# Presentation: 711010

## Analysis of LIFE Study by Ethnic Demographic Subgroup

- John Lawrence
- Mathematical Statistician
- FDA/CDER/Division of Biometrics I

- DCRDP Advisory Committee Meeting           January 6, 2003

## Outline

- I. General Issues in Analysis of Subgroups
- II. Other Relevant Studies
- III. LIFE study
- IV. Summary

- DCRDP Advisory Committee Meeting           January 6, 2003

## I. General Issues in Analysis of Subgroups

- =overall effect of drug relative to control-trial is designed to answer question about 
- Effectiveness is not uniform across individuals or across subgroups-pharmacokinetic variability-genetic or environmental differences-differences in disease pathogenesis    see Wood (2001) *NEJM* 344 (18):1393

- DCRDP Advisory Committee Meeting           January 6, 2003

## Analysis of Subgroups

- What does a successful clinical trial show?-as a group a large number of patients treated with the test drug would be better off-it does not show that every individual would be better off

- DCRDP Advisory Committee Meeting           January 6, 2003

## -Subgroups can be surrogate markers for genetic or other factors that effect individual responses to a drug  see Exner et al. (2001) NEJM 344(18):1351

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- -Subgroups can be surrogate markers for genetic or other factors that effect individual responses to a drug  see Exner et al. (2001) *NEJM* 344(18):1351

- Analysis of Subgroups

## Analysis of Subgroups

- Confidence intervals for treatment effect in subgroups used to describe what was observed
- Expect to see differences in point estimates
- Generally, no tests of hypotheses for subgroups (small sample sizes => low power)
- Analysis is usually post hoc- different ways of testing for interaction

- DCRDP Advisory Committee Meeting           January 6, 2003

## Analysis of Subgroups

- Subgroup analysis intended to explore uniformity of overall effect
- Usually informative only when there is a significant overall effect
- High false positive or false negative rate    see Peto R. et al (1977) *Brit. J. of Cancer* (35): 1-39           ICH Topic E9 “Statistical Principles for Clinical Trials”: 30           Fleming T. (1995) *Drug Info. J.* (29):1681S-1687S

- DCRDP Advisory Committee Meeting           January 6, 2003

## Quantitative vs. Qualitative Treatment Interaction

- In general, expect differences in treatment effect across subgroups to be small relative to overall 
- _Quantitative Interaction_- treatment effect varies in magnitude by subgroup, but is always in same direction
- _Qualitative Interaction_- direction of treatment effect varies by subgroup, sometimes positive, sometimes negative

- DCRDP Advisory Committee Meeting           January 6, 2003

## Slide 9

- DCRDP Advisory Committee Meeting           January 6, 2003

## Gail-Simon Test for Qualitative Interaction

- -likelihood ratio test-null hypothesis:     treatment effect in all subgroups are    in same direction-compare likelihood of data under null hypothesis to likelihood of data under alternative hypothesis   see Gail and Simon (1985) *Biometrics* (41): 361-373

- DCRDP Advisory Committee Meeting           January 6, 2003

## Gail-Simon Test for Qualitative Interaction

-     Mechanics of test (assuming two subgroups):i) if point estimate of HR in both subgroups on same side of 1, then no evidence of qualitative interaction => test statistic is 0ii) if point estimates on opposite sides, standardize each by estimated standard error => test statistic is  the one with smaller magnitude

- DCRDP Advisory Committee Meeting           January 6, 2003

## Summary- General Approach

-     Subgroup analysis generally exploratoryDifferent types of interactions and methods for subgroup analysis Look to biological plausibility or evidence from other studies to confirm observations

- DCRDP Advisory Committee Meeting           January 6, 2003

## II. Data from Other Studies

- Hypertension-losartan label: “COZAAR was effective in reducing blood pressure regardless of race, although the effect was somewhat less in black patients (usually a low-renin population)”-similar statement on some labels for beta-blockers

- DCRDP Advisory Committee Meeting           January 6, 2003

## Data from Other Studies

- SOLVD-two large, randomized trials comparingACE-inhibitor enalapril with placebo in patientswith left ventricular dysfunction-authors reported a significant reduction in risk of hospitalization among white patients, but not in blacks see Exner et al. (2001) *NEJM* 344(18):1351

- DCRDP Advisory Committee Meeting           January 6, 2003

## Data from Other Studies

- V-Heft II-Patients with LVH, reduced exercise tolerance   or history of heart failure randomized to   enalapril or hydralazine + isosorbide dinitrate-authors reported that a reduction in mortality was observed in whites but not in blacks  see Carson et al. (1999) *J. Card Failure* (5): 178

- DCRDP Advisory Committee Meeting           January 6, 2003

## Data from Other Studies

- “these conclusions ... must be viewed as hypothesis generating... A prospective trial of black patients would be needed to test this hypothesis”    see Carson et al. (1999) *J. Card Failure* (5): 178

- DCRDP Advisory Committee Meeting           January 6, 2003

## III. LIFE Study

- 9193 hypertensive patients with LVH  randomized to losartan or atenolol 533 of patients were Black, nearly all Blacks from US
- Primary endpoint: stroke/MI/CV death overall estimated HR = 0.869 95% CI = (0.772, 0.979)      p-value = 0.021difference mainly in stroke component

- DCRDP Advisory Committee Meeting           January 6, 2003

## Hazard Ratio and 95% CIs - Primary Endpoint

- DCRDP Advisory Committee Meeting           January 6, 2003

## Survival Curves- Primary Endpoint- By Race

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## Annual Hazard Rates- Primary Endpoint- By Race

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## Survival Curves- CV Mortality- By Race

- DCRDP Advisory Committee Meeting           January 6, 2003

## Survival Curves- MI- By Race

- DCRDP Advisory Committee Meeting           January 6, 2003

## Survival Curves- Stroke- By Race

- DCRDP Advisory Committee Meeting           January 6, 2003

## Relative Efficacy within Black Subgroups

- DCRDP Advisory Committee Meeting           January 6, 2003

## Gail-Simon Test

- Nominal p-value for Black vs. Non-Black Qualitative Interaction = 0.016.
- Impossible to correctly adjust this p-value for multiple comparisons post hoc. -3 subgroups pre-specified for special importance (U.S. region, Diabetics, ISH)  -formal analysis plan would list all important subgroups and specify a method to correctly adjust for number of tests

- DCRDP Advisory Committee Meeting           January 6, 2003

## How Likely Is This Due to Chance?

-      If true hazard ratio in all subgroups is 0.869: Prob[Blacks point estimate in opp. direction] = 0.28 Prob[Blacks, Whites, Age <65, Age >65, U.S.,      Non-U.S., Males, or Females have a point      estimate in opp. direction] = 0.37
-      calculated as in  Fleming T. (1995) *Drug Info. J.* (29):1681S-1687S

- DCRDP Advisory Committee Meeting           January 6, 2003

## How Likely Is This Due to Chance?

-    If true hazard ratio in all subgroups is 0.869:
-    Prob[CI for Blacks shows a reversal of effect] = 0.003
-    Prob[CI for Blacks, Whites, Age <65, Age >65,     U.S., Non-U.S., Males, or Females shows a     reversal of effect] = 0.005

- DCRDP Advisory Committee Meeting           January 6, 2003

## Other Approaches

- Assume effects in subgroups come from adistribution, but can vary.
- Many assumptions needed- variability of effects?- common mean?
- Cannot make strong conclusion without agreement on above.

- DCRDP Advisory Committee Meeting           January 6, 2003

## Conclusions

- Not rare for a subgroup to have point estimate in wrong direction- but, rare to have CI in wrong direction.
- Exactly how rare is impossible to determine from a post hoc analysis.  Generally, post hoc analyses are hypothesis generating.

- DCRDP Advisory Committee Meeting           January 6, 2003

## Conclusions

- _Factors that may decrease strength of evidence_
- - Multiple subgroups; many chances to find unusual things
- - No pre-specified analysis to control for multiplicity
- _Factors that may increase strength of evidence_
- - Possible racial differences observed in other related studies
- - Consistency of effect within Black subgroups
- - Consistency in components of primary endpoint
- - Consistency across different analysis methods

- DCRDP Advisory Committee Meeting           January 6, 2003

## Acknowledgements

- _CDER/Office of Biostatistics_
- Jim Hung, Robert O’Neill, Charles Anello, and George Chi
- _CDER/Division of Cardio-Renal Drug Products_
- Doug Throckmorton, Tom Marciniak, Norman Stockbridge, Abraham Karkowsky and Jogarao Gobburu
- _CBER and CDRH_
- Gregory Campbell, Gene Pennello, Telba Irony,
- and David Banks

- DCRDP Advisory Committee Meeting           January 6, 2003