Presentation delivered at the live webinar hosted by the Alzheimer Research Forum (www.alzforum.org) on the 17th of December, 2015.

1. Construction of Composite
Endpoints for Early Stage Trials
and other ways to improve power
CTAD 2015
Suzanne Hendrix, PhD
Pentara Corporation
Disclosure: President and CEO of Pentara Corporation through which I am a
paid consultant for several public, private and non-profit organizations .

2. Disclosures and Thanks
• Rush ROS, ADCS and ADNI for data
• Eisai and API for the first composite projects
• Roche, Janssen, Affiris for additional
composite research support
• Stephanie Stanworth, Noel Ellison and Leah
Garriott (Pentara)
• Bruce Brown (BYU) – Data mining tools

3. Outline
• History – ADAS-cog 8, APCC, ADCOMS, Roche
composite, RBANS composite, Affiris
composite
• Why? What is a composite? Co-primary
endpoints
• Type 1 error vs Type 2 error

4. History of Quantitative Composites
(As seen through the eyes of Suzanne Hendrix)
• 2008 and earlier – NTB composite (Bapi / Harrison), Hobart/Cano research
showing problems with ADAS-cog in Mild AD, Rescoring algorithm from
Wouters et. al.
• 2008 – MCI - Eisai collaboration – ADCOMS* (Veronika Logovinsky),
presented early composite in 2010 (cog only, cog +global, cog+function)
• 2009 – MCI/Mild -ADAS-cog 8* – 8 item ADAS-cog, ADAS-8+NTB
• 2010 – APCC* – for pre-MCI population developed with API – presented
AAIC 2011, AARoundtable 2012 (MMSE shows up!)
• 2011 – Pre-MCI Composite* for Columbian cohort
• 2012 – Roche composite* (Glenn Morrison), RBANS composite* (Michael
Ropacki) J & J Early Composites (Raghavan), ADNI cognitive core,
PROADAS- AZ
• 2013 – PACC presented, Lilly early composites
• 2013 – Mild AD composite developed with Affiris*
• 2014 – Affiris composite outperforms CDR-sb
* Pentara was involved in this project

5. Signal to Noise Improves with Fewer
Items, Then Worsens with Too Many
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 3 5 7 9 11 13
Best Mean to Standard Deviation Ratio for ADAS-cog Items
MSDR
Number of Items

6. ADAS-cog 13
Items for MCI
Mean to SD
Ratio
ADAS-cog 13
Items for Mild AD
Mean to
SD Ratio
All Items 0.285 All Items 0.714
Excludes Word Finding
Difficulty
0.357 Excludes Word Recognition 0.743
Also Excludes Number
Cancellation
0.400 Also Excludes Remembering Test
Instructions
0.779
Also Excludes Ideational
Praxis
0.409 Also Excludes Commands 0.800
Also Excludes
Immediate Word Recall
0.412 Also Excludes Constructional
Praxis
0.817
Also Excludes Word
Recognition Task
0.414 Also Excludes Number
Cancellation
0.821
ADAS-cog 8 + 4 NTB Items (RAVLT,
RAVLT Delayed, Clock Drawing,
and Digit Span)
0.946
ADAS-cog13 Can Be Improved
In MCI and Mild AD by Removing Items

7. Original Method – Find Good Individual
items and Put Them Together
(MSDRs = 1/CV in Pre-MCI population)
• Logical Memory IIa (Delayed) - 0.128
• Category fluency – Fruits - 0.123
• Logical Memory Ia (Immediate) - 0.110
• Mini-Mental Status Examination - 0.109
• Word list memory (Delayed recall) - 0.104
• Word list recall (Immediate) - 0.102

8. Why Use a Composite?
• It can’t perform worse than the worst component
in the composite
• It often performs better than the best component
in the composite
• We’re already using composites!
• Do we believe that all of the points on the ADAS-
cog or CDR-sb are equivalent? Are they all on the
Disease trajectory?
• Patients may change more on Cognition than
Function or vice versa – Composite allows both
changes to be relevant.

9. 0%
25%
50%
75%
100%-30
-20
-10
0
10
1 2 3 4 5 6 7 8 9 10 11
Percentofprogressorswithdementiadiagnosis
ChangefrombaselineAPCCscore
Years% progressors with dementia…
Converter mean
n=1137 981 798 647 548 458 429 378 322 236 176
n=341 316 304 276 255 235 218 178 148 115 99
APCC Composite Declines In Pre-MCI
Population

10. Data-Based Composites Increase
Power /Reduce Sample Sizes
Popu-
lation ADAS-13
Composite
(adj)
%
improve
ment
%
patients
wasted
%
patients
used
Multiple
of
patients
ADAS8 Mild Mild 0.714 0.755 6% 10.6% 89.4% 1.12
ADAS8+NTB4 Mild 0.714 0.870 22% 32.7% 67.3% 1.49
ADAS8 MCI MCI 0.285 0.381 34% 44.0% 56.0% 1.79
ADAS6+MMSE1 MCI 0.285 0.466 63% 62.5% 37.5% 2.67
ADAS6+MMSE1+
NTB2 MCI 0.285 0.489 71% 66.0% 34.0% 2.94
ADCOMS MCI 0.285 0.427 50% 55.5% 44.5% 2.25
ADAS13 vs RBANS
Optimized MCI 0.285 0.589 107% 76.6% 23.4% 4.27
Logical
Memory II
Best Single Item vs
APCC pre-MCI 0.13 0.156 20% 30.9% 69.1% 1.45

11. Continuous Scales Improve Power
Over Discrete Scales
• Validation data looks better for ADCOMS than CDR-sb - ADCOMS is
a more granular version of CDR-sb
• If you power a study at 80% for CDR-sb, Type 2 error is 20% (chance
of failing a good treatment) with CDR-sb, but 6% with ADCOMS
• P=0.05 on CDR-sb => p=0.0092 on ADCOMS
• P=0.178 on CDR-sb => 0.05 on ADCOMS
• CDR-sb requires 40% more subjects (wastes 28.6% of subjects)
• Using a discrete scale does NOT ensure clinical relevance. In many
situations, it hides it! Make sure you know whether your treatment
works or not, then address clinical relevance (several clinically
irrelevant effects together might be relevant)

12. Why Now?
• Studies are failing – only 0.4% success rate – lots
of equivocal results in phase 2
• What happened to type 1 errors? We should be
succeeding 5% of the time by chance alone.
• We want to go earlier – statistically harder to
power in this population
• Power is the key to seeing real differences
• We can’t be casual about losing power – it wastes
patient and caregiver time and energy

13. Type 1 vs. Type 2 Error

14. Who are we fighting against?
• We have met our enemy and it is us
• Regulatory /Clinical concerns vs. Power/
Statistical concerns
• We all want to make good decisions
– Effective treatments should result in positive trials
– Ineffective treatments should fail convincingly
– Equivocal results are the worst outcome

15. Questions
• Does the composite endpoint really measure a
disease? Or is it off of the primary disease
path?
• Are the individual components of the
composite endpoint valid, biologically
plausible, and of importance for patients?
• Does a composite reflect “How a Patient Feels,
Functions, or Survives”?

16. Ways to Improve Power
• Measure a continuous disease with . . .
a continuous outcome!
– Time to event is more powerful only when event
rates are very low
• Clinical relevance can’t be addressed by
choosing a coarse scale
• Combine endpoints to get 1 answer by using
composites or combined p-values for overall
significance

17. Can We Change the Status Quo?

18. Methods
• Dimension reduction technique applied to:
• Two MCI groups (n=650)
– ADNI I MCI population
– ADCS placebo group (from donepezil, vit E trial)
• Three Mild AD groups (n=320)
– ADNI I Mild AD population
– ADCS NSAID study placebo group
– ADCS Homocysteine study placebo group

20. “Isoquant” Plots Reduce Dimensions

21. Recover Original Scores

22. Comments
• AD is nearly unidimensional (axis 1) = progression
path
• Learning Effects and Normal Aging are opposite
ends of the next dimension ( axis 2)
• Composites are identifying a latent variable
associated with dimension 1 – Progression of
Alzheimer’s Disease
– Factor Analysis does not achieve this same goal.
• Cognition represents the Core Disease Symptoms
– it is not a biomarker(!)

23. http://mathinsight.org/vectors_cartesian_coordinates_2d_3d
A Composite is a Summed Vector

24. Composites That Reflect Disease Pathway Result in
Larger Disease Related Treatment Effects
• Are we treating a “shadow” of the disease?
• We assume proportional treatment effects, but we
usually see larger ones for good composites (it hurts
us twice)
• If ADLs and Cognition are both required and both
only correlate with disease progression then both
have weaker power, and significance on both
requires a very large effect
• Phase 2 should be more certain without having to
have enormous samples

25. Co-Primary Outcomes
What do they really cost us?
True Alpha Level
Scenario
Low 0.35
Correlation
Medium 0.4
Correlation
High 0.45
Correlation
Co-primaries at 0.05 0.0057 0.0067 0.0079
One significant, one a
"trend" 0.0095 0.0111 0.0128
One significant, other
"same direction" 0.0280 0.0305 0.0331
• Even in Mild disease, where cognition and function
are both changing, co-primary endpoints reduce
power from 80% to 54% - chance of failing a
successful treatment is more than double (46%
instead of 20%)

26. Conclusions
• True alpha with co-primary outcomes is 0.007
• Co-primaries require many more patients (20% to
80% more)
• Requiring significance on one outcome and a
‘trend’ on a second results in alpha=0.011
• Traditional scales require many more patients
than optimized composites (12% to 327% more)
• Not measuring the true disease trajectory costs
at least 28% of our subjects – is it worth it?

27. Why are we accepting the status quo?