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Abstract
The concept of a minimal clinically important difference (MCID) is well established. Here, we review the evidence base and methods used to define MCIDs as well as their strengths and limitations. Most MCIDs in chronic obstructive pulmonary disease (COPD) are empirically derived estimates applying to populations of patients. Validated MCIDs are available for many commonly used outcomes in COPD, including lung function (100 ml for trough FEV1), dyspnea (improvement of ≥ 1 unit in the Transition Dyspnea Index total score or 5 units in the University of California, San Diego Shortness of Breath Questionnaire), health status (reduction of 4 units in the St George’s Respiratory Questionnaire total score), and exercise capacity (47.5 m for the incremental shuttle walking test, 45–85 s for the endurance shuttle walking test, and 46–105 s for constant-load cycling endurance tests), but there is currently no validated MCID for exacerbations. In a clinical trial setting, many factors, including study duration, withdrawal rate, baseline severity, and Hawthorne effects, can influence the measured treatment effect and determine whether it reaches the MCID. We also address recent challenges presented by clinical trials that compare active treatments and suggest that MCIDs should be used to identify the additional proportion of patients who benefit, for example, when one drug is replaced by another or when a second drug is added to a first. We propose the term “minimum worthwhile incremental advantage” to describe this parameter.
A minimal clinically important difference (MCID) provides a guide as to whether an intervention provides a minimum level of perceived benefit and moves beyond the concept of statistical differences. The term was first described in 1989 (1) and is now a well-established concept, viewed by healthcare professionals and regulatory bodies as a reliable method for evaluating an intervention.
Chronic obstructive pulmonary disease (COPD) exemplifies some of the challenges associated with calculating and using MCIDs (2). Here, we review the evidence base and methods used to define MCIDs for the major outcomes in COPD. In response to the advent of multiple-drug therapies for COPD, we propose the introduction of a concept, based around the MCID, termed the “minimum worthwhile incremental advantage,” to describe the benefit of one active treatment regimen over another.
MCIDs are usually derived by one of two approaches: distribution- and anchor-based (3, 4). Distribution-based methods use the frequency distribution of observed events, and 0.5 SD and 1 SEM have been suggested as MCIDs (5). These methods provide a value that indicates a statistical difference, but this may not be perceivable by a patient or clinician. In contrast, anchor-based methods examine the relationship between scores on the assessment instrument and other measures of impaired health (the anchors) (3, 4).
Estimation of an MCID for an instrument should be based on multiple approaches, with the resulting values narrowed down to a single value or small range, based on a process of triangulation (4). When empirically derived triangulated values are not possible, a modified Delphi model has been used to identify a consensus value. The latter illustrates an important underlying component of all MCID estimation—at a key point in its development a value judgment will have been made, even if the data were collected empirically. There is no absolute way of determining an MCID. It is important to recognize that value judgments are not verifiable against external objective standards, only against judgments made by others. It should also be appreciated that MCIDs are average estimates obtained in groups of patients. For an individual patient, a worthwhile perceived benefit may occur below the mean estimated MCID. Until recently, most experience with MCIDs has been in the context of placebo-controlled trials, in which the measured treatment effects may be large, but for trials comparing active treatments, the differences between treatments may be smaller. This does not mean that an MCID should be recalculated for this setting, but it does require a recalibration of the expected size of benefit.
The following sections discuss how COPD-related MCIDs have been defined and validated, and the values of these MCIDs are summarized in Table 1.
| Endpoint | MCID (Improvement) | Method of Estimation | Reference |
|---|---|---|---|
| Lung function | |||
| Trough FEV1 | 100 ml | Anchor-based (exacerbations, patient perception, 2-yr decline in lung function) | 9 |
| Exacerbations | No validated MCID | — | — |
| Dyspnea | |||
| TDI total score | 1 unit | Anchor-based (physician’s global evaluation score), distribution-based (SEM, 0.5 SD), expert preference | 19 |
| UCSD SOBQ | 5 units | Anchor-based (CRQ dyspnea domain, TDI), distribution-based (SEM, Cohen’s effect size), estimate by experienced users | 20 |
| Health status | |||
| SGRQ total score | 4 units | Anchor-based (MRC dyspnea grade, CRQ dyspnea domain, mortality rate), expert and patient preference | 23 |
| CRQ domain scores | 0.5 units (average)* | Anchor-based (patient perspectives), distribution-based (SEM, Cohen’s effect size), expert panel-based | 24 |
| Exercise capacity | |||
| 6-min walk distance | 26 ± 2 m (patients with severe COPD) | Anchor-based (SGRQ, UCSD SOBQ), distribution-based (SEM, Cohen’s effect size, empirical rule effect size) | 31 |
| Incremental shuttle walking test | 47.5 m | Anchor-based (patient perception) | 32 |
| Endurance shuttle walking test | 45–85 s | Anchor-based (patient perception), distribution-based (0.5 SD) | 33 |
| Constant-load cycling endurance tests | 46–105 s | Distribution-based (0.5 SD) | 8 |
| Dyspnea during exercise tests | |||
| Modified Borg scale | 1 unit | Distribution-based (Cohen’s effect size) | 39 |
| Visual analog scale | 10–20 units | Distribution-based (Cohen’s effect size) | 39 |
Improving lung function is not an objective of COPD management (6, 7), but it is the primary endpoint most frequently used by regulatory authorities in interpreting drug efficacy in COPD trials (8).
Opinions on what constitutes an MCID for FEV1 vary. The American Thoracic Society/European Respiratory Society task force has defined a range of 100 to 140 ml (8). Regulators consider a change of 5 to 10% from baseline as clinically important and a change of less than 3% from baseline as not clinically important (8). An MCID of 100 ml for predose or trough FEV1 has been proposed, based on clinical anchoring to endpoints such as exacerbations, perception of dyspnea, and decline in lung function, but not survival (9).
Although trough FEV1 measurements are reproducible, there are issues with repeatability (noise effects can be > 100 ml) (9). Additionally, baseline lung function can affect the potential for improvement, so relative change rather than absolute change may be more meaningful in patients with worse airflow limitation (2).
One of the major problems with determining the MCID for FEV1 is the poor correlation between it and appropriate anchors. Perhaps the best that can be achieved is recognition that in studies where the treatment achieves the current FEV1 consensus MCID, other endpoints achieve their respective MCIDs.
A reduction in frequency of 20% has been suggested as a reasonable MCID for exacerbations, calculated by anchoring exacerbation rates to the St George’s Respiratory Questionnaire (SGRQ) (10). Even with this 20% value, there appears to be a large range in what is considered an important change. Rates between 4.4 and 42.0%, for example, have been associated with meaningful changes in questionnaire-based instruments (11), and if the studies that have influenced the 2011 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines are considered, then statistically significant differences in exacerbation rates between 9 and 53.5% indicate meaningful clinical benefit (12).
The development of an MCID for exacerbations is complicated by the lack of a uniform definition for exacerbations and severity grading as well as underreporting (12, 13). Moreover, the distribution of exacerbation rates is skewed, with seasonal variation (8) and substantial inter- and intrapatient variability in frequency (10, 14). Thus, there is presently no validated MCID for exacerbations; indeed, as the wide range of possible MCIDs indicates, this might not be possible using clinical anchor-based approaches. Perhaps more patient-centered methods might be used, for example using discrete choice modeling techniques in patients.
Dyspnea can be measured by different instruments: the Transition Dyspnea Index (TDI) (15), the modified Medical Research Council (mMRC) scale (16), and the University of California, San Diego Shortness of Breath Questionnaire (17).
The mMRC scale is used widely as a discriminative instrument but has poor evaluative properties to assess changes in dyspnea (18). The TDI is widely used to measure treatment effects; an improvement of 1 or more units in the total score has been defined as the MCID (19). An improvement of 5 units is suggested as a reasonable MCID for the University of California, San Diego Shortness of Breath Questionnaire (20).
In COPD trials, the SGRQ (21) and Chronic Respiratory Questionnaire (CRQ) (22) are widely used. The MCID for the SGRQ is a reduction of 4 units in the total score, estimated using triangulation (23). The average MCID for the CRQ domain scores is 0.5 (24), although the MCIDs for the individual domains differ around this mean estimate (25). The MCID has never, to our knowledge, formally been defined for a total CRQ score.
With older therapies, such as salmeterol/fluticasone or tiotropium, average treatment effects with SGRQ compared with placebo have typically been at or below the MCID (26, 27). More recently, agents such as indacaterol (28, 29) and aclidinium (30) have shown mean SGRQ improvements at or above the MCID, compared with placebo.
The 6-minute walk distance and incremental shuttle walking test are commonly used in COPD (31, 32). A recent study identified an MCID of 26 ± 2 m for the 6-minute walk distance in patients with severe COPD using distribution- and anchor-based estimates (31). The MCID for the incremental shuttle walking test is 47.5 m (32).
Constant work-rate exercise is another measure used to assess exercise capacity in COPD. For the endurance shuttle walking test, an MCID of 45 to 85 seconds has been proposed (33), whereas the American Thoracic Society/European Respiratory Society task force states that for constant-load cycling endurance tests, an improvement of 46 to 105 seconds can be considered clinically important (8). Improvements in endurance time achieved with bronchodilators show large variation (34–36), which may be attributable to variations in patient characteristics and phenotypes, exercise protocols, and study duration (37).This complicates the estimation of a generally acceptable MCID for endurance.
Dyspnea may be measured during exercise tests using the modified Borg scale and the visual analog scale (VAS) (38). A difference of 1 or more units has been proposed as the MCID for the Borg scale, whereas an improvement of 10 to 20 units on the VAS was associated with moderate symptom improvement (39).
In clinical trials, several factors may influence the treatment effect and determine whether it reaches the MCID, but these are factors that determine measured efficacy; they do not alter the threshold change at which a treatment may be judged to be clinically beneficial.
A minimum study duration is important and will vary by outcome. Studies of 6 to 12 months’ duration appear to provide the optimal length to measure patient-reported outcomes. The largest SGRQ effect, compared with placebo, occurs at around 6 months with long-acting bronchodilators (30, 40), although using long-acting β2-agonist plus inhaled corticosteroid combinations, 2 months appeared to be sufficient (41, 42). A duration of 2 to 3 months may be sufficient for assessment of the TDI (30, 40).
Studies longer than 1 year raise complications due to the progressive nature of COPD and the presence of differential dropout. For example, although treatments may produce an initial improvement in SGRQ, scores may subsequently return to the baseline level (or worse) due to disease progression (26, 27). Healthy survivor effects are also important; in the 3-year TOwards a Revolution in COPD Health (TORCH) trial, early study withdrawal was associated with worse baseline lung function and health status, and more frequent exacerbations regardless of treatment allocation (43). Because there are usually more patient withdrawals in the less effective treatment arm, this may lead to a biased estimate of effectiveness.
There is little evidence that MCID attainment is dependent on disease severity. However, treatment effects may vary by baseline disease severity. In a post hoc analysis of the TORCH dataset, the greatest improvement in SGRQ score in patients treated with salmeterol/fluticasone versus placebo was seen in those with the most severe GOLD grade at baseline (44). Similarly, a high dose of indacaterol (300 μg) was seen to have greater benefit in patients with an mMRC grade greater than or equal to 2 at baseline (18).
In theory, baseline severity may affect the attainment of MCID due to floor and ceiling effects (45); however, in most COPD studies baseline values of the most widely used measurements lie in the middle of the scaling range, so this is unlikely to be a major confounding issue.
MCIDs were not developed in the context of specific treatments—the reference point (at least with anchor-based methods) is patient- or clinician-perceived benefit. For that reason, the same MCID should apply, regardless of therapeutic modality. For example, a dyspnea MCID should be the same, whether benefit is achieved through a bronchodilator or through pulmonary rehabilitation. For health status measures it is potentially more complicated, because these instruments measure a wider range of aspects of the disease and a bronchodilator may produce benefit through a different route than an antiinflammatory agent. However, the principle should still apply if the instrument has been developed properly so that undue weight is not given to one area of the disease over another.
Hawthorne effects—changes in behavior due to observation of a participant—may occur on entry to a clinical trial. For example, improved compliance or better inhaler technique with concomitant treatments may improve symptoms in patients receiving placebo. A systematic review of randomized controlled trials of inhaled bronchodilators in patients with COPD suggests that a Hawthorne effect influences SGRQ scores in COPD trials (46). Typically this results in an improvement of 2 to 3 points on the SGRQ with placebo.
A few recent studies have reported changes in placebo-treated patients that exceed the MCID (26, 27). The reason for this is not fully understood but may be related to the socioeconomic status of the country. This in turn will require a large treatment effect for the therapy to be judged worthwhile, a problem that will be exacerbated if there is a sizeable difference in dropout rates (47).
Because an MCID is an average estimate, it should be used as an indicative value rather than an absolute cut-off point between benefit and no benefit (23).
It is useful to consider the approach taken on this issue in other therapeutic areas. For example, an empirically validated noninferiority margin, using measurements from a VAS, has been established to compare treatment effects on endometriosis-associated pelvic pain (48). In rheumatology, the proportion of patients reaching MCIDs across a range of patient-reported outcomes has been used to compare active treatments (49).
One suggested approach for COPD has been to use the threshold for clinical significance and the 95% confidence intervals around the mean treatment effect to categorize the size of treatment response. This analysis produces one of five effects, ranging from “no effect” to a “large clinically significant effect” (Figure 1) (25). However, this method still uses the MCID as a critical hurdle. In a chronic disease this is a major challenge, because it requires at least half of the patients to improve by the MCID. Arguably, this is unrealistic in a disease such as COPD.
Figure 1. Method of categorizing clinical trial results using the threshold for clinical significance and the confidence intervals around the mean treatment effect. The solid line represents the threshold for a minimum clinically significant effect. Reproduced with permission of the European Respiratory Society (Eur Respir J March 2002 19:398–404; doi:10.1183/09031936.02.00063702; Reference 25).
[More] [Minimize]An alternative approach to using means is to use a responder analysis to identify the proportion of patients who improve by more than the MCID. This is attractive because responder rates may remain relatively stable across a range of thresholds (25, 50). Of course, this approach raises the question: what is a worthwhile responder rate? That is, again, a value judgment. Taking the TDI as an example, in recent studies, the responder rate for bronchodilator monotherapy compared with placebo was approximately 10% (30, 51). Using dual bronchodilator therapy, higher responder rates versus placebo have been reported, but the responder rate was still below 20% (52). This illustrates the challenges associated with modern COPD trials that compare combination therapies with monotherapy. It seems unrealistic to expect the incremental gain from adding a second active agent on top of a first to be as great as the difference between monotherapy and placebo and, as a consequence, to expect the additional drug to produce an improvement that exceeds the MCID. In this context, use of responder analyses may shift the debate from a possibly misdirected attempt to redefine the MCID to a lower value for use in active comparator studies toward forming a consensus view about the additional proportion of patients who benefit and what constitutes a worthwhile incremental gain. We propose the term “minimum worthwhile incremental advantage” to describe the percentage of patients who would experience improvement at or above the MCID on adding one treatment to another, or comparing two active treatments.
There has been little new methodology in MCID estimation over the last decade, and few methods are truly patient-centered, but as suggested for exacerbations, new methodologies could be developed. These may also be able to address the question of whether an MCID might vary by severity. A more complex area is linked to the increasing perception that COPD is part of a multisystem disease process. However, the first challenge is the creation of instruments that measure the totality of the disease effect. In theory this is addressed by the use of generic health status instruments, but, although MCIDs are available for these, such instruments are known to be poorly responsive to COPD-specific interventions.
In COPD, validated MCIDs exist for a variety of outcomes, including lung function, dyspnea, health status, and exercise capacity, but there is as yet no validated MCID for exacerbations. Factors such as trial duration, Hawthorne effects, withdrawal rates, and baseline disease severity may affect the size of benefit relative to the MCID in clinical trials, and active comparator trials present additional challenges. MCIDs should be interpreted as indicative, rather than absolute. We suggest that responder analysis is an appropriate method to assess the minimum worthwhile incremental advantage between active treatments.
The authors wrote the manuscript and were assisted in its preparation by Molly Heitz and Maggie Davis, professional medical writers contracted to CircleScience (Macclesfield, UK). The authors thank Donald Banerji (Novartis Pharmaceuticals Corporation, East Hanover, NJ) for reviewing the manuscript.
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Medical writing support for this article was funded by Novartis Pharma AG, Basel, Switzerland.
Author Contributions: All authors were involved in drafting, reviewing, and critically revising the manuscript and approved the final version of the text.
Originally Published in Press as DOI: 10.1164/rccm.201310-1863PP on January 2, 2014
Author disclosures are available with the text of this article at www.atsjournals.org.
