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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

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statistical review of p040049 acorn s corcap cardiac support device

Statistical Review of P040049 Acorn’s CorCap Cardiac Support Device

Laura Thompson, Ph.D.

Mathematical Statistician

CDRH/FDA

FDA Statistical Review

outline
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

FDA Statistical Review

study design
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)

FDA Statistical Review

primary endpoint
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

FDA Statistical Review

differences in baseline characteristics across treatments
Differences in Baseline Characteristics across Treatments
  • 42 baseline covariates examined
  • 4 lowest p-values

FDA Statistical Review

explanatory variables used in primary endpoint analysis
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

FDA Statistical Review

primary endpoint model proportional odds
Primary Endpoint Model - Proportional Odds

Two possible binary logistic regression models:

  • “Success” = “Improved”; “Failure” = “same” or “worsened”
  • “Success” = “Improved” or “same”; “Failure” = “Worsened”
  • Proportional odds model fits both models simultaneously, with common treatment effect

>

>

Improved

Same

Worsened

FDA Statistical Review

proportional odds property constant difference in log odds
Proportional Odds Property (constant difference in log odds)
  • Proportionality: The odds of any higher category for trt1 are times the odds for trt2

Hypothetical

Illustration

vs. Same or worsened

vs. worsened

FDA Statistical Review

non proportional odds non constant log odds
Non-Proportional Odds (non-constant log odds)

Hypothetical

Illustration

vs. Worsened

vs. Same or Worsened

Comment:

Is Proportional Odds assumption appropriate for the data?

FDA Statistical Review

missing data in primary endpoint
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

FDA Statistical Review

slide11

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

FDA Statistical Review

imputation models
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

FDA Statistical Review

assumption missing at random
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

FDA Statistical Review

analysis of primary endpoint
Analysis of Primary Endpoint

FDA Statistical Review

slide16

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.

FDA Statistical Review

slide17

Concern about Proportional Odds Assumption

FDA’s Analysis of Primary Endpoint for Different Cut-points (using available data):

CorCap N = 93; Control N = 98

Comment:

Please discuss the appropriateness of the proportional odds assumption.

FDA Statistical Review

separate analyses of components of primary endpoint
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

FDA Statistical Review

slide19

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

FDA Statistical Review

separate analysis of change in nyha component of primary endpoint
Separate Analysis of Change in NYHA Component of Primary Endpoint
  • Patients who had MCP or died do not have recorded NYHA at CCD

FDA Statistical Review

difficulty of re operation after corcap
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.

FDA Statistical Review

slide23

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

FDA Statistical Review

multiple secondary endpoints a reminder
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.

FDA Statistical Review

major secondary endpoints
“Major” Secondary Endpoints

FDA Statistical Review

other secondary endpoints
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.

FDA Statistical Review

relationship between structural and functional endpoints
Relationship between Structural and Functional Endpoints
  • Low magnitude of correlation; low p-value does not imply high degree of concordance

p = 0.003

FDA Statistical Review

stratum specific analyses a reminder
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

FDA Statistical Review

within stratum analyses of primary endpoint
Within-stratum Analyses of Primary Endpoint
  • MVR stratum X Treatment not found to be significant

FDA Statistical Review

within stratum analyses by component
Within-stratum Analyses - by component
  • MCPs
  • Change in core NYHA from baseline

FDA Statistical Review

within stratum analyses of primary endpoint31
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

FDA Statistical Review

statistical summary
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

FDA Statistical Review

statistical summary cont
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

FDA Statistical Review