Panel Presentation FDA Statistical Review 1
Statistical Review of P040049 Acorn’s CorCap Cardiac Support Device
Laura Thompson, Ph.D.Mathematical Statistician
CDRH/FDA
Panel Presentation FDA Statistical Review 2
Outline
•Study Design
•Primary Endpoint Analysis
•Concerns
•Separate Analyses of Components of Primary Endpoint
•Analyses of Secondary Endpoints
•Analyses of Primary Endpoint by MVR Strata
•Summary
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Study Design
• Two-arm, randomized 1:1 study (300 pts)• Randomization blocked by site (30 sites) and stratified by
concomitant MVR surgery
• Primary analysis pooled across strata (test for treatment x MVR interaction was not found to be significant)
TotalN = 300
MVR stratumN =193
No MVR stratumN = 107
ControlMVR only
N = 102
Treatment MVR + CorCap N = 91
ControlMed Rx only
N = 50
Treatment Med Rx +
CorCap N = 57
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Primary Endpoint
• Composite Endpoint (evaluated > 12 months) all-cause mortality change in core lab NYHA class assessment from baseline major cardiac procedures indicative of worsening HF
• Ordinal Scoring (1=Improved, 2=Same, 3=Worsened) Improved = Improved NYHA class and did not die and did
not receive MCP Same = no change in NYHA from baseline, did not die and
did not receive MCP Worse =
• Died, or• Received MCP for worsening HF, or• Worsened on NYHA class
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Differences in Baseline Characteristics across Treatments
•42 baseline covariates examined
•4 lowest p-values
Covariate CorCap (n = 148)
Control (n = 152)
p-value
Female 53.4% 36.2% 0.001
Peak VO2 13.3 ml/kg/min
15.5 ml/kg/min
0.0005
DBP 68.9 mmHg 71.5 mmHg 0.053
Years since HF diagnosis
4.5 years 5.4 years 0.080
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Explanatory Variables used in Primary Endpoint Analysis
•MVR stratum
•Site Size (small, medium, large)
•Length of follow-up (“early”, “late” enrollee)
•3 baseline covariates Gender baseline peak VO2 DBP
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Primary Endpoint Model - Proportional Odds
Two possible binary logistic regression models:
1.“Success” = “Improved”; “Failure” = “same” or “worsened”
2.“Success” = “Improved” or “same”; “Failure” = “Worsened”
• Proportional odds model fits both models simultaneously, with common treatment effect
Improved Same Worsened> >
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Proportional Odds Property (constant difference in log odds)
• Proportionality: The odds of any higher category for trt1 are times the
odds for trt2
log( )
0H : 1; 0
HypotheticalIllustration
vs. Same or worsened vs. worsened
0.5
1.0
1.5
2.0
2.5
3.0
log
od
ds
Improved Improved or Same
trt 1
trt 2
= 1
= 1
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Non-Proportional Odds (non-constant log odds)
HypotheticalIllustration
0.5
1.0
1.5
2.0
2.5
3.0
log
od
ds
Improved Improved or Same
Trt 1
Trt 2 = 1.75
= 1
vs. Same or Worsened vs. Worsened
Comment: Is Proportional Odds assumption appropriate for the data?
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Missing Data in Primary Endpoint
• Assignment of NYHA class by site physician was unblinded
• Core lab assignment of NYHA class was done by a blinded cardiologist
• 42% of patients have baseline core lab NYHA assessments (CorCap n=61, Control n=65 available)
• The sponsor has shown a low concordance between the site-assessed and core lab NYHA
• 58% of baseline core lab NYHA assessments were filled-in or imputed using an imputation model
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Imputation Models
• Observed variables used to predict missing core lab baseline NYHA MVR stratum Site Size (small, medium, large) Length of follow-up (“early”, “late” enrollee) Duration of HF Age Baseline 6-MW Baseline MLHF score Baseline SF-36 score Ischemic/non-ischemic etiology Gender DBP Baseline Peak VO2 Baseline LVEF Baseline Site-assessed NYHA
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Imputation Models
•Imputation Model #1: Linear regression of baseline NYHA on observed baseline variables
•Imputation Model #2: Ordinal regression of baseline NYHA on observed baseline variables
•Multiple imputation techniques
•59% of CorCap and 55% of Control baseline NYHA values were imputed
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Assumption: Missing at Random
• Missing at random: Baseline NYHA is missing due only to enrollment time (and can be predicted from observed variables)
• Missing not at random: Baseline NYHA for “early” enrollees (before 7/4/2002) is distributed differently than for “later” enrollees.
• In an unblinded trial, there is a concern of selection bias in choosing patients who enter the trial.
• In this trial, a concern is that later enrollees may be less sick than earlier enrollees.
• Nonetheless, a selection bias might affect CorCap and Control roughly equally
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Selected Baseline Means by “Early” and “Later” Enrollees
Early Enrollee(N = 137)
Later Enrollee(N = 163)
p (t-test)
Site NYHA 2.93 2.85 0.12
LVEF 26.5 27.3 0.45
LVEDD 72.9 70.7 0.08
6MW (m) 333.5 347.1 0.19
VO2 13.1 13.0 0.93
LVESD 64.2 61.6 0.06
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Analysis of Primary Endpoint
Imputation Method Odds Ratio 95% CI p
No imputation; available data onlyCorCap: N = 93Control: N = 98(FDA Analysis)
1.57 (0.89, 2.79) 0.12
Linear regression imputationCorCap: N = 147Control: N = 146
1.73 (1.07, 2.79) 0.02
Ordinal regression imputation
1.69 (1.06, 2.72) 0.03
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Concerns about Imputation
•More than1/3 of patients are missing primary endpoint measurements. More than half of patients are missing baseline core lab NYHA.
•Results may be sensitive to violation of “missing at random” (MAR) assumption
•Comment: Discuss the reliability of analyses that used imputation.
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Concern about Proportional Odds Assumption
FDA’s Analysis of Primary Endpoint for Different Cut-points (using
available data): CorCap N = 93; Control N = 98Cutpoint Estimated Odds Ratios
Improved vs. (Same or Worsened)
2.00 (0.991, 4.036)
(Improved or Same) vs. Worsened
1.45 (0.793, 2.641)
Comment: Please discuss the appropriateness of the proportional odds assumption.
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Separate Analyses of Components of Primary Endpoint
•Which components contribute relatively more to the overall composite?
•Familywise error rate was not controlled a priori. P-values cannot be interpreted with respect to any significance level.
•A Bonferroni correction would imply a significance level of 0.05/3=0.017
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Separate Analysis of Mortality Component of Primary Endpoint
•Log-rank test of difference in KM survival curves p = 0.85
Cumulative Number of Deaths by Time
TreatmentN=148
ControlN=152
30 Days 7 (4.7%) 1 (0.7%)
12 months 19 (12.8%) 21 (13.8%)
24 months 22 (14.9%) 24 (15.8%)
Up to CCD 25 (16.9%) 25 (16.4%)
As of 4/15/2005 29 (19.6%) 33 (21.7%)
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Separate Analysis of Change in NYHA Component of Primary Endpoint
• Patients who had MCP or died do not have recorded NYHA at CCD
Model Odds Ratio 95% CI p
No imputation; available NYHA data only (FDA)CorCap: N=52Control: N=45
1.61 (0.71, 3.65) 0.25
Linear regression imputationCorCap: N=107Control: N=99
1.64 (0.87, 3.08) 0.12
Linear regression imputation
+ assume class IV for MCP
1.74 (1.00, 3.02) 0.049
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Analysis of MCP Contribution to Primary Endpoint
CorCap Control CMH Odds Ratio (95% CI)
19/148 = 12.8% 33/152 = 22% 2.22 (1.16, 4.20)p = 0.014
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Difficulty of Re-operation after CorCap
• A referral bias could arise if physicians were reluctant to refer CorCap patients for MCP
• This might have affected the relative number of patients who received MCP across treatment groups
• Could a referral bias account for an observed increase in percentage improved on NYHA for the CorCap vs. control groups?
• However, observed improvement on NYHA seen in CorCap vs. control was not statistically significant.
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Pre-specified “Major” Secondary Endpoints
•LVEDV, LVEF, MLHF, site-assessed NYHA Hochberg procedure to control
familywise type I error rate at 5% Hochberg p = 0.032 Presented individual p-values are
adjusted for multiplicity
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Multiple Secondary Endpoints:A Reminder
•If and only if the primary endpoint is met, pre-specified multiple secondary endpoints are tested as a set at an additional overall significance level.
•For any secondary endpoints for which multiple testing issues were not considered a priori, statistical significance cannot be interpreted
•The chance could be too high that the randomization to treatment groups resulted in an artificial “significant” difference on a few of many secondary endpoints.
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“Major” Secondary Endpoints
Difference in mean change over time (CorCap – Control)
Adjusted p
Site NYHA -0.04 0.98
LVEF 0.83% 0.98
MLHF -4.47 0.12
LVEDV -17.9 0.032
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Other Secondary Endpoints
•Other secondary endpoint tests were not controlled for multiple testing issues. P-values are not interpretable with respect to significance.
•Comment: Please comment on the use of tests of other secondary endpoints in making statements about intended use.
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Relationship between Structural and Functional Endpoints
• Low magnitude of correlation; low p-value does not imply high degree of concordance
-200 -100 0 100
-50
050
Change in LVEDV at 12 months
Cha
nge
in M
LHF
scor
e at
12
mon
ths r = 0.22
p = 0.003
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Stratum-Specific Analyses: A Reminder
•Power the study to detect a stratum X treatment interaction at a pre-specified significance level.
•If interaction is significant, perform tests within each stratum. A within-stratum analysis with a significant result can claim a treatment effect.
•If sample size is not large enough for interaction test, then tests within strata can be made for exploratory purposes
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Within-stratum Analyses of Primary Endpoint
•MVR stratum X Treatment not found to be significant
Model Odds Ratio 95% CI
NO MVR StratumN = 107
2.57 (1.09, 6.08)
MVR StratumN = 193
1.51 (0.84, 2.72)
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Within-stratum Analyses - by component
• MCPs
• Change in core NYHA from baseline
CorCap Control CMH Odds Ratio (95% CI)
No MVRN = 107
5/57 = 8.8% 12/50=24% 3.70 (1.12, 12.5)
MVRN = 193
14/91=15.4% 21/102=20.6% 1.63 (0.76, 3.57)
Model Odds Ratio 95% CI
NO MVR StratumN = 107
2.37 (0.72, 7.72)
MVR StratumN = 193
1.45 (0.66, 3.20)
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Within-stratum Analyses of Primary Endpoint
•MVR X Treatment Interaction not statistically significant (study not powered to detect)
•Larger observed treatment difference was seen in the stratum with smaller sample size (NoMVR n=107; MVR n=193)
•Observed difference across strata might be worth examining further
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Statistical Summary
•Sponsor met composite primary endpoint at 0.05 significance level
•Large amount of missing data may make inference uncertain
•Examination of separate components of composite shows strong influence of reduction in MCPs
•Difficult to determine if referral bias for MCP accounts for any of the perceived benefit of CorCap
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Statistical Summary (cont)
•Similar number of deaths in each treatment group
•Results from major secondary analyses were mixed with respect to finding a significant CorCap benefit
•Measures of cardiac structure do not show an association with functional status
•Treatment difference across MVR strata may not be consistent