The importance of endpoint selection: How effective does a drug need to be for success in a clinical trial of a possible Alzheimer’s disease treatment?
European Journal of Epidemiology
The importance of endpoint selection: How effective does a drug need to be for success in a clinical trial of a possible Alzheimer's disease treatment?
Stephanie Evans 0 1 2 3
Kevin McRae-McKee 0 1 2 3
Mei Mei Wong 0 1 2 3
Christoforos Hadjichrysanthou 0 1 2 3
Frank De Wolf 0 1 2 3
Roy Anderson 0 1 2 3
0 Frank De Wolf
1 Mei Mei Wong
2 & Stephanie Evans
3 Janssen Prevention Center , Leiden , The Netherlands
To date, Alzheimer's disease (AD) clinical trials have been largely unsuccessful. Failures have been attributed to a number of factors including ineffective drugs, inadequate targets, and poor trial design, of which the choice of endpoint is crucial. Using data from the Alzheimer's Disease Neuroimaging Initiative, we have calculated the minimum detectable effect size (MDES) in change from baseline of a range of measures over time, and in different diagnostic groups along the AD development trajectory. The Functional Activities Questionnaire score had the smallest MDES for a single endpoint where an effect of 27% could be detected within 3 years in participants with Late Mild Cognitive Impairment (LMCI) at baseline, closely followed by the Clinical Dementia Rating Sum of Boxes (CDRSB) score at 28% after 2 years in the same group. Composite measures were even more successful than single endpoints with an MDES of 21% in 3 years. Using alternative cognitive, imaging, functional, or composite endpoints, and recruiting patients that have LMCI could improve the success rate of AD clinical trials.
Alzheimer's disease; Clinical trials; Longitudinal data analysis
List of abbreviations
AD Alzheimer’s disease
MDES Minimum detectable effect size
Department of Infectious Disease Epidemiology, School of
Public Health, Imperial College London, London, UK
All cause dementias are one of the world’s leading health
concerns. In the absence of effective therapies, is it
estimated that the number of people with dementia will reach
131.5 million by 2050. Alzheimer’s disease (AD) is the
most common form of dementia accounting for 50–75% of
all case that typically affect the older age groups [
]. AD is
a neurodegenerative condition characterised by a
progressive decline in cognitive function, accompanied by changes
in the concentrations of certain proteins (e.g. Amyloid1-42
(Ab1 42) and tau) in cerebral spinal fluid (CSF), and
changes in the brain that can be picked up by scanning
technologies such as Magnetic Resonance Imaging (MRI)
There is currently no treatment or cure, and in 2016 the
Office for National Statistics reported that AD had
overtaken cardiovascular disease to become the leading cause
of death in England and Wales [
]. Unlike cardiovascular
disease where 41 drugs have been approved by the U.S
Food and Drug Administration (FDA) since 2002, only five
drugs that provide short-term symptomatic relief and have
no preventative or curative activity, have been marketed in
the AD therapy area since 1984. No new drugs have been
approved by the FDA since 2002 [
The high attrition rate in clinical trials (CTs) of possible
AD therapies has been attributed to a number of factors
including inadequate target selection due to the uncertainty
surrounding the biological mechanisms behind disease
], and the true efficacy of a treatment being
masked by the variance in the endpoint employed [
nature of AD as a slowly developing disease over many
decades means that the timespan of a CT, typically less
than 2 years [
] could be too short for an effect to be
In their 2016 draft guidelines for clinical investigation of
medicines for the treatment of AD, the European
Medicines Agency (EMA) states that efficacy in an AD CT should
be measured by a cognitive, functional and clinical
endpoint when considering patients with established AD [
However, in patients with less severe disease the guidelines
are more ambiguous. In patients with prodromal AD or
mild cognitive impairment (MCI), they recommend the use
of two co-primary endpoints assessing cognition and
function, and in preclinical AD patients they state that there
is no gold standard for assessment. The FDA guidelines
also state that CTs in on AD should use a co-primary
outcome measure approach in which a drug demonstrates
efficacy on both a cognitive and a functional or global
assessment scale [
], suggesting the use of a composite
cognitive and functional score as a suitable tool for
assessment in early disease and giving CDR-SB as an
example of such an endpoint. However, they also state that
they would consider approving isolated cognitive measures
as endpoints in trials where patients are in a preclinical AD
stage. Biomarkers are not currently accepted as endpoints
but the FDA will consider them for approval as either
primary or secondary outcome measures if sufficient
evidence can be provided [
]. Despite these guidelines, the
Alzheimer’s Disease Assessment Scale-cognition subscale
] is still the most widely used general
cognitive measure in AD CTs [
]. This is despite concerns
that ADAS-Cog may underestimate changes in and
differences between patients given the drug and those in the
control group. These concerns are particularly pertinent
when dealing with patients with MCI or early AD [
or when the length of the trial is less than 18 months
The Alzheimer’s Disease Neuroimaging Initiative
(ADNI) is a consortium of universities and medical centres
in the United States and Canada that have formed a
longitudinal observational cohort study to identify new
imaging biomarkers measuring AD progression [
range of cognitive, biomarker, and functional data has been
We aim to investigate whether there are measures in any
of these three groups that could be used as endpoints to
increase the probability of success in an AD preventative
CT. As it is not feasible for us to assess the potential of
every measure recorded in ADNI as an endpoint, we have
selected a small subset of measures that we believe are
appropriate for demonstrating our case, that ADAS-Cog
may not be the most suitable endpoint for AD trials. Using
a formula described by [
], we have calculated the
minimum detectable effect size (MDES), defined as the
absolute change from baseline that lies outside of the sum of the
type I and type II error levels for a standard Z-test, for a
selection of measures from each of the three groups in
ADNI. We report the required treatment efficacy as the
percentage by which the actual change from baseline in an
untreated would have to be reduced by to bring the value of
each measure back within a non-detectable region from the
baseline value, for different time points in the study.
For our analysis we used the ADNI study
(adni.loni.usc.edu) that was launched in 2003 as a public–private
partnership, led by Principal Investigator Michael W.
Weiner, MD. The primary goal of ADNI has been to test
whether serial MRI, PET, other biological markers, and
clinical and neuropsychological assessment can be
combined to measure the progression to MCI and to early AD.
For up-to-date information, see www.adni-info.org. The
dataset used was downloaded on 31st October 2016 from
the ADNI server. 106 individuals with a ‘‘subjective
memory concern’’ (SMC) diagnosis at baseline were
excluded from the analyses. Due to the decreasing sample
size in each of the four cognitive groups over time, data
from visits more than 6 years after baseline was discarded
for all individuals on the grounds that there was insufficient
data in any of baseline diagnostic groups after this time
point (Table S1). The MDES was calculated for a selection
of cognitive, biological, and functional measures. These
variables, along with their availability in the ADNI dataset
are shown in Table 1.
Cognitive markers in ADNI
Potential cognitive endpoints that have been recorded in
the ADNI study include the mini-mental state evaluation
score (MMSE), Montreal cognitive assessment (MoCA),
and a more comprehensive version of the ADAS-Cog
The ADAS-Cog was designed specifically to identify
AD in a CT [
]. The ADNI study contains two variants of
ADAS-Cog that score patients using either 11 or 13
] allowing participants to score a maximum of 70
points (ADAS-Cog-11) or 85 points (ADAS-Cog-13)
respectively, with lower scores indicating better cognitive
The MMSE was developed to evaluate the cognitive
performance of psychiatric patients as an alternative to
other cognitive scoring tests that were lengthy to
]. Scores range from 0 to 30 with a higher score
indicative of better cognitive function and cut-off points
are typically defined as follows; C 24 Cognitively Normal
(CN), 18-23 MCI, B 18 AD [
]. MMSE is often recorded
as a secondary endpoint in AD-CTs but is not commonly
used as a primary endpoint.
The MoCA scoring system was developed to screen
MCI individuals who have MMSE scores of 24 or higher
thus are considered to be CN based on MMSE alone [
Like MMSE, MoCA is often listed as a secondary endpoint
Magnetic resonance imaging markers in ADNI
One of the major goals of the ADNI study is to develop
standardised imaging techniques to help create uniform
standards for acquiring longitudinal magnetic resonance
imaging (MRI) data [
]. As such, ADNI database contains
several MRI measurements including hippocampal and
whole brain volume that are thought to be useful for the
classification of cognitively impaired individuals into an
AD or MCI subset. For details of how the MRI volumes are
calculated, see [
Hippocampal volume atrophy has long been associated
with disease progression in AD [
] and it has been
suggested that hippocampal atrophy could be used as a
surrogate marker for efficacy in an AD CT [
brain atrophy has also been strongly associated with
cognitive decline [
], with rates of atrophy typically being
higher the further down the AD disease trajectory a patient
Functional markers in ADNI
A decline in the ability to perform daily activities such as
handling finances, shopping, using the telephone, and
managing medication is an important factor in diagnosing
AD using the Diagnostic and Statistical Manual (DMS-VI).
There are several methods to assess functional capabilities
recorded in ADNI, including the Clinical Dementia Rating
Sum of Boxes (CDRSB), and Functional Activities
The CDRSB is a composite score assessing both
cognitive function and daily living activities. The score ranges
from 0 to 18, and is calculated by summing over scores in
six domains including memory, orientation, judgment/
problem solving, community affairs, home and hobbies,
and personal care, with higher scores indicative of more
severe disease [
The FAQ measures activities such as preparing meals
and managing personal finances [
]. The FAQ score
ranges from 0 to 30 and can be used to differentiate those
with mild cognitive impairment and mild Alzheimer’s
Composite measures calculated from ADNI
Although not yet common in CTs, several composite
measures of AD-related decline have been proposed in the
literature. We have calculated three of these measures
using the ADNI dataset, namely the AD Composite Score
]. Preclinical Alzheimer’s Cognitive
Composite (PACC) [
], and a five item composite
proposed by Huang et al. [
]. ADCOMS consists of four
ADAS-Cog items, two MMSE items, and six CDR-SB
items, and is designed to provide improved sensitivity for
measuring cognitive decline in amnestic MCI, prodromal
AD, and in mild AD dementia. The PACC was designed to
estimate decline in preclinical AD groups that Ab1 42
positive. This score consists of the Total Recall score from
the Free and Cued Selective Reminding Test (substituted
with the Delayed Recall from the ADAS-cog test in ADNI,
as advocated by [
]), the Delayed Recall score on the
Logical Memory IIa subtest, the Digit Symbol Substitution
Test score, and the total MMSE score. In the construction
of the PACC, all of these measures are standardised by
dividing by the baseline standard deviation, before
summing to generate an overall score. The third composite
developed by Huang et al., is the sum of Word Recall,
Delayed Recall and Orientation scores from the
ADAScog, along with CDR-SB and FAQ scores. It was designed
to improve detection of decline in Ab1 42 positive MCI
Minimum detectable effect size calculations
For a treatment effect to be statistically significant at the a
level with a one-tailed hypothesis test (or at the a/2 level
with a two-tailed test), the estimate of the mean must fall to
the right of the a-level critical value. Further, to have a
probability 1 - b of detecting a treatment effect, the mean
treatment effect must lie a distance greater than or equal to
1 - b-level critical value to the right of the critical value
under the null hypothesis where b represents the level of
statistical. The MDES that can be statistically identified
between two populations in a randomised trial is therefore.
MDES ¼ va þ v1 b npð1 pÞ; ð1Þ
where va is the a-level critical value of the distribution
used in the hypothesis test, v1 b is the 1 - b-level critical
(typically 80%), r is the pooled standard deviation of the
trial endpoint, n is the total number of individuals in the
trial at the time point under consideration, and p is the
proportion of individuals in the treatment group [
Detecting an effect in cognitive markers
The MDES was calculated for four cognitive markers,
ADAS-Cog11, ADAS-Cog13, MMSE and MoCA (Fig. 1).
The data from ADNI suggest that a CT that uses
ADASCog-11 as an end point would be unable to detect a
treatment effect within 6 years if the patients in the trial were
either CN or had early MCI (EMCI) at baseline, even if the
treatment acted instantly and with 100% efficacy. If the
baseline population was composed of individuals
diagnosed with late MCI (LMCI), an effect could be
detected within 2 years if a treatment slowed the increase
in ADAS-Cog-11 by at least 45%. In this LMCI
population, the MDES increases at later time points. The group
that was AD at baseline had the smallest MDES, and an
effect of 35% could be detected in 2 years (Fig. 1).
Similarly to ADAS-Cog11, if the endpoint of a CT with
baseline demographics the same as in the ADNI database
was taken to be ADAS-Cog13, it would be difficult to
detect an effect in a CN population with treatment efficacy
of 100% detectable in a 6 year trial, and impossible to
detect an effect in a population of patients with EMCI. The
LMCI population gave the highest chance of success with a
45% efficacy detectable within 2 years, although as with
ADAS-Cog11, increasing the length of the trial past
4 years had a negative effect on the MDES. A 35% effect
size could be identified in a 3 year trial. In AD group, the
MDES was 38%.
Using MMSE as an endpoint in a CT, no effect will be
detected in a 6 year trial if the population is CN at baseline
which is unsurprising given that MMSE was not designed
to be used in CN individuals. In a population with EMCI at
baseline, a drug would have to have 100% efficacy for an
effect to be detected. However, using MMSE as an
endpoint allows an effect to be detected in the LMCI group at
an earlier time point than either of the ADAS-Cog scores,
with a treatment effect that slowed the decline in MMSE
score by 60% detectable within 1 year, and 35% by
2 years. Again, a lower effect can be detected in the AD
group at 1 year (35%) but there is no advantage to using an
AD group, over a set of patients with LMCI in a 2 year trial
The MoCA scores did not reveal a detectable effect in
any diagnostic group within 6 years (Fig. 1).
Detecting an effect in MRI markers
Hippocampal and whole brain atrophy could be considered
to be targets in a CT, however here we consider their utility
as endpoints in CTs where they are not directly targeted,
thus we estimated the MDES using hippocampal atrophy,
and whole brain volume (Fig. 2).
An effect of altering the rate of hippocampal atrophy
can be detected in all diagnostic groups within 3 years
from baseline. The CN group demonstrated a
detectable therapy effect size of 86% in 3 years but this
improves to being able to detect a 39% effect in a 6 year
trial. In the EMCI group, a therapy effect of 72% can be
detected in 3 years, and this improves to a detectable
therapy effect of 36% in a trial lasting 5 years. The LMCI
group has the smallest MDES, with an efficacy of 92% can
be detected after 1 year, 46% by 2 years and less than 30%
from trials of 3 or more years. In the AD group, slowing the
decline in hippocampal atrophy could only be detected at
one and 2 years (79 and 53% respectively).
When taking whole brain atrophy as an endpoint, no
effect can be detected in the CN population until 3 years
(72%) and the minimum effect size that can be detected is
59% at 6 years. The EMCI group has an MDES of 78% at
four years but this improves to 28% by 5 years. In the
LMCI group, an effect size of 71% can be detected within
2 years, and this is improves to 28% in a trial of more than
Detecting an effect in dementia rating or functional activities
We calculated the MDES for the clinical dementia rating
sum of boxes (CDRSB), and the functional activity
questionnaire (FAQ) scores (Fig. 3). The CDRSB had a
detectable effect in all groups except for EMCI. In the CN
group, the minimum MDES in the first 6 years (66%)
occurred at 4 years, but an effect was detectable at all time
points in this group. In the LMCI group, an effect of 29%
could be detected in a 2 year trial. This effect size did not
change significantly as the length of the trial increased. In
the AD group, the MDES was also 29%, again occurring
after 2 years.
The FAQ endpoint gave similar results to CDRSB in the
more severe populations but had a higher MDES in the CN
population, with an effect of 90% only detectable after
6 years. In the EMCI population, an effect size of 81%
would be detectable in a trial lasting 4 years. The LMCI
and AD populations had a MDES of around 30%.
Detecting an Effect in Composite Endpoints
We calculated the MDES for three previously published
composite endpoints, ADCOMS, PACC, and another by
Huang et al. [
] (Fig. 4). Using the ADCOMS measure
allows an effect of 40% to be detected by 6 months in the
LMCI population, and an effect of 33% at the same time
point in the group that had AD at baseline. The minimum
effect that can be detected with the ADCOMS measure is
22% by 2 years in the LMCI population, or 20% by 2 years
in the AD group. To detect an effect in either the CN or
EMCI populations using ADCOMS as an outcome measure
the effect of the treatment would have to be at least 75%
and the trial would need to run for 4 years (CN) or 3 years
The PACC is most successful in detecting a change in
the CN population with an effect size of 51% being
identifiable by 3 years. It is the least successful endpoint for
detecting change in the LMCI and AD groups.
The composite proposed by Huang et al. [
] allows an
effect of 21% to be identified in the LMCI group by
Fig. 4 Minimum detectable effect size in composite endpoints.
MDES was calculated for ADCOMS, PACC, and the measure
generated by Huang et al. [
] over 6 years from baseline in the
ADNI study. Missing bars indicate a non-detectable effect size, or
time points where there were less than 100 people thus have been
excluded from the analysis
2 years. It is slightly more successful than ADCOMS in
determining a change in the AD group, and at later time
points in the LMCI group.
Length of Trial on MDES
For almost all of the endpoints that we considered,
increasing the length of the trial from 0.5 to 3 years
decreases the MDES, thus improving the likelihood of a
treatment being successful. However, after 3 years, the
MDES of a change in score/marker level from baseline
either stays approximately the same level, or increases.
Figure 5 shows the distribution of the baseline markers for
those individuals still involved with the study at each time
point in the ADNI study. For all cognitive and functional
endpoints, individuals who remain in the study after
3 years have less abnormal baseline values of these
measurements, and are therefore expected to decline at a slower
rate. However, there is not a significant change in the
variability of the baseline values for these individuals. It
would therefore be less likely that a treatment effect could
be detected in this population in a CT where change from
baseline in a treatment versus control group using one of
these endpoints was the outcome of interest.
In this study we assessed the MDES of potential cognitive,
imaging, functional and composite clinical trial endpoints
when compared to baseline measures using the ADNI study
(Table 2). We have demonstrated that several single
endpoints may be better than the ADAS-Cog, that is widely
used and can be considered as standard, for detecting a
treatment effect in patients that have either LMCI or AD at
baseline, namely a decline in MMSE, hippocampal
atrophy, whole brain atrophy, an increase in CDRSB, and an
increase in FAQ. The composite endpoints ADCOMS and
that proposed by Huang et al. [
] are also more sensitive
than ADAS-Cog in an LMCI group. In addition to the work
presented here, we explored the MDES using CSF markers
but found no detectable effect within 6 years.
The FDA has provided new draft guidelines for clinical
trial endpoints in patients at different stages of disease
ranging from stage 1, where patients have pathological
abnormalities to stage 4 with severe dementia, stating that
cognitive endpoints are appropriate for patients in stage 1
or 2 of the disease (pathological symptoms but no or little
cognitive complaints), but that an integrated scale assessing
both function and cognition such as the composites
examined in this work would be an appropriate, and
acceptable endpoint in patients with stage 3 and 4 of the
Fig. 5 Individuals retained in the study past 3 years are less abnormal
at baseline. Violin plots show the distribution of the baseline values of
the measures used in this study for the individuals retained at each
After conducting this study, we would suggest that a
potentially effective trial design would involve targeting an
LMCI or AD population for at least 2 years and using
functional scores such as CDRSB as a single endpoint, or
ADCOMS as a composite. If a single cognitive endpoint
was to be used, we would suggest using MMSE over
ADAS-cog since a lower efficacy treatment effect can be
identified using this measure. Further, an effect can be
detected earlier using MMSE in an LMCI population.
The issue of detectable effect sizes in AD CTs is
particularly pertinent following the recent failures of
promising drugs including Solanezumab in a trial using change in
ADAS-cog at 80 weeks as a primary endpoint, with
patients selected on diagnostic group (mild AD), Ab status
and MMSE at baseline. Using the methodology presented
here, we estimate that the MDES in this trial would have
been 4.07 points in ADAS-cog, far above the change of 0.8
in the trial, but had the endpoint been chosen to be one of
the composite scores, it is possible that a significant effect
could have been detected.
Previous work has focused on estimating required
sample sizes for a trial to be successful using variety of
endpoints but with predefined therapy efficacies [
(reviewed by [
]). While these analyses have provided
insight into sample sizes required to detect an effect of a
treatment with 25% efficacy, the numbers produced are
often infeasible for CT situation, and such work does not
provide evidence as to the size of the effect that can be
detected when the population size drawn from an
time point. Red points indicate the mean baseline value of each
measures over time
acceptable CT design. By using longitudinal patient data
from the ADNI study, we have estimated the most efficient
single and composite measures for detecting a clinical
effect using change-from-baseline over 6 years, for four
baseline diagnosis groups. The advantages in studying
effect size in this manner are two-fold. As well as being
able to make inferences about ideal populations and
timespans for clinical trials, we have been able to account for
the effect of withdrawal of participants from the study on
the MDES. This effect is seen most strongly when
considering the cognitive scores as endpoints (Fig. 1) but also
occurs with functional and composite measurements
(Figs. 3, 4). In the LMCI group, the MDES increases after
3 years, meaning that in a trial of three or more years
where all participants start as LMCI, we are less likely to
detect an effect than in a shorter trial. There are two
possible reasons for this, firstly, the sample size reduces year
upon year (Table 1), but this can be accounted for by
taking a larger starting population. However, on average
the baseline measurements of the patients that are retained
in the trial past 3 years are less abnormal than for those that
withdraw. This is an artefact created by using the mean
change from baseline methodology that is commonly
adopted in AD CTs [
], because those with worse baseline
scores, who are expected to progress to AD at a faster rate,
are more likely withdraw from the trial so those individuals
that are left in the trial at later time points had, on average,
higher cognitive or functional scores at baseline, and have
a lower rate of decline over time. The effect of removing
10% of the patients with high ADAS-cog 1 or 13 at
baseline (defined as those with a score greater than 1
standard deviation away from the mean) increases the
MDES at 3 years by 1 and 0.5% respectively in the LMCI.
However, given that ADNI is a more homogeneous
population than a general LMCI patient group, this effect could
be higher in a clinical trial situation and needs studying
further using a larger, or more regular population. This
effect does not appear when considering the MRI markers,
suggesting that the rate of brain atrophy is not dependent
on the baseline measurement (Fig. 2).
There are several limitations to this study. Firstly, we
only compared mean change from baseline, not the rate of
change in measurements over time as has been suggested
by some [
]. However, the FDA have not reached a
conclusion as to whether the comparison of the rate of
change of a marker between treatment and control groups
could act as a sole endpoint in a CT  thus mean change
from baseline is the most clinically relevant comparison at
this point in time. Furthermore, we have calculated the
MDES assuming that any treatment would act immediately
from baseline with the specified effect, and that the effect
would be linear over time. However, a simple addition can
take account of pharmacokinetics and pharmacodynamics
if the drug efficacy required to achieve a detectable effect
for a non-linear treatment effect is known. The results
presented here are generalizable to trials in which patient
populations are classified in the same way as in the ADNI
dataset. It is possible that the MDES in the markers
described here (most notably hippocampal volume, but also
ADAS-cog to some extent), could be underestimated
within the four baseline demographic groups in ADNI than
in such cognitive subgroups in the general population. It
should also be noted that treatments targeting vascular risk
factors or conditions such as hypertension or diabetes may
provide improvements in different cognitive domains than
treatments targeting amyloid or tau.
Using the results presented above to select combinations of
endpoints for an AD CT, could increase the likelihood of a
trial being successful. The methodology presented here has
been applied having in mind more traditional clinical trials
conducted in the AD area. However, this could also be
applied to trials focusing on lifestyle intervention. The
results presented here may be particularly applicable to
trials such as the FINGER study, where there are no
placebo or drug related side effects on the recorded measures.
The composite measures examined here could be used to
replace the Neuropsychological Test Battery (NTB)
measure used in this trial [
It would be an interesting question to repeat this analysis
with a dataset containing prodromal, and pre-AD subsets,
as well as with data where patients were diagnosed using
the National Institute on Aging and the Alzheimer’s
Association (NIA-AA) criteria  to explore whether this
diagnostic criteria provides a less variable outcome.
Acknowledgements Data collection and sharing for this project was
funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
(National Institutes of Health Grant U01 AG024904) and DOD ADNI
(Department of Defence award number W81XWH-12-2-0012). ADNI
is funded by the National Institute on Aging, the National Institute of
Biomedical Imaging and Bioengineering, and through generous
contributions from the following: AbbVie, Alzheimer’s Association;
Alzheimer’s Drug Discovery Foundation; Araclon Biotech;
BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir,
Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and
Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated
company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;
Janssen Alzheimer Immunotherapy Research & Development, LLC.;
Johnson & Johnson Pharmaceutical Research & Development LLC.;
Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics,
LLC.; NeuroRx Research; Neurotrack Technologies; Novartis
Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier;
Takeda Pharmaceutical Company; and Transition Therapeutics. The
Canadian Institutes of Health Research is providing funds to support
ADNI clinical sites in Canada. Private sector contributions are
facilitated by the Foundation for the National Institutes of Health
(www.fnih.org). The grantee organization is the Northern California
Institute for Research and Education, and the study is coordinated by
the Alzheimer’s Therapeutic Research Institute at the University of
Southern California. ADNI data are disseminated by the Laboratory
for Neuro Imaging at the University of Southern California.
Author Contributions wrote the paper (SE),, conducted the analysis
(SE, KMM), designed the project (SE, KMM, CH), managed dataset
approval (MMW), supervised the project (FW, RMA).
Funding This study was funded by the Janssen Prevention Center.
Compliance with ethical standards
Conflict of interest R.M.A. is a non-executive board member of
GlaxoSmithKline (GSK). GSK played no part in this research, its
funding or the preparation of the manuscript.
Ethics approval and consent to participate Obtained by ADNI. Not
applicable to this study.
Consent for publication Obtained from ADNI.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creative
commons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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