F E ATU R E D
ARTICLE
Tracking Motor Impairments in the Progression of Huntington’s Disease
Jeffery D. Long, PhD,1,2 Jane S. Paulsen, PhD,1,3,4* Karen Marder, MD, MPH,5 Ying Zhang, PhD,2 Ji-In Kim, PhD,1
James A. Mills, MS,1 and the Researchers of the PREDICT-HD Huntington’s Study Group
1
Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USA
3
Department of Neurology, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
4
Department of Psychology, College of Liberal Arts and Sciences, University of Iowa, Iowa City, Iowa, USA
5
Departments of Neurology and Psychiatry, Sergievsky Center and Taub Institute, College of Physicians and Surgeons, Columbia University,
New York, New York, USA
2
ABSTRACT:
The Unified Huntington’s Disease
Rating Scale is used to characterize motor impairments
and establish motor diagnosis. Little is known about the
timing of diagnostic confidence level categories and the
trajectory of motor impairments during the prodromal
phase. Goals of this study were to estimate the timing of
categories, model the prodromal trajectory of motor
impairments, estimate the rate of motor impairment
change by category, and provide required sample size
estimates for a test of efficacy in clinical trials. In total,
1010 gene-expanded participants from the Neurobiological Predictors of Huntington’s Disease (PREDICT-HD) trial
were analyzed. Accelerated failure time models were
used to predict the timing of categories. Linear mixed
effects regression was used to model the longitudinal
motor trajectories. Age and length of gene expansion
were incorporated into all models. The timing of categories varied significantly by gene expansion, with faster
progression associated with greater expansion. For the
median expansion, the third diagnostic confidence level
Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease caused by the trinucleotide expansion of cytosine-adenine-guanine (CAG)
in exon 1 of the huntingtin (HTT) gene.1 HD is asso-
category was estimated to have a first occurrence 1.5
years before diagnosis, and the second and first categories were estimated to occur 6.75 years and 19.75 years
before diagnosis, respectively. Motor impairments displayed a nonlinear prodromal course. The motor impairment rate of change increased as the diagnostic
confidence level increased, with added acceleration for
higher progression scores. Motor items can detect
changes in motor impairments before diagnosis. Given a
sufficiently high progression score, there is evidence that
the diagnostic confidence level can be used for prodromal staging. Implications for Huntington’s disease
research and the planning of clinical trials of efficacy are
C 2013 International Parkinson and Movediscussed. V
ment Disorder Society
K e y W o r d s : movement disorders; Huntington’s disease; neurodegenerative disease; predictive testing;
cohort studies
ciated with severe motor, cognitive, and psychiatric
impairments that typically develop in adulthood.2
Although HD onset is characterized by a tripartite of
symptoms and signs, diagnosis is based on motor
------------------------------------------------------------------------------------------------------------------------------
*Correspondence to: Dr. Jane S. Paulson, The University of Iowa College of Medicine, 1–305 Medical Education Building, Iowa City, IA 52242; predictpublications@uiowa.edu
Funding agencies: This research is supported by the National Institutes of Health (NIH), the National Institute of Neurological Disorders and Stroke
(NS040068), the CHDI Foundation, Inc. (A3917), Cognitive and Functional Brain Changes in Preclinical Huntington’s Disease (HD) (5R01NS054893), 4D
Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington’s (1U01NS082086), Functional Connectivity in Pre-manifest Huntington’s
Disease (1U01NS082083), and Basal Ganglia Shape Analysis and Circuitry in Huntington’s Disease (1U01NS082085). The publication was supported by
the National Center for Advancing Translational Sciences and the NIH through grant 2 UL1 TR000442-06. The content is solely the responsibility of the
authors and does not necessarily represent the official views of the NIH.
Relevant conflicts of interest/financial disclosures: Nothing to report.
Full financial disclosures and author roles may be found in the online version of this article.
Received: 3 May 2013; Revised: 23 July 2013; Accepted: 31 July 2013
Published online 21 October 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/mds.25657
Movement Disorders, Vol. 29, No. 3, 2014
311
L O N G
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A L .
impairments, and endpoints for clinical trials focus
mainly on motor signs.3,4 The main measure of motor
impairments is the motor assessment section of the Unified Huntington’s Disease Rating Scale (UHDRS),5
which is administered by a trained examiner. The first
part of the motor section consists of 31 items in 15
domains of motor impairment, each rated on a 5-point
scale ranging from 0 (normal) to 4 (severest impairment). The motor items are distributed among oculomotor functioning (6 items), chorea (7 items), dystonia
(5 items), bradykinesia (11 items), and rigidity (2
items).6 In many studies of HD, interest is in the sum of
all 31 items, which is referred to as the total motor
score (TMS).2,7-9
The second part of the motor assessment section consists of the diagnostic confidence level (DCL), which is a
single item with a 5-category ordinal rating scale. The
examiner selects a response to the question, “To what
degree are you confident that this person meets the
operational definition of the unequivocal presence of an
otherwise unexplained extrapyramidal movement disorder (eg, chorea, dystonia, bradykinesia, rigidity) in a
subject at risk for Huntington’s disease?” The rating
categories are 0 (normal; no abnormalities), 1 (nonspecific motor abnormalities; < 50% confidence), 2 (motor
abnormalities that may be signs of HD; 50%-89% confidence), 3 (motor abnormalities that are likely signs of
HD; 90%-98% confidence), and 4 (motor abnormalities that are unequivocal signs of HD; 99%
confidence).
Despite widespread use of the DCL and the TMS,
many questions remain regarding their ability to track
progression, especially in the prodromal period of HD
(ie, in the years before DCL 5 4). The response format
of the DCL suggests that the item can be used as a type
of progression staging, but little is known about the
timing of the occurrences of the categories. The trajectory of the TMS is well characterized for later stages of
HD (ie, the years after first DCL 5 4)10-13; however, the
nature of the prodromal TMS trajectory is unclear.
There is also lack of information regarding the relation
between the DCL and the TMS. For example, given a
cross-section of progression, there is scarce information
regarding what constitutes typical TMS levels for DCL
categories. Furthermore, it is possible that individuals
remain for a time within a DCL category, and it is of
interest to examine how the TMS changes during this
time.
The issues raised above can be addressed with longitudinal data that span the prodromal phase of HD.
Such data are provided by the Neurobiological Predictors of Huntington’s Disease (PREDICT-HD) study.2,14
Using the PREDICT-HD database, the current study
had the following aims: First, examine the timing of
DCL categories in progression. Second, examine change
in motor impairment (TMS and the motor factors) from
the prodromal period (DCL < 4) through the manifest
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Movement Disorders, Vol. 29, No. 3, 2014
period (DCL 5 4). Third, estimate typical mean levels of
rates of annual change in motor impairments for DCL
categories. Fourth, compute estimated required sample
sizes for a hypothetical clinical trial of efficacy examining change in TMS. Age and CAG length are important
factors in motor onset and are incorporated into the
methods used to address the goals.
Participants and Methods
Participants
Participants were N 5 1010 individuals who voluntarily underwent genetic testing and were found to
have a CAG repeat length > 36 (mean 5 42.35,
median 5 42, minimum 5 37, maximum 5 62). A subset of N 5 21 participants were diagnosed at study
entry. Another subset of N 5 204 “converters”
received a diagnosis at some point after study entry.
Enrollment was at sites in the United States, Australia, the United Kingdom, Canada, Germany, and
Spain. Institutional review boards at each participating
site approved the study, and each participant signed
an informed consent. At study enrollment, participants
were required to be at least 18 years old. Exclusion
criteria included history of a significant developmental
cognitive disorder, other central nervous system disease or injury, evidence of an unstable medical or psychiatric illness (including substance abuse), a
pacemaker or metallic implants, prescribed antipsychotic medication in the last 6 months, or phenothiazine derivative antiemetic medication in the last 3
months. For additional details, see Paulsen et al.2
Clinical Assessments
Participants underwent detailed motor, cognitive,
psychiatric, and functional evaluations at baseline and
annually thereafter. Participants had data for the TMS
and DCL for at least one visit up to a maximum of 10
visits. There was a small amount of missing data
(approximately 1%), consisting of unanswered TMS
items, which were assigned a value of zero
(“normal”). Preliminary analysis (not presented) indicated that missing values did not appear to cause outliers or other unexpected values. There were 88 motor
examiners, and the mean number of examinations per
examiner was 49.43 (standard deviation 5 61.99); and
the quartiles (Q) were Q1 5 2, Q2 5 16, and Q3 5 76.
In addition, 60.83% of participants had the same rater
throughout, 23.64% had two raters, and 15.53% had
three or more raters. Findings did not change based
on the number of raters or examinations. At study
entry, the site coordinator responded to question 81 of
the UHDRS: “Does the motor rater know the participant’s gene status? (0 5 no, 1 5 yes).” Gene mutation
status was reported as be known (UHDRS question
M O T O R
I M P A I R M E N T S
81 5 “yes”) for 69% of participants. The findings did
not vary based on blinding.
H D
P R O G R E S S I O N
TABLE 1. Demographic information for the sample
Converters,
N 5 204
Statistical Methods
The first goal of estimating the timing of the DCL
categories was addressed by using a Weibull accelerated failure time (AFT) model.15 The same AFT model
was used with four different events: first occurrence of
DCL 5 1, 2, 3, or 4. The log time to each DCL category was predicted separately, with age as the time
metric and with CAG and CAG-squared as the predictors. CAG-squared was included because of evidence
that the relation between time of onset and CAG is
nonlinear.16,17 The outcome for each participant was
treated as left-censored, right-censored, or intervalcensored. The age at first occurrence for each category
was the predicted value from the fitted model stratified
by CAG, and the distance between the predicted values was computed for the different categories. The
standard error of prediction was used to compute
95% confidence intervals (CIs).
The second aim of examining motor impairment trajectories was addressed using only the converters. For
each converter, time in the study (in years) was anchored to the first occurrence of a DCL 5 4 (diagnosis).
This was accomplished by subtracting the year at first
occurrence from each year (zero indicated the time of
diagnosis). The trajectory of the TMS and motor factors was estimated using cubic splines in linear mixed
effects regression (LMER).18,19 The TMS and each
motor factor were modeled separately. Time predictors were cubic splines with the quartiles of time as
the knots, and a random effect was included for each
spline term plus the intercept. Age and CAG were
incorporated into the analysis using the age-CAG
product (CAP)20 as a predictor. The CAP is computed
as CAP 5 (age at study entry) 3 (CAG 2 33.66) and is
similar to the burden score reported by Penney et al.21
The CAP is a purported index of the cumulative toxicity of mutant huntingtin at the time of study entry.
For reference, CAP 368 denotes a “high” probability
of converting in the near future after study entry,
290 < CAP < 368 denotes a “medium” probability,
and CAP 290 denotes a “low” probability.
The third aim concerned estimating the level and
slope of motor impairments for DCL categories. All
gene-expanded participants were included, except for
those with a baseline diagnosis (21 excluded patients).
LMER was used again, with predictors being dummy
variables for time-varying DCL categories and CAP.
To help inform future clinical trials, the zero point of
time was anchored to study entry, so that time represented years in the study. All possible interactions
among CAP, the dummy variables, and time were
included as predictors. Six correlated random effects
were specified, one for each DCL category dummy
I N
Non-converters,
N 5 806
Combined,
N 5 1010
Variable
Mean
SD
Mean
SD
Mean
SD
Agea
Education
CAG repeat length
CAPb
Womenc
43.99
14.14
43.24
402.14
0.66
10.26
2.59
2.78
76.58
0.47
40.13
14.58
42.01
322.58
0.61
9.94
2.63
2.30
72.55
0.49
41.17
14.46
42.34
343.70
0.63
10.17
2.63
2.50
81.59
0.48
a
The age indicated is the age at study entry.
Age-CAG product (CAP) is computed as: CAP 5 (age at study entry) 3
(CAG 2 33.66).
c
Values indicate the proportion of women.
Abbreviations: SD, standard deviation; CAG, cytosine-adenine-guanine.
b
variable and time. Because of the relevance for planning clinical trials of efficacy, emphasis was on the
hypothesis test of zero slope. Efficacy trials require a
detectible change in the untreated group to have the
potential for demonstrating a treatment effect. Detectible change was defined as a slope significantly different from zero.
Finally, the required sample size for clinical trials of
efficacy was computed based on LMER models.
Single-arm sample size was computed for the test of
equal TMS slopes for hypothetical placebo and treatment groups. Additional details are presented in the
Appendix.
Results
Table 1 provides demographic information for the
larger sample and subsamples of converters and nonconverters. Converters had substantially higher mean
CAP than non-converters.
The results of the DCL timing analysis are illustrated in Figure 1, which depicts the predicted year
before diagnosis of a DCL category (color-coded) as a
function of CAG, with 95% CIs. Although the entire
CAG distribution was used for the analysis, the quartiles of the gene-positive CAG distribution were used
for graphing, which were Q1 5 41, Q2 5 42, and
Q3 5 44. The horizontal axis indicates the years to
diagnosis, with diagnosis occurring at year zero
(marked by a solid vertical line). CAG-squared was
statistically significant in the prediction of all DCL
categories, but its effect was greatest for DCL 5 4
(P < 0.001). For the median CAG 5 42, DCL 5 3 was
predicted to occur 1.50 years before diagnosis (95%
CI, 0.23-2.77), DCL 5 2 was predicted to occur 6.75
years before diagnosis (95% CI, 5.62-7.88), and
DCL 5 1 was predicted to occur 19.74 years before
diagnosis (95% CI, 18.65-20.83). Figure 1 reflects the
significance of CAG on the timing of the categories.
For CAG 5 41, the predicted occurrence of the categories was further from diagnosis. For CAG 5 44, the
Movement Disorders, Vol. 29, No. 3, 2014
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L O N G
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FIG. 1. The predicted year of diagnostic confidence level (DCL) category occurrence is illustrated with 95% confidence interval as a function of
cytosine-adenine-guanine (CAG) repeat length. A vertical line denotes the time of motor diagnosis (DCL 5 4).
predicted occurrence of the categories was closer to
diagnosis, and the 95% CI for DCL 5 3 overlapped
with DCL 5 4 (the vertical line at zero is contained in
the CI).
Converter trajectories of motor impairments are
indicated in Figure 2. Individual empirical curves are
the jagged gray lines, and the fitted spline curves
stratified by CAP are the smooth colored lines. The
data thinned over time because the study is ongoing,
and many of the individuals depicted in the graphs do
not yet have many follow-up visits. The data thinning
is reflected in the decreasing acceleration at the
extreme right-hand side of each graph. Intercept (starting level) varied significantly by CAP for all motor
outcomes (all P < 0.001). The motor trajectories were
not statistically different by CAP strata for chorea and
rigidity, but they were significantly different for the
remaining variables in Figure 2 (all remaining
FIG. 2. Trajectories of motor impairments for converters are illustrated as a function of age-CAG product (CAP). Light gray lines are the empirical
trajectories of the participants, and colored lines are the fitted spline model curves. A vertical line denotes the time of motor diagnosis (diagnostic
confidence level 5 4).
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Movement Disorders, Vol. 29, No. 3, 2014
M O T O R
TABLE 2. Linear mixed effects regression results: total
motor score slopes and mean at year 2 by age-CAG product and diagnostic confidence level
Mean (standard error)
CAP
DCL
Slope
Mean TMS at year 2
285
285
285
285
285
290
290
290
290
290
310
310
310
310
310
350
350
350
350
350
400
400
400
400
400
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
0.0381 (0.0485)
0.0919 (0.0494)
0.1638 (0.0902)
0.5396 (0.1567)a
1.227 (0.1897)a
0.0414 (0.0475)
0.0894 (0.0479)
0.1732 (0.0871)b
0.5542 (0.1517)a
1.2811 (0.1848)a
0.0544 (0.0459)
0.0793 (0.0435)
0.211 (0.0762)c
0.6129 (0.1329)a
1.4974 (0.1655)a
0.0806 (0.0529)
0.0593 (0.0443)
0.2865 (0.0663)a
0.7302 (0.105)a
1.9301 (0.1321)a
0.1133 (0.0745)
0.0342 (0.0607)
0.3809 (0.0817)a
0.8768 (0.1014)a
2.4709 (0.108)a
1.0632 (0.1023)a
4.8179 (0.135)a
8.4232 (0.3209)a
11.213 (0.6121)a
13.4854 (1.1873)a
1.088 (0.1012)a
4.8739 (0.1315)a
8.5255 (0.3112)a
11.3388 (0.5938)a
13.5992 (1.1537)a
1.1876 (0.0996)a
5.0978 (0.1205)a
8.9347 (0.2763)a
11.8418 (0.5244)a
14.0545 (1.0242)a
1.3867 (0.1114)a
5.5457 (0.1169)a
9.753 (0.234)a
12.848 (0.4117)a
14.9651 (0.8016)a
1.6356 (0.1462)a
6.1055 (0.1462)a
10.776 (0.2531)a
14.1056 (0.3621)a
16.1032 (0.6554)a
a
P < 0.001.
P < 0.01.
P < 0.05.
Abbreviations: TMS, total motor score; CAP, age-CAG product; DCL, diagnostic confidence level.
b
c
P < 0.001). Although the entire CAP distribution was
used in the analysis, curves for the quartiles of the
converters were used for graphing (Q1 5 360,
Q2 5 405, and Q3 5 450). Negative values on the horizontal axis indicate years to diagnosis, and positive
values indicate years after diagnosis (0 5 time of diagnosis). The top left graph depicts the TMS trajectory,
and the remaining graphs illustrate the trajectories of
the motor factors.
Table 2 lists the LMER results from the analysis
examining change in motor impairments by CAP and
DCL category. The results are presented only for
TMS, but the motor factors had similar patterns. The
column headed “Slope” in Table 2 lists the estimated
mean rate of annual change for the TMS (along with
the standard error). The column headed “Mean TMS
at year 2” lists the predicted mean TMS after 2 years
in the study. The 2-year point corresponds with the
power analysis discussed in the Appendix. Although
the entire CAP distribution was used in the analysis,
Table 2 shows results only for limited values, including the quartiles (Q1 5 285, Q2 5 350, and Q3 5 400).
I M P A I R M E N T S
I N
H D
P R O G R E S S I O N
The rate of change was statistically greater than zero
for DCL 5 3 or 4, regardless of CAP. CAP 5 290 was
the minimum that produced slope significance for
DCL 5 2 at the P < 0.05 level, and CAP 5 310 was the
minimum for the P < 0.01 level. As indicated in the
“Mean TMS at year 2” column, all mean estimates
were statistically greater than zero at the P < 0.001
level.
Table 3 indicates the estimated required sample size
(in boldface) for a hypothetical 2-year clinical trial of
efficacy with measurements every 6 months. The
single-arm sizes are for testing the null hypothesis of
equal TMS rate of change over time for placebo and
treatment groups (see Appendix). Minimum CAP indicates sampling all available participants with
CAP minimum CAP, and dropout refers to the rate
in each group (see Appendix).
Discussion
The results of this analysis provide information concerning the timing of DCL categories in HD progression and the nature of change in motor impairments.
There is evidence the DCL and the UHDRS motor
items can detect changes in motor impairments during
the HD prodromal period. Age and CAG repeat length
were significant both for the timing of DCL categories
and for change in motor impairments. Greater CAG
length was associated with more rapid DCL progression, and higher CAP was associated with faster
change in motor impairments.
Trajectories of motor impairments were characterized
by nonlinear change in the prodromal phase (see Fig. 2).
The more targeted analysis of TMS change while in the
same DCL category provides an indication of how
annual rates of change in motor impairments vary by
DCL and CAP (see Table 2). There was no significant
change associated with DCL 5 0 (normal) or DCL 5 1
(nonspecific motor abnormalities) regardless of CAP.
DCL 5 2 (possible signs of HD; 50%-89% confidence)
showed significance for a minimum CAP 5 290 or 300,
depending on the desired level of significance. DCL 5 3
(likely signs of HD; 90%-98% confidence) and DCL 5 4
(unequivocal signs of HD; 99% confidence) showed
significance regardless of CAP, suggesting that a clinical
examination with corroborative history of HD may be a
sufficient entry criterion.
The finding that motor impairment slopes increased
with DCL provides evidence that the stage of disease
is related to the rate of progression, even during prodromal HD. The pattern of progression for our results
is consistent with a long-term slow build-up of impairments that ultimately gives rise to an accelerated trajectory of deterioration that varies by CAP (see Fig.
2). This pattern has been verified in other cohorts of
HD patients.5,10,11,13,22 For example, Mahant and
Movement Disorders, Vol. 29, No. 3, 2014
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L O N G
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A L .
TABLE 3. Required sample size (boldface) for a hypothetical clinical trial of efficacya
Parameter Estimates
Effect Size
Dropout, %
Min CAP
Type I error, %
bS
g11
g12
g22
r2e
30%
40%
50%
60%
70%
0
0
0
0
0
0
10
10
10
10
10
10
20
20
20
20
20
20
290
350
400
290
350
400
290
350
400
290
350
400
290
350
400
290
350
400
5
5
5
10
10
10
5
5
5
10
10
10
5
5
5
10
10
10
1.56
2.04
2.81
1.56
2.04
2.81
1.56
2.04
2.81
1.56
2.04
2.81
1.56
2.04
2.81
1.56
2.04
2.81
21.40
25.96
32.11
21.40
25.96
32.11
21.40
25.96
32.11
21.40
25.96
32.11
21.40
25.96
32.11
21.40
25.96
32.11
4.45
5.61
6.56
4.45
5.61
6.56
4.45
5.61
6.56
4.45
5.61
6.56
4.45
5.61
6.56
4.45
5.61
6.56
3.20
3.67
5.26
3.20
3.67
5.26
3.20
3.67
5.26
3.20
3.67
5.26
3.20
3.67
5.26
3.20
3.67
5.26
16.54
19.39
24.63
16.54
19.39
24.63
16.54
19.39
24.63
16.54
19.39
24.63
16.54
19.39
24.63
16.54
19.39
24.63
552
377
263
402
275
192
591
404
281
431
295
205
636
435
303
464
317
221
310
212
148
226
155
108
332
227
158
242
166
115
358
245
170
261
178
124
199
136
95
145
99
69
213
145
101
155
106
74
229
157
109
167
114
79
138
94
66
101
69
48
148
101
70
108
74
51
159
109
76
116
79
55
101
69
48
74
51
35
109
74
52
79
54
38
117
80
56
85
58
41
a
The single-arm sample size is for a treatment–placebo slope difference analyzed with linear mixed effects regression. Sample size is listed as a function of
percentage of dropout, minimum CAP, Type I error rate, estimated parameters, and effect size. Details are provided in the Appendix.
Abbreviations: Min, minimum; CAP, age-CAG product.
colleagues observed that motor impairments did not
increase at a constant rate in their longitudinal study
of diagnosed patients with early through late HD.12
Individuals were stratified based on initial TMS, and it
was found that the rate of yearly TMS change
increased with initial TMS for ranges of values similar
to those observed in our study (ie, TMS 僆 [0,77]).
Similar results have been found using CAG length
rather than TMS,23-25 and there is evidence that other
domains, such as cognitive impairments, also may
change as a function of stage.26
The results have implications for the classification of
progression at study entry. In HD research, it is common to use DCL 5 4 as the definition of diagnosis or
manifest HD. By complement, DCL < 4 defines the
prodromal, pre-diagnosis, or pre-manifest phase. In
some studies, a different DCL cutoff is used; for example, DCL < 2 for pre-manifest HD and DCL 2 for
manifest HD.27 In other studies, several values are
used to define multiple groups, such as manifest
(DCL 5 4), pre-manifest with abnormalities (DCL 5 2
or 3), and pre-manifest with minimal signs (DCL 5 0
or 1).26,28,29
If the goal of classification is to distinguish individuals who are actively deteriorating from those who are
not, then our results suggest that DCL 5 4 is assigned
relatively late. Figure 2 and Table 2 provide evidence
of active and meaningful motor decline for DCL 5 3
and perhaps even for DCL 5 2, provided the CAP is
sufficiently large. Cutoffs for phases of activity might
be DCL 2 versus DCL 3 when the entire CAP
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Movement Disorders, Vol. 29, No. 3, 2014
range is considered, or DCL 1 versus DCL 2 when
CAP 290 is considered (see Table 2).
The findings have implications for the planning of
clinical trials of efficacy, with the outcome variable
being motor impairments and the TMS in particular.
The slope results from Table 2 indicate a possible
minimum CAP that might be considered should a
researcher want to recruit patients with the greatest
likelihood of active motor decline. Recruitment based
on DCL might be problematic because of the unreliability of the rating at the individual level. In ancillary analysis (results not presented), we have
observed that progression through the DCL categories is not necessarily monotonic, with some patients
even dropping over time after an initial value of
DCL 5 4. In contrast, the CAP can be highly reliable
provided that age is accurately reported and CAG
length is laboratory-verified.20 Therefore, we recommend using CAP as the primary consideration in
recruitment. Table 3 is a resource for planning clinical trials of efficacy if the CAP of recruits can be
computed. Sample sizes are provided for comparing
the rate of change (slope) of the treatment and placebo groups under various non-informative dropout
scenarios. Graphical evidence (not presented) suggests
that non-informative dropout is a reasonable assumption for the planning of a clinical trial based on the
PREDICT-HD database.30
Finally, the sensitivity to change of the TMS has
been questioned for use with prodromal individuals.31,32 Our results indicate that the TMS is sensitive
M O T O R
in the prodromal period in that it can detect statistically and substantively significant change.
I M P A I R M E N T S
I N
H D
P R O G R E S S I O N
TABLE A1. Dropout proportions (p) for computing required
sample size.
Dropout Condition
Appendix
Required sample size for slope difference. Estimates
of sample size required for a randomized clinical trial
of efficacy were based on power formulas for
LMER33,34. Sample size was calculated for the endpoint of TMS rate of change (slope) over a 2 year
study with observations every 6 months (baseline, 6,
12, 28, and 24 months). The design was a two-arm
Placebo (P) and Treatment (T) group study, and dropout was assumed to be non-informative (or ignorable).
The evaluation of efficacy is defined as the test of the
null hypothesis that the TMS rates of change in the P
and T groups are equal. The null hypothesis can be
evaluated based on parameter estimates in LMER.
Suppose Yij is the TMS value for the ith participant
(i 5 1, . . ., N) at the jth month (j 5 1, . . ., ni), and tij
denotes time in months. Assuming linear change over
time, the LMER model can be written as the following,
(A1)
Yij 5bI 1bs tij 1bDI gi 1bD s gi tij 1 bIi 1bsi tij 1eij
where gi is a dummy variable for group (gi 5 1 if the
participant is in the T group, and 0 otherwise). In
Equation A1, the bs are the fixed effects, with bI being
the P group intercept and bS being the P group slope;
bDI is the difference in the intercepts among the
groups, and bDS is the difference among the slopes.
The bs are random effects, and e is random error. We
make the
assumptions, ½ bIi bSi T N ð0; GÞ?
typical
eij N 0; r2e Ii . The prime object of inference is bDS,
as this is the difference in TMS longitudinal change of
the T and P groups. The null hypothesis of equality of
T and P slopes is H0: bDS 5 0, and can be evaluated
_
with a Z-test. The Z statistic is Z 5 b_Ds and leads
SE b DS
to the rejection of H0 when |Z| > Z12a, with the latter
being the 100(1 2 a)th quantile of the standard normal
distribution (single tailed).
The LMER model of A1 can be written more generally as
Yi 5Xi b1Zi b1ei
where Xi is the design matrix of the fixed effects, b is
the vector of fixed effects, Zi is the design matrix of
the random effects, b is the vector of random effects,
and ei is the vector of random errors. In this context,
Xi for a person with no dropout has dimensions 5 3 4
with the first column being a vector of 1s, the second
column a vector of time values (0, 6, 12, 18, 24),
the third column a vector of gi values, and the last column a vector of gi 3 time values. An individual is
defined as a dropout if their row dimension is less
than 5.
Missing Visits
0%
10%
20%
0
1
2
3
4
1.00
0
0
0
0
0.90
0.04
0.03
0.02
0.01
0.80
0.08
0.06
0.04
0.02
The required sample size for the Z-test depends on
the variance of the responses,
Vi 5Zi GZTi 1r2e Ii
Vi is 5 3 5 for no dropout, but has reduced dimension under dropout similar to Xi. Suppose a is the type
I error rate (set to 0.05 or 0.10), and 1 2 c is power
(set to 0.80). Then the required sample size for a single arm (half the total sample size) is
21
2 X2 XL T 21
Z12a1 Z12g
X
V
X
p
kl kl
kl kl
k
l
N
4;4
5
(A2)
2
2
bDS
where l refers to a specific dropout pattern, Xkl is the
design matrix for the kth group with the lth dropout
pattern, and pkl is the proportion of participants with
the lth missing pattern in the kth group, and the pkl
sum to one among the dropout patterns within a
group. The 4,4 subscript indicates the element in the
4th row and 4th column of the resulting matrix. When
there is no dropout, p11 5 p21 5 1, and the right hand
in
numerator
is
expression
21the
XT1 V 21 X1 1XT2 V 21 X2 4;4 , where the subscripts 1 and
2 represent the P and T groups.
Three dropout scenarios were considered: no
dropout (0%), 10%, and 20%. The values pertain
to the percentage of participants who dropped out
in each group, not the total missing data. Dropout
was identical for the P and T groups (p1l 5 p2l 5 pl),
and the dropout proportions, pl, tapered to create a
scenario in which dropout was slight very earlier in
the hypothetical study, but increased over time.
Five dropout patterns were considered, no dropout,
missing last visit, missing last two visits, missing
last three visit, and missing last four visits. Table
A1 shows the pl used for the three dropout
conditions.
The variance components of Equation A2 can be
estimated using the PREDICT-HD database. However,
PREDICT-HD is not a treatment study and all geneexpanded patients are considered as members of the
untreated placebo group in this context (not to be
confused with gene-negative patients), with mean
slope bS. The object of inference, bDS, can be estimated
as the proportion reduction of the placebo group slope
Movement Disorders, Vol. 29, No. 3, 2014
317
L O N G
E T
A L .
under a hypothetical treatment, expressed as
bDS 5wbS , where w is the proportion reduction in the
placebo slope. Sample size was estimated for w 5 0.30,
0.40, 0 .50, 0 .60, 0.70. The last value, representing a
70% improvement by treatment, was the approximate
percentage group difference in change over 12 weeks
in a HD randomized clinical trial of tetrabenazine,34,35
which is one of the few studies to show statistically
significant efficacy.36 The range of effects is also consistent with clinical trials of other neurodegenerative
diseases, such as Multiple Sclerosis.37 Table 3 presents
parameter estimate for bS, the unique elements of G
(g11, g12, g22), and r2e. These values can be used along
with different design matrices, dropout proportions,
etc., to provide estimates for a wide variety of clinical
trial scenarios.
We assumed a single-tailed statistical test, 80% power,
and a 2-year study with measurements taken at baseline
and every half-year, t 5 0, 6, 12, 18, 24 months. The
variance components were estimated using the
PREDICT-HD
participants
in
the
range
of
CAP 5 [minimum CAP, 846], with the latter value being
the maximum value in the sample. Different dropout
scenarios, including differential dropout by group, can
be created by altering the design matrices of Equation
A2.
PREDICT-HD Investigators,
Coordinators, Motor Raters,
Cognitive Raters
Stephen Cross, Patricia Ryan, Eric A. Epping, and
Stacie Vik (University of Iowa, Iowa City, Iowa,
USA); Edmond Chiu, Joy Preston, Anita Goh, Stephanie Antonopoulos, and Samantha Loi (St. Vincent’s
Hospital, The University of Melbourne, Kew, Victoria,
Australia); Phyllis Chua and Angela Komiti (The University of Melbourne, Royal Melbourne Hospital,
Melbourne, Australia); Lynn Raymond, Joji Decolongon, Mannie Fan, and Allison Coleman (University of
British Columbia, Vancouver, British Columbia, Canada); Christopher A. Ross, Mark Varvaris, and
Nadine Yoritomo (John Hopkins University, Baltimore, Maryland, USA); William M. Mallonee and
Greg Suter (Hereditary Neurological Disease Centre,
Wichita, Kansas, USA); Ali Samii and Alma Macaraeg
(University of Washington and VA Puget Sound
Health Care System, Seattle, Washington, USA); Randi
Jones, Cathy Wood-Siverio, and Stewart A. Factor
(Emory University School of Medicine, Atlanta, Georgia, USA); Roger A. Barker, Sarah Mason, and Natalie
Valle Guzman (Cambridge Centre for Brain Repair,
Cambridge, UK); Elizabeth McCusker, Jane Griffith,
Clement Loy, and David Gunn (Westmead Hospital,
Sydney, Australia); Michael Orth, Sigurd S€
ubmuth,
Katrin Barth, Sonja Trautmann, Daniela Schwenk,
318
Movement Disorders, Vol. 29, No. 3, 2014
and Carolin Eschenbach (University of Ulm, Ulm, Germany); Kimberly Quaid, Melissa Wesson, and Joanne
Wojcieszek (Indiana University School of Medicine,
Indianapolis, IN); Mark Guttman, Alanna Sheinberg,
and Albie Law (Centre for Addiction and Mental
Health, University of Toronto, Markham, Ontario,
Canada); Susan Perlman and Brian Clemente (UCLA
Medical Center, Los Angeles, California, USA);
Michael D. Geschwind, Sharon Sha, and Gabriela Satris (University of California San Francisco, California,
USA); Tom Warner and Maggie Burrows (National
Hospital for Neurology and Neurosurgery, London,
UK); Anne Rosser, Kathy Price, and Sarah Hunt (Cardiff University, Cardiff, Wales, UK); Frederick Marshall, Amy Chesire, Mary Wodarski, and Charlyne
Hickey (University of Rochester, Rochester, New
York, USA); Peter Panegyres, Joseph Lee, Maria
Tedesco, and Brenton Maxwell (Neurosciences Unit,
Graylands, Selby-Lemnos & Special Care Health Services, Perth, Australia); Joel Perlmutter, Stacey Barton,
and Shineeka Smith (Washington University, St. Louis,
Missouri, USA); Zosia Miedzybrodzka, Daniela Rae,
and Mariella D’Alessandro (Clinical Genetics Centre,
Aberdeen, Scotland, UK); David Craufurd, Judith Bek,
and Elizabeth Howard (University of Manchester,
Manchester, UK); Pietro Mazzoni, Karen Marder, and
Paula Wasserman (Columbia University Medical Center, New York, New York, USA); Rajeev Kumar,
Diane Erickson, and Breanna Nickels (Colorado Neurological Institute, Englewood, Colorado, USA); Vicki
Wheelock, Lisa Kjer, Amanda Martin, and Sarah
Farias (University of California Davis, Sacramento,
California, USA); Wayne Martin, Pamela King, Marguerite Wieler, and Satwinder Sran (University of
Alberta, Edmonton, Alberta, Canada); and Oksana
Suchowersky, Anwar Ahmed, Stephen Rao, Christine
Reece, Alex Bura, Lyla Mourany, and Jagan Pallai
(Cleveland Clinic Foundation, Cleveland, Ohio, USA).
Executive Committee
Jane S. Paulsen, Principal Investigator; Eric A.
Epping, Jeffrey D. Long, Hans J. Johnson, Jeremy H.
Bockholt, and Kelsey Montross.
Scientific Consultants
Brain: Jean Paul Vonsattel and Carol Moskowitz
(Columbia University Medical Center).
Cognitive: Deborah Harrington (University of California, San Diego); Tamara Hershey (Washington University); Holly Westervelt (Rhode Island Hospital/
Alpert Medical School of Brown University); Megan
M. Smith, and David J. Moser (University of Iowa).
Functional: Janet Williams and Nancy Downing
(University of Iowa).
M O T O R
Imaging: Hans J. Johnson (University of Iowa); Elizabeth Aylward (Seattle Children’s Research Institute);
Christopher A. Ross (Johns Hopkins University); Vincent A. Magnotta (University of Iowa); and Stephen
Rao (Cleveland Clinic, Cleveland, OH.).
Psychiatric: Eric A. Epping (University of Iowa);
David Craufurd (University of Manchester).
I M P A I R M E N T S
I N
H D
P R O G R E S S I O N
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Core Sections
Biostatistics: Jeffrey D. Long, Ji-In Kim, James A.
Mills, Ying Zhang, Dawei Liu, Wenjing Lu, and
Spencer Lourens (University of Iowa).
Ethics: Cheryl Erwin (McGovern Center for Health,
Humanities and the Human Spirit); Eric A. Epping
and Janet Williams (University of Iowa); Martha
Nance (University of Minnesota).
Biomedical Informatics: Jeremy H. Bockholt and
Ryan Wyse (University of Iowa).
Acknowledgements: We thank the PREDICT-HD sites, the study
participants, the National Research Roster for Huntington Disease
Patients and Families, the Huntington’s Disease Society of America, and
the Huntington Study Group.
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