods to
determine minimally important changes (MICs) have been proposed which can broadly be divided
in distribution-based and anchor-based methods. Comparisons of these methods have led to insight
into essential differences between these approaches. Some authors have tried to come to a uniform
measure for the MIC, such as 0.5 standard deviation and the value of one standard error of
measurement (SEM). Others have emphasized the diversity of MIC values, depending on the type
of anchor, the definition of minimal importance on the anchor, and characteristics of the disease
under study. A closer look makes clear that some distribution-based methods have been merely
focused on minimally detectable changes. For assessing minimally important changes, anchor-based
methods are preferred, as they include a definition of what is minimally important. Acknowledging
the distinction between minimally detectable and minimally important changes is useful, not only to
avoid confusion among MIC methods, but also to gain information on two important benchmarks
on the scale of a health status measurement instrument. Appreciating the distinction, it becomes
possible to judge whether the minimally detectable change of a measurement instrument is
sufficiently small to detect minimally important changes.
Introduction
Health status questionnaires are increasingly used in med-
ical research and clinical practice. They are attractive
because they provide a self-report of patients' perceived
health status. However, the meaning of the (changes in)
scores on these questionnaires is not intuitively apparent.
The interpretation of (change)scores has been a topic of
research for almost two decades [1,2]. It is recognized that
the statistical significance of a treatment effect, because of
its partial dependency on sample size, does not always
correspond to the clinical relevance of the effect. Statisti-
cally significant effects are those that occur beyond some
level of chance. In contrast, clinical relevance refers to the
benefits derived from that treatment, its impact upon the
patient, and its implications for clinical management of
the patient [2,3]. As a yardstick for clinical relevance one
is interested in the minimally important change (MIC) of
health status questionnaires. Changes in scores exceeding
the MIC are clinically relevant by definition.
Published: 22 August 2006
Health and Quality of Life Outcomes 2006, 4:54 doi:10.1186/1477-7525-4-54
Received: 26 July 2006
Accepted: 22 August 2006
This article is available from: http://www.hqlo.com/content/4/1/54
© 2006 de Vet et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0
),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Different methods to determine the MIC on the scale of a
measurement instrument have been proposed. These
methods have been summarized by Lydick and Epstein
[4], and recently more extensively by Crosby et al. [5].
Both overviews distinguish distribution-based and
anchor-based methods [4,5].
Distribution-based approaches are based on statistical
characteristics of the sample at issue. Most distribution-
based methods express the observed change in a standard-
ized metric. Examples are the effect size (ES) and the
standardized response mean (SRM), where the numera-
tors of both parameters represent the mean change and
the denominators are the standard deviation at baseline
and the standard deviation of change for the sample at
issue, respectively. Another distribution-based measure is
the standard error of measurement (SEM), which links the
reliability of the measurement instrument to the standard
deviation of the population [5]. ES and SRM are relative
representations of change (without units), whereas the
SEM provides a number in the same units as the original
measurement. The major disadvantage of all distribution-
based methods is that they do not, in themselves, provide
a good indication of the importance of the observed
change.
Anchor-based methods assess which changes on the
measurement instrument correspond with a minimal
important change defined on the anchor [4], i.e. an exter-
nal criterion is used to operationalize a relevant or an
important change. The advantage is that the concept of
'minimal importance' is explicitly defined and incorpo-
rated in these methods. A limitation of anchor-based
approaches is that they do not, in themselves, take meas-
urement precision into account [4,5]. Thus, there is no
information on whether an important change according
to an anchor-based method, lies within the measurement
error of the health status measurement.
An often used anchor-based method is the one proposed
by Jaeschke et al. [2], which defined MIC as the mean
change in scores of patients categorized by the anchor as
having experienced minimally important improvement or
minimally important deterioration. Another anchor-
based method, proposed by Deyo and Centor [6], is based
on diagnostic test methodology. In this method, the
change score on the measurement instrument is consid-
ered the diagnostic test and the anchor, dividing the pop-
ulation in persons who are minimally importantly
changed and those who are not, is considered the gold
standard. At different cut-off values of change scores the
sensitivity and specificity are calculated and the MIC value
is set at the change value on the measurement, where the
sum of the percentages of false positives and false nega-
tives is minimal.
A number of studies have compared anchor-based and
distribution-based approaches. Comparisons of these
approaches sometimes led to surprisingly similar results.
However, in other situations different results were found.
The focus of this paper is on explanation of the differences
between distribution-based and anchor-based
approaches. We will provide arguments for the distinction
between minimally detectable change and minimally
important change. Appreciating and acknowledging this
distinction enhances the interpretation of change scores
of a measurement instrument.
Comparison of SEM with anchor-based approaches
A number of studies have compared the value of the SEM
with the MIC value derived by an anchor-based approach.
A SEM value is easy to calculate, based on the standard
deviation (SD) of the sample and the reliability of the
measurement instrument: in formula: SEM = SD √(1-R).
As reliability parameter, test-retest reliability or Cron-
bach's α can be used. In the latter case, SEM can be calcu-
lated based on one measurement and it purely represents
the variability of the instrument [7]. Test-retest reliability
requires two measurements in a stable population. It rep-
resents the temporal stability and is therefore more appro-
priate than Cronbach's α to use in the context of changes
in health status which are based on measurements at two
different time points [8]. In classical test theory, SEM has
a rather stable value in different populations [6].
Several authors showed that a MIC based on patient's glo-
bal rating as anchor was close to the value of one SEM
[9,11]. Cella et al. [12] also observed similar values for
SEM and MIC, using clinical parameters as anchor instead
of patients' global rating of change. However, Crosby et al.
[13] showed that only for patients with moderate baseline
values the anchor-based MIC value more or less equalled
the SEM value (with adjustment for regression to the
mean). With higher baseline values the MIC became con-
siderably larger than one SEM, while with lower baseline
values the MIC became much smaller than one SEM. A
recent study [14] compared SEM with anchor-based esti-
mations of minimally important change using crosssec-
tional and longitudinal anchors. No substantial
differences were found between these methods, but it
should be noted that they only presented anchor-based
values when effect sizes were between 0.2 and 0.5 [14].
Wyrwich [15], compared SEM to MIC values determined
by an anchor-based approach in two sets of studies which
differed on several points. Set A consisted of studies on
musculoskeletal disorders like low back pain [16], neck
pain [17] and lower extremity disorders [18], while set B
included studies on chronic disorders like chronic respira-
tory disease [10], chronic heart failure [11], and asthma
[19]. In addition, set A studies used the ROC method and

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studies in set B applied mean change as anchor-based
method. And they differed with regard to the definition of
'minimal important change' on the anchor. For set A, the
MIC corresponded to 2.3 or 2.6 * SEM, for set B, the MIC
values were close to 1*SEM.
In summary, it seems that the proposition that one SEM
equals the MIC is not a universal truth.
MIC is a variable concept
1. MIC depends on the definition of 'important change' on the anchor
A patient's self report global rating scale of perceived
change has often been used as anchor. Studies determin-
ing MIC have used different definitions of 'minimally
importance' using this anchor. Wyrwich et al. [10,11,19]
defined a slight change on the anchor as 'minimally
important', consisting of the categories "a little worse/bet-
ter" and "somewhat worse/better". In their earlier studies
Wyrwich et al. even included the category "almost the
same, hardly any worse/better" [10,11]. Other authors
have defined 'minimal importance' as a larger change on
the anchor. Binkley et al. [18] chose the category "moder-
ate improvement" as minimally important. Stratford et al.
[17] chose to lay the cut-off point for MIC between "mod-
erate" and "a good deal" of improvement. Others [20-24]
have laid the cut-off point for MIC between "slightly
improved" and "much improved" on the patient global
rating scale. In studies requiring moderate or much
improvement, the MIC corresponds to about 2.5 times the
SEM value. The differences in set A and B in Wyrwich's
study [15] may be partly explained by a different defini-
tion of important change on the anchor: set A consists of
studies which defined MIC as a good deal better [16] and
studies in set B [10,11,19] defined MIC as a little and
somewhat better according to the anchor.
The MIC value depends to a great degree on the anchor's
definition of minimal importance. So, the crucial question,
then, is "what is a minimally important improvement or
deterioration?" Some authors tend to emphasize minimal,
while others stress important [25]. Remarkably, the refer-
ence standard is usually based on the amount of change
and little research has focused on the "importance" of the
change.
2. MIC depends on the type of anchor
Clinicians may have other opinions about what is impor-
tant than patients. Therefore, clinician-based anchors may
lead to different MIC values. Kosinski et al. [26] used five
different anchors to estimate the minimally important dif-
ferences for the SF-36 in a clinical trial of people with
rheumatoid arthritis, and found different MIC values
dependent on the anchor used. Some authors [16,20-24]
have asked patients' global rating of perceived change in
overall health, while others asked to rate the perceived
change separately for each dimension of their measure-
ment instrument [10,19]. For example, in a study deter-
mining the MIC of the Chronic Respiratory Disease Scale
the patients' global rating has been asked separately for
the subscales dyspnoea, fatigue and emotional function
[10]. In the rating of change in overall health status
patients have to weigh the relative contribution of the dif-
ferent dimensions on their health status. For example, if
patients with asthma judge dyspnoea to be much more
important for their quality of life than emotional func-
tioning, a small change in dyspnoea will affect the global
rating of overall health, while for emotional functioning
the change must be larger to be influential. The observed
MIC value will be smallest for the anchor that shows the
highest correlation with the health status scale under
study.
3. MIC depends on baseline values and direction of change
Several studies have shown that the MIC value of a meas-
urement instrument depends on the baseline score on
that instrument. This was clearly shown by Crosby et al.
[13] who compared the SEM, corrected for regression to
the mean, with the anchor-based MIC for various baseline
scores of obesity-specific health related quality of life.
With higher baseline values MIC became considerably
larger than one SEM. Other authors [16,24,27,28] showed
that the values of anchor-based MIC for functional status
questionnaires in patients with low back pain were
dependent on baseline values. Patients with a high level of
functional disability at baseline must change more points
on the Roland Disability Questionnaire than patients
with less functional disability at baseline to consider it an
important change. In addition, Van der Roer et al. [24]
reported different MIC values for acute and chronic low
back pain patients.
Furthermore, there has been discussion whether the MIC
for improvement is the same as for deterioration [5]. In
some studies the same MIC is reported for patients who
improve and patients who deteriorate [2,29,30], but oth-
ers found different MIC values for improvement and dete-
rioration. Cella et al. [31] demonstrated that cancer
patients who reported global worsening had considerably
larger change scores on the Functional Assessment of Can-
cer Therapy (FACT) scale than those reporting comparable
global improvements. Also Ware et al. observed that a
larger change on the SF-36 was needed for patients to feel
worsened than to feel improved [32].
Thus, the MIC is dependent on, among other things, the
type of anchor, the definition of 'minimal importance' on
the anchor, and on the baseline score which might be an
indicator of severity of the disease. Therefore, various
authors have suggested to present a range of MIC values
[24,26,33-35], to account for this diversity. Hays et al. rec-

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ommend to use different anchors and to give reasonable
bounds around the MIC, rather than forcing the MIC to be
a fixed value [33,34].
Distinction between minimally detectable and minimally
important changes
Some authors have searched for uniform measures for
minimally important changes. Wyrwich and others
[10,11] have evaluated whether the one-SEM criterion can
be applied as a proxy for MIC. Norman et al. [36] made a
systematic review of 38 studies (including 62 effect sizes),
and observed, with only a few exceptions, that the MICs
for health related quality of life instruments were close to
half a standard deviation (SD). This held for generic and
disease specific measures and was not dependent on the
number of response options.
Norman et al. [36] explain their finding of 0.5 SD by refer-
ring to psychophysiological evidence that the limit of peo-
ple's ability to discriminate is approximately 1 part in 7,
which is very close to half a SD. Thus, this criterion of 0.5
SD may be considered a threshold of detection and corre-
sponds more to minimally detectable change than to mini-
mally important change. Also SEM, based on the test-retest
reliability in stable persons, is merely a measure of detect-
able change [37]. Note that, using the formula SEM = SD
√(1-R), 1 SEM equals 0.5 SD, when the reliability of the
instrument is 0.75. Thus, 0.5 SD and SEM clearly alert to
the concept of minimally detectable changes.
Wyrwich [15] comparing the two sets of studies showed
that if the cut-off point for 'minimal importance' on the
anchor is laid between "no change" and "slightly
changed", i.e. the first category above no change, together
with a complaint-specific anchor, the MIC is close to one
SEM. But in this case it focuses more on minimally detecta-
ble change than minimally important change. Wyrwich [15]
showed a clear dependency between MIC and cut-off
value of 'minimal importance' on the anchor of patients'
global rating of perceived change.
Salaffi et al. [38] presented the change on a numerical rat-
ing scale for pain using two cut-off points on a patient glo-
bal impression of change scale. In their opinion, a MIC
using "slightly better" as cut-off point on the anchor
reflected the minimum and lowest degree of improve-
ment that could be detected, while the cut-off point
"much better" refers to a clinically important outcome.
Note that the choice of anchor and cut-off point is arbi-
trary and cannot be based on statistical characteristics.
Interpretation and applicability
We believe that the confusion about MIC will decrease if
the distinction between minimally detectable and mini-
mal important change is appreciated and acknowledged.
In statistical terms, the minimally detectable change
(MDC), also called smallest detectable change or smallest
real change [37] shows which changes fall outside the
measurement error of the health status measurement
(either based on internal or test-retest reliability in stable
persons). It is represented by the following formula: MDC
= 1.96 * √2 * SEM, where the 1.96 derives from the 95%
confidence interval of no change, and √2 is included
because two measurements are involved in measuring
change [37].
As a different concept, the MIC value depicts changes
which are considered to be minimally important by
patients, clinicians, or relevant others. The SEM, the min-
imally detectable change and the minimally important
change are all important benchmarks on the scale of the
measurement instrument, which helps with the interpre-
tation of change scores.
Appreciating the distinction, we can answer the important
question whether a health status measurement instru-
ment is able to detect changes as small as the MIC value.
This application is shown in a study on measurement
instruments for low back pain [27] and for visual impair-
ments [39].
Conclusion
Some distribution-based methods to assess MIC have
been more focussed on minimally detectable changes
than on minimally important changes. For assessing min-
imally important changes, anchor-based methods are pre-
ferred, as they include a definition of what is minimally
important. Acknowledging the distinction between mini-
mally detectable and minimally important changes is use-
ful, not only to avoid confusion among MIC methods, but
also to gain information on two important benchmarks
on the scale of a health status measurement instrument.
Moreover, it becomes possible to judge whether the min-
imally detectable change of a measurement instrument is
sufficiently small to detect minimally important changes.
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