Strengthening Acute Stroke Trials Through Optimal Use of
Disability End Points
Fiona B. Young, BSc; Kennedy R. Lees, MD, FRCP; Christopher J. Weir, PhD; for the Glycine
Antagonist in Neuroprotection (GAIN) International Trial Steering Committee and Investigators
Downloaded from http://stroke.ahajournals.org/ by guest on July 28, 2017
Background and Purpose—Suboptimal choices of primary end point for acute stroke trials may have contributed to
inconclusive results. The Barthel Index (BI) and Rankin Scale (RS) have been widely used and analyzed in various
ways. We sought to investigate the most powerful end point for use in acute stroke trials.
Methods—Data from the Glycine Antagonist in Neuroprotection (GAIN) International Trial were used to simulate 24 000
clinical trials exploring various patterns and magnitudes of treatment effect and thus to estimate the statistical power for
a range of end points based on the BI or RS.
Results—RS end points were more powerful than BI end points. End points dichotomized toward the favorable extreme
of either scale or adjusted according to baseline prognosis (“patient-specific” end point) were among the most powerful.
Combining RS and BI in a “global” end point was also successful. Improvements in statistical power indicated that using
a RS end point instead of BI ⱖ60 could reduce the sample size by up to 84% (95% CI, 80% to 87%), 73% (95% CI,
68% to 79%) for a patient-specific BI end point, or 81% (95% CI, 76% to 85%) for a global end point.
Conclusions—The RS and global end points are preferable to BI end points; the position of the cut point is also important.
Better choices of end point substantially strengthen trial power for a given trial size or allow reduced sample sizes
without loss of statistical power. (Stroke. 2003;34:2676-2680.)
Key Words: clinical trials 䡲 end point determination 䡲 neuroprotection 䡲 stroke, acute 䡲 thrombolysis
M
ost clinical trials in acute stroke have been unsuccessful in demonstrating a positive therapeutic effect.
Neuroprotective trials have likely been underpowered to
detect subtle but clinically important treatment effects. Statistical power is the probability that a statistical test identifies
a significant treatment effect (where one truly exists) at a
given significance level and sample size.1 Inappropriate
choice of cut point for analysis of the outcome scale(s) may
be one of several factors contributing to a lack of statistical
power. Efforts must be made to optimize the analysis of
clinical trials for both ethical and practical reasons.2
A variety of primary end points have been used in acute
stroke trials. The Barthel Index (BI)3 and the modified Rankin
Scale (RS)4 have been the most commonly used disability
outcome measures. The BI is a 10-item scale in which
disability is assessed on various aspects of self-care, such as
dressing and toilet use. It has a maximum score of 100 (fully
independent, physically functioning). The RS is a 6-point
scale in which a patient is rated from 0 (no symptoms) to 5
(severe disability). Both the RS and BI have been shown to be
reliable and valid for use in stroke5; however, the RS may be
less reproducible because of its relative lack of structure.6
To date, functional outcome scores have usually been
dichotomized as favorable versus unfavorable, although there
is little consensus on the optimal cut point,7 and selection of
this is often arbitrary. The most commonly used end point in
published trials has been the BI cut point at 60, at which a
patient is thought to be capable of independence from
full-time care.8 BI cut points have ranged from 55 to 100.5
The RS has been used less frequently, although outcomes of
ⱕ2 (slight or no disability) and ⱕ1 (no significant disability)
have been utilized. A trichotomized BI end point (split into 3
categories) has also been used.9
The BI has a U-shaped distribution, in which patient
outcomes cluster at the extremes. The quarter of patients who
die are arbitrarily scored 0; the 40% who recover are scored
95 or 100. Since the remaining third have BI scores distributed between 5 and 90, any cut point selected within this
range will have a small number of patients populating the
adjacent categories: as few as 5% of the patients may lie 5 or
10 points below a cut point of 60. If it is assumed that a drug
treatment effect will improve patients by only 1 or 2 BI
categories and that not all patients will improve, the potential
to detect such a small shift must be negligible. In contrast,
patients are more heavily represented around BI 95, and here
small improvements applying to a larger number of subjects
may be more readily detected. Clearly, however, dichotomi-
Received March 28, 2003; final revision received July 1, 2003; accepted July 11, 2003.
From the Division of Cardiovascular and Medical Sciences, University of Glasgow, Gardiner Institute, Western Infirmary (F.B.Y., K.R.L.), and
Robertson Centre for Biostatistics, University of Glasgow (C.J.W.), Glasgow, Scotland.
Reprint requests to Fiona B. Young, Division of Cardiovascular and Medical Sciences, University of Glasgow, Gardiner Institute, Western Infirmary,
Glasgow G11 6NT, Scotland. E-mail [email protected]
© 2003 American Heart Association, Inc.
Stroke is available at http://www.strokeaha.org
DOI: 10.1161/01.STR.0000096210.36741.E7
2676
Young et al
Downloaded from http://stroke.ahajournals.org/ by guest on July 28, 2017
zation at this mild end of the scale disadvantages the
contribution of more severely affected patients; on average,
their outcomes will be much poorer, and small but valuable
improvements caused by treatment would not be measured.
To allow both mildly and moderately severely affected
patients to contribute to the significance test, a second cut
point can be added, forming a trichotomized analysis.
Another approach is to use a global end point, simultaneously incorporating outcome measures from different domains such as handicap and activities of daily living. This is
conceptually appealing because no single outcome measure
describes all dimensions of recovery from stroke, yet it has
received limited attention to date. The statistical power of a
global end point should be greater than or equal to that of an
individual end point10 but may be weakened with the inclusion of a scale less influenced by a treatment.
There is considerable heterogeneity in stroke severity;
using an end point with a fixed cut point may render many
patients uninformative. It may be appropriate to group patients according to clinical presentation and to vary cut points
according to group. This “patient-specific” end point would
give a more realistic assessment of a treatment effect and
allow all patients to contribute to the results of the trial.
This article explores the optimal primary end points incorporating the BI and RS. We assessed a selection of end points
used in published trials as well as patient-specific and global
end points. We sought to establish which end point would
perform best under likely trial circumstances. The Oxford
classification11 was used to categorize patients by clinical
presentation in this study.
Methods
Our statistical approach is described in an appendix to this article
(available online at http://stroke.ahajournals.org). Briefly, we based
our work on the patients from the Glycine Antagonist in Neuroprotection (GAIN) International Trial9 data set. The GAIN trial was
neutral; however, to avoid any bias, only the placebo patients were
used. We generated 24 000 clinical trials, each with 1400 patients
split between active treatment and placebo groups (700 per group),
representing 33.6 million randomized patients. Within each trial,
patients were simulated by randomly sampling with replacement
from the GAIN data. The characteristics of every simulated patient
were based on a real example from the GAIN trial, preserving the
correlation between the National Institutes of Health Stroke Scale
(NIHSS),12 Oxford classification, and final outcome described by RS
and BI. The placebo and treatment groups were generated slightly
differently, so that the simulated treatment group was forced to have
slightly milder stroke as assessed by NIHSS at baseline. The
difference between the average NIHSS score for the 2 groups varied
from 0 through 4 points (described as treatment level), but for clarity
our results concentrate on the 2-point difference. This treatment level
is equivalent to a relative risk reduction in being dead or disabled of
9%, an absolute risk reduction of 4%, or an odds ratio of 1.19, with
the use of BI ⱖ60.
The above “fixed” effect was our most basic approach since it
assumes that treatment is uniformly effective in all patients. Consequently, we also simulated effects in which benefit from treatment
was dependent on certain patient characteristics, such as age and sex
(neuroprotective effect, denoted NP); in which a uniform benefit was
offset in a randomly selected subgroup by deterioration to mimic the
effect of thrombolysis (TP1); and finally, an effect that was dependent on patient characteristics, with deterioration in some patients
(TP2). In summary, there were 24 000 trials: 1500 simulated trials
Optimal Disability End Points for Acute Stroke
TABLE 1.
2677
End Points Assessed
Scale
Barthel Index
End Point
ⱖ60 dichotomy
ⱖ95 dichotomy
ⱖ60 and ⱖ95 trichotomy
Patient-specific dichotomy
(LACI, PACI and POCI ⱖ95; TACI ⱖ60)
Rankin Scale
ⱕ2 dichotomy
ⱕ1 dichotomy
ⱕ1 and ⱕ2 trichotomy
Patient-specific dichotomy
(LACI and POCI ⱕ1, PACI and TACIⱕ2)
Global outcome
(1) Dichotomy (BIⱖ95 and RSⱕ1)
(2) Patient-specific dichotomy
(BI: LACI, PACI and POCI ⱖ95; TACI ⱖ60)
(RS: LACI and POCI ⱕ1; PACI and TACIⱕ2)
BI indicates Barthel Index; RS, Rankin Scale; LACI, lacunar infarction; PACI,
partial anterior circulation infarct; POCI, posterior circulation infarct; TACI, total
anterior circulation infarct.
for each of 4 treatment effects and 4 treatment levels, with every trial
involving 1400 patients.
End Points
Published cut points were used when we dichotomized or trichotomized the BI and/or RS (Table 1). We also explored patient-specific
cut points, in which we specified different thresholds for favorable
outcome according to baseline prognosis, using the Oxford classification to group patients. We chose thresholds that were close to the
median value of BI or RS achieved by each Oxford classification
category in the original GAIN trial.
Estimation of Statistical Power
We analyzed the simulated trials via Pearson’s ␹2 test for dichotomized end points and the Cochran-Mantel-Haenszel ␹2 test13 for
trichotomized end points. The global end points were analyzed via
generalized estimating equations.14 A bootstrap approach was used
to calculate CIs for the power.
The end points were compared by calculating the sample size that
would be required to maintain the same statistical power when 1 end
point was chosen in preference to BI ⱖ60 with the use of standard
sample size equations.15–17 If an end point were more powerful, the
required sample size expressed as a percentage would be ⬍100%.
For an overall comparison of the end points, binary logistic regression was used to model the proportion of significant trials, adjusted
for treatment effect size.
Results
The pattern of results we observed was similar across all
treatment effect patterns for both the RS and BI end points
(Table 2). The NP effect and the TP2 effect could be detected
with the lowest power. The BI ⱖ60 dichotomy was consistently
the least powerful end point. Among the remaining BI end
points, the ⱖ95 dichotomy and the patient-specific dichotomized end points were equally the most powerful (Figure). The
RS end points followed a less consistent pattern. The RS ⱕ2 end
point was the least powerful for all treatment effect patterns; end
points incorporating RS ⱕ3 were no better (data not shown).
Depending on the treatment effect pattern, the RS ⱕ1, the RS
ⱕ1 and ⱕ2 trichotomy, or the dichotomized patient-specific end
2678
Stroke
TABLE 2.
Statistical Power Obtained for Each End Point
Scale
Barthel Index
Rankin Scale
Global end point
November 2003
Fixed
NP
TP1
ⱖ60
End Point
0.353 (0.329, 0.378)
0.135 (0.118, 0.153)
0.170 (0.151, 0.189)
0.103 (0.087, 0.118)
TP2
0.881 (0.865, 0.898)
Fixed*
ⱖ95
0.639 (0.614, 0.663)
0.263 (0.241, 0.286)
0.431 (0.406, 0.456)
0.241 (0.219, 0.262)
0.986 (0.980, 0.992)
ⱖ60 and ⱖ95
0.565 (0.540, 0.590)
0.212 (0.191, 0.233)
0.309 (0.286, 0.333)
0.169 (0.150, 0.188)
0.980 (0.973, 0.987)
PS dichotomy
0.575 (0.550, 0.600)
0.265 (0.243, 0.288)
0.357 (0.333, 0.382)
0.261 (0.238, 0.283)
0.974 (0.966, 0.982)
ⱕ2
0.703 (0.680, 0.726)
0.233 (0.212, 0.255)
0.455 (0.429, 0.480)
0.259 (0.237, 0.282)
0.997 (0.995, 1.000)
ⱕ1
0.760 (0.738, 0.782)
0.270 (0.248, 0.292)
0.613 (0.588, 0.637)
0.412 (0.387, 0.437)
0.962 (0.952, 0.972)
ⱕ1 and ⱕ2
0.779 (0.758, 0.800)
0.272 (0.249, 0.295)
0.575 (0.550, 0.600)
0.369 (0.345, 0.394)
0.994 (0.990, 0.998)
PS dichotomy
0.735 (0.713, 0.758)
0.277 (0.254, 0.299)
0.559 (0.534, 0.584)
0.363 (0.338, 0.387)
0.982 (0.975, 0.989)
Global dichotomy
0.767 (0.746, 0.789)
0.291 (0.268, 0.314)
0.577 (0.552, 0.602)
0.386 (0.361, 0.411)
0.990 (0.985, 0.995)
Global PS dichotomy
0.715 (0.692, 0.738)
0.294 (0.271, 0.317)
0.505 (0.480, 0.531)
0.365 (0.340, 0.389)
0.993 (0.988, 0.997)
Power levels for a treatment effect equivalent to a 2-point shift in baseline NIHSS. For BIⱖ60 this is approximately OR⫽1.19, RRR⫽9%, ARR⫽4%.
Parentheses contain the 95% confidence interval for the power.
PS indicates patient specific; NP, neuroprotective treatment effect; TP1 and TP2, thrombolytic treatment effects.
* Power levels achieved with a fixed 3-point shift in baseline NIHSS, for the BIⱖ60 end point this is approximately OR⫽1.42, RRR⫽18%, ARR⫽9%.
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point was the most powerful. The range of power was narrower
for the RS end points than the BI end points.
Both the dichotomized and patient-specific global end points
were more powerful than the BI end points for all treatment
effect patterns but not always more powerful than RS ⱕ1 or the
RS ⱕ1 and ⱕ2 trichotomy. Generally, the patient-specific
global end point was less powerful than the dichotomized global
end point.
Table 3 compares the end points in terms of required sample
sizes relative to BI ⱖ60. For the BI end points, the greatest
sample size reduction was obtained under the TP2 effect and the
patient-specific end point or the BI ⱖ95 end point. The RS end
points generally had larger sample size reductions. Either of the
global end points could reduce the sample sizes even further,
depending on the underlying treatment effect pattern.
Overall, the RS end points were more powerful than the BI
end points (Table 4). The odds of achieving a statistically
significant result increased by 89% under a fixed treatment
effect if a RS end point were used instead of a BI end point.
Discussion
Our results have important implications for the choice of
primary end point in acute stroke trials. Primary end points
that include the RS are more powerful than those based on the
Overall comparison of end points. End points are ordered from
least to most powerful from left to right, and lines represent differences that were not statistically significant. PS indicates
patient-specific; Global 1, dichotomy; and Global 2, PS
dichotomy.
BI. The position of the cut point on these scales is also of
great importance; end points dichotomized toward the favorable extreme were more powerful. The patient-specific BI
and the trichotomized RS also performed well.
Our analyses were performed with a range of treatment
effects, and our findings are reasonably consistent across a likely
range of trial conditions. However, since all analyses used the
GAIN International database, applying the end points to an
independent data set may be informative.
Broderick and colleagues18 used National Institute of Neurological Disorders and Stroke (NINDS) trial data and established
that the RS dichotomized at ⱕ1 was the most effective in
differentiating between the treatment groups in that trial. The BI
dichotomized at ⱖ95 was also effective. However, since such an
analysis is data dependent, it may not be generalizable. An
analysis that relies solely on choosing positive end points from
a selection of trials in which putative effects may have been seen
is subject to selection bias and random variability. Our method
involves assumptions about the generation of the treatment
effect (since it assumes that outcome at 90 days is related to
initial stroke severity). We used a sampling-based approach in
which the “simulated” outcomes at 90 days were generated by
selecting outcomes from the GAIN International database; real
patient outcomes were used, and the correlation structure between the outcome measures was retained. By simulating 1500
trials of each treatment scenario (equivalent to 33.6 million
patients), we achieved accurate estimates of statistical power.
Dichotomization may be less sensitive than trichotomized end
points, global end points, or patient-specific end points.19 Berge
and Barer20 supported the separate definition of favorable
outcome for subgroups of patients to maximize the power of
stroke trials. Patient-specific end points would ensure that trial
results are generalizable across a wide range of stroke severity.
Our cut points were chosen on the basis of the Oxford classification category; further work is required to assess more appropriate methods of selecting the cut points.
The inclusion of only the BI and RS in the global end points
may have restricted the power. These outcome measures are
highly correlated; the full potential of a global end point to assess
many different dimensions of recovery was not exploited. The
Young et al
TABLE 3.
Optimal Disability End Points for Acute Stroke
2679
Comparison of End Points to >60 Dichotomy in Terms of a Percentage Sample Size
Endpoint
Fixed (%)
NP (%)
TP1 (%)
TP2 (%)
100
100
100
100
BIⱖ60 dichotomy
BIⱖ95 dichotomy
46.9 (44.1, 49.6)
41.9 (35.6, 48.1)
31.4 (27.6, 35.3)
28.6 (22.4, 34.6)
BIⱖ60 and ⱖ95 trichotomy
56.4 (53.1, 59.9)
51.3 (44.0, 58.7)
44.9 (39.4, 50.1)
40.7 (32.7, 48.7)
BI PS dichotomy
53.4 (50.3, 56.6)
41.3 (35.0, 57.6)
39.0 (34.3, 43.7)
26.7 (21.0, 32.6)
RSⱕ2 dichotomy
42.1 (39.7, 44.6)
45.9 (39.1, 52.7)
29.4 (25.9, 33.1)
26.0 (20.4, 31.6)
RSⱕ1 dichotomy
34.7 (32.7, 36.6)
38.1 (32.4, 44.0)
20.0 (17.5, 22.4)
16.0 (12.3, 19.9)
RSⱕ1 and ⱕ2 trichotomy
34.0 (32.1, 35.9)
37.0 (31.4, 42.6)
21.4 (18.7, 24.0)
17.9 (13.7, 22.0)
RS PS dichotomy
37.0 (34.9, 39.0)
39.7 (33.7, 45.9)
22.6 (19.9, 25.4)
18.4 (14.3, 22.7)
Global dichotomy
35.6 (33.6, 37.4)
35.3 (29.9, 40.7)
22.0 (19.3, 24.7)
18.6 (14.3, 23.0)
Global PS dichotomy
38.9 (36.7, 41.0)
35.6 (30.0, 41.0)
25.7 (22.6, 29.0)
19.3 (14.9, 23.9)
None of the sample size percentages included 100 in the 95% CI.
BI, indicates Barthel Index; RS, Rankin Scale; PS, patient specific; NP, neuroprotective treatment effect; TP1 and TP2, thrombolytic
treatment effects.
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inclusion of other outcome measures such as the NIHSS may
further improve the power, as used in the NINDS trial.21
However, some regulatory authorities, such as the European
Medicines Evaluation Authority, have been reluctant to consider
a global end point that combines diverse outcome measures.22
Most stroke trials are powered to detect an absolute risk
reduction of 10%. This study used a treatment effect level that
was equivalent to an absolute risk reduction of 4% (BI ⱖ60,
fixed effect). We believe that this is a more realistic effect of a
stroke intervention. This has resulted in levels of statistical
power substantially below 80%, suggesting that the sample size
of 1400 is too small. The final column in Table 2 shows the
power that was achieved when a 3-point decrease in baseline
NIHSS was applied (absolute effect of 9%). With this larger
treatment effect, the power for all end points exceeds 80%, and
although the absolute differences among the end points are
smaller, the hierarchy is unchanged.
Treatment effect patterns influence study power. This may
have been underestimated in stroke trial design. When treatment
benefit is restricted to subgroups, lower power is observed
because the average benefit is diluted by nonresponders. For
example, our NP effect restricted the benefit for elderly women,
and the overall absolute risk reduction was reduced to 2%.
Three trials have demonstrated a positive therapeutic effect in
acute stroke: the NINDS recombinant tissue plasminogen activator (rtPA) trial,21 Stroke Treatment With Ancrod Trial
(STAT),23 and Prolyse in Acute Cerebral Thromboembolism
(PROACT) II.24 None of those trials used the most commonly
published end points. The NINDS trial used a global end point
TABLE 4.
Comparison of BI and RS End Points
Treatment
Effect
RS vs BI
(odds ratio)
Fixed
1.890
NP
1.416
TP1
2.091
TP2
2.079
Note: all odds ratios significant at a⫽0.05.
BI, indicates Barthel Index; RS, Rankin Scale; NP, neuroprotective treatment
effect; TP1 and TP2, thrombolytic treatment effects.
incorporating the BI (ⱖ95), RS (ⱕ1), NIHSS (ⱕ1), and Glasgow Outcome Scale25 (⫽1). PROACT II used RS ⱕ2, and the
STAT study used BI ⱖ95 or score equal to prestroke value. It is
notable that these end points were among the most powerful we
assessed. However, a post hoc analysis of the European Cooperative Acute Stroke Study (ECASS) II trial26 found that if RS
ⱕ2 had been used instead of RS ⱕ1, the trial would have been
positive.
It is not only the choice of end point that can influence the
power of a clinical trial: validity of outcome measures and
restrictive entry criteria may also be factors. STAT and NINDS
both restricted time to treatment to 3 hours. PROACT II
restricted entry to patients with proven middle cerebral artery
occlusion.
We have demonstrated the disadvantage of BI ⱖ60 as a
primary end point. Trials that are currently in progress should
consider revisions to their statistical analysis plan before unblinding takes place to optimize statistical power. Such a
decision has recently been announced by the international
Intravenous Magnesium Efficacy in Stroke (IMAGES) trial
group.27
In conclusion, this study has shown that many clinical trials in
acute stroke have not used an optimal primary end point, which
may have led to inconclusive results. Statistical power is not
sufficient to render a trial informative, but it may be a prerequisite. Substantial and significant increases in power are observed when a dichotomized end point cut at the favorable
extreme of the BI or RS, a patient-specific end point, or a global
end point is used. On average, RS end points appear more
powerful than BI end points, whether analyzed alone or as part
of a global end point.
Acknowledgments
This study was supported by a collaborative studentship from the
Medical Research Council and Pfizer to F.B. Young and by a Medical
Research Council career development fellowship to Dr Weir. The GAIN
trial was sponsored by GlaxoWellcome (now GlaxoSmithKline). GlaxoSmithKline had no involvement in this analysis or article.
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Stroke. 2003;34:2676-2680; originally published online October 16, 2003;
doi: 10.1161/01.STR.0000096210.36741.E7
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