1. DCRDP Advisory Committee Meeting January 6, 2003 1 Analysis of LIFE Study by Ethnic Demographic Subgroup John Lawrence Mathematical Statistician FDA/CDER/Division of Biometrics I

2. DCRDP Advisory Committee Meeting January 6, 2003 2 Outline I. General Issues in Analysis of Subgroups II. Other Relevant Studies III. LIFE study IV. Summary

3. DCRDP Advisory Committee Meeting January 6, 2003 3 I. General Issues in Analysis of Subgroups   =overall effect of drug relative to control -trial is designed to answer question about  uEffectiveness 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

4. DCRDP Advisory Committee Meeting January 6, 2003 4 Analysis of Subgroups uWhat 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

5. DCRDP Advisory Committee Meeting January 6, 2003 5 -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

6. DCRDP Advisory Committee Meeting January 6, 2003 6 Analysis of Subgroups uConfidence intervals for treatment effect in subgroups used to describe what was observed uExpect to see differences in point estimates uGenerally, no tests of hypotheses for subgroups (small sample sizes => low power) uAnalysis is usually post hoc- different ways of testing for interaction

7. DCRDP Advisory Committee Meeting January 6, 2003 7 Analysis of Subgroups uSubgroup analysis intended to explore uniformity of overall effect uUsually informative only when there is a significant overall effect uHigh 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

8. DCRDP Advisory Committee Meeting January 6, 2003 8 Quantitative vs. Qualitative Treatment Interaction  In general, expect differences in treatment effect across subgroups to be small relative to overall  uQuantitative Interaction - treatment effect varies in magnitude by subgroup, but is always in same direction uQualitative Interaction - direction of treatment effect varies by subgroup, sometimes positive, sometimes negative

9. DCRDP Advisory Committee Meeting January 6, 2003 9

10. 10 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

11. DCRDP Advisory Committee Meeting January 6, 2003 11 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 0 ii) if point estimates on opposite sides, standardize each by estimated standard error => test statistic is the one with smaller magnitude 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 0 ii) if point estimates on opposite sides, standardize each by estimated standard error => test statistic is the one with smaller magnitude

12. DCRDP Advisory Committee Meeting January 6, 2003 12 Summary- General Approach Subgroup analysis generally exploratory Different types of interactions and methods for subgroup analysis Look to biological plausibility or evidence from other studies to confirm observations Subgroup analysis generally exploratory Different types of interactions and methods for subgroup analysis Look to biological plausibility or evidence from other studies to confirm observations

13. DCRDP Advisory Committee Meeting January 6, 2003 13 II. Data from Other Studies uHypertension -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

14. DCRDP Advisory Committee Meeting January 6, 2003 14 Data from Other Studies uSOLVD -two large, randomized trials comparing ACE-inhibitor enalapril with placebo in patients with 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

15. DCRDP Advisory Committee Meeting January 6, 2003 15 Data from Other Studies uV-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

16. DCRDP Advisory Committee Meeting January 6, 2003 16 Data from Other Studies u“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

17. DCRDP Advisory Committee Meeting January 6, 2003 17 III. LIFE Study u9193 hypertensive patients with LVH randomized to losartan or atenolol 533 of patients were Black, nearly all Blacks from US uPrimary endpoint: stroke/MI/CV death overall estimated HR = 0.869 95% CI = (0.772, 0.979) p-value = 0.021 difference mainly in stroke component

18. DCRDP Advisory Committee Meeting January 6, 2003 18 Hazard Ratio and 95% CIs - Primary Endpoint

19. DCRDP Advisory Committee Meeting January 6, 2003 19 Survival Curves- Primary Endpoint- By Race

20. DCRDP Advisory Committee Meeting January 6, 2003 20 Annual Hazard Rates- Primary Endpoint- By Race

21. DCRDP Advisory Committee Meeting January 6, 2003 21 Survival Curves- CV Mortality- By Race

22. DCRDP Advisory Committee Meeting January 6, 2003 22 Survival Curves- MI- By Race

23. DCRDP Advisory Committee Meeting January 6, 2003 23 Survival Curves- Stroke- By Race

24. DCRDP Advisory Committee Meeting January 6, 2003 24 Relative Efficacy within Black Subgroups

25. DCRDP Advisory Committee Meeting January 6, 2003 25 Gail-Simon Test uNominal p-value for Black vs. Non-Black Qualitative Interaction = 0.016. uImpossible 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

26. DCRDP Advisory Committee Meeting January 6, 2003 26 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, U.S., Non-U.S., Males, or Females have a point estimate in opp. direction ] = 0.37 If true hazard ratio in all subgroups is 0.869: Prob[ Blacks point estimate in opp. direction ] = 0.28 Prob[ Blacks, Whites, 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 calculated as in Fleming T. (1995) Drug Info. J. (29):1681S-1687S

27. DCRDP Advisory Committee Meeting January 6, 2003 27 How Likely Is This Due to Chance? If true hazard ratio in all subgroups is 0.869: 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 shows a reversal of effect ] = 0.003 Prob[ CI for Blacks, Whites, Age 65, U.S., Non-U.S., Males, or Females shows a reversal of effect ] = 0.005 Prob[ CI for Blacks, Whites, Age 65, U.S., Non-U.S., Males, or Females shows a reversal of effect ] = 0.005

28. DCRDP Advisory Committee Meeting January 6, 2003 28 Other Approaches uAssume effects in subgroups come from a distribution, but can vary. uMany assumptions needed - variability of effects? - common mean? uCannot make strong conclusion without agreement on above.

29. DCRDP Advisory Committee Meeting January 6, 2003 29 Conclusions uNot rare for a subgroup to have point estimate in wrong direction- but, rare to have CI in wrong direction. uExactly how rare is impossible to determine from a post hoc analysis. Generally, post hoc analyses are hypothesis generating.

30. DCRDP Advisory Committee Meeting January 6, 2003 30 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

31. DCRDP Advisory Committee Meeting January 6, 2003 31 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