Panel PresentationFDA Statistical Review1 Statistical Review of P040049 Acorn’s CorCap Cardiac...

Panel Presentation FDA Statistical Review 1

Statistical Review of P040049 Acorn’s CorCap Cardiac Support Device

Laura Thompson, Ph.D.Mathematical Statistician

CDRH/FDA


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


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


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


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


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


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> >


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


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?


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


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


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


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


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


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


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.


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.


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


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%)


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


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


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.


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


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.


“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


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.


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


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


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)


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)


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


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


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

Panel PresentationFDA Statistical Review1 Statistical Review of P040049 Acorn’s CorCap Cardiac...

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