dcrdp advisory committee meeting january 6, 2003 1 analysis of life study by ethnic demographic...

DCRDP Advisory Committee Meeting January 6, 2003

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Analysis of LIFE Study by Ethnic Analysis of LIFE Study by Ethnic Demographic SubgroupDemographic Subgroup

John Lawrence John Lawrence Mathematical StatisticianMathematical Statistician

FDA/CDER/Division of Biometrics IFDA/CDER/Division of Biometrics I


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OutlineOutline

I. General Issues in Analysis of SubgroupsI. General Issues in Analysis of Subgroups

II. Other Relevant StudiesII. Other Relevant Studies

III. LIFE studyIII. LIFE study

IV. SummaryIV. Summary


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I. General Issues in Analysis of I. General Issues in Analysis of SubgroupsSubgroups

=overall effect of drug relative to control=overall effect of drug relative to control-trial is designed to answer question about -trial is designed to answer question about

Effectiveness is not uniform across individuals or Effectiveness is not uniform across individuals or across subgroupsacross subgroups-pharmacokinetic variability-pharmacokinetic variability-genetic or environmental differences-genetic or environmental differences-differences in disease pathogenesis-differences in disease pathogenesis see Wood (2001) see Wood (2001) NEJMNEJM 344 (18):1393 344 (18):1393


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Analysis of SubgroupsAnalysis of Subgroups

What does a successful clinical trial show?What does a successful clinical trial show?

-as a group a large number of patients treated with -as a group a large number of patients treated with the test drug would be better offthe test drug would be better off

-it does not show that every individual would be -it does not show that every individual would be better offbetter off

5DCRDP Advisory Committee Meeting January 6, 2003

-Subgroups can be surrogate markers for genetic -Subgroups can be surrogate markers for genetic or other factors that effect individual responses to or other factors that effect individual responses to a druga drug

see Exner et al. (2001) see Exner et al. (2001) NEJMNEJM 344(18):1351 344(18):1351



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Confidence intervals for treatment effect in Confidence intervals for treatment effect in subgroups used to describe what was observedsubgroups used to describe what was observed

Expect to see differences in point estimatesExpect to see differences in point estimates

Generally, no tests of hypotheses for subgroups Generally, no tests of hypotheses for subgroups (small sample sizes => low power)(small sample sizes => low power)

Analysis is usually post hoc- different ways of Analysis is usually post hoc- different ways of testing for interactiontesting for interaction


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Subgroup analysis intended to explore uniformity Subgroup analysis intended to explore uniformity of overall effectof overall effect

Usually informative only when there is a Usually informative only when there is a significant overall effectsignificant overall effect

High false positive or false negative rate High false positive or false negative rate

see Peto R. et al (1977) see Peto R. et al (1977) Brit. J. of CancerBrit. J. of Cancer (35): 1-39 (35): 1-39 ICH Topic E9 “Statistical Principles for Clinical Trials”: 30 ICH Topic E9 “Statistical Principles for Clinical Trials”: 30

Fleming T. (1995) Fleming T. (1995) Drug Info. J.Drug Info. J. (29):1681S-1687S (29):1681S-1687S


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Quantitative vs. Qualitative Quantitative vs. Qualitative Treatment InteractionTreatment Interaction

In general, expect differences in treatment effect In general, expect differences in treatment effect across subgroups to be small relative to overall across subgroups to be small relative to overall

Quantitative InteractionQuantitative Interaction- treatment effect varies in magnitude by - treatment effect varies in magnitude by subgroup, but is always in same directionsubgroup, but is always in same direction

Qualitative InteractionQualitative Interaction- direction of treatment effect varies by subgroup, - direction of treatment effect varies by subgroup, sometimes positive, sometimes negativesometimes positive, sometimes negative


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Quantitative Interaction

Favors Test Drug Favors Control

Qualitative Interaction


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Gail-Simon Test for Qualitative Gail-Simon Test for Qualitative InteractionInteraction

-likelihood ratio test-likelihood ratio test-null hypothesis: -null hypothesis: treatment effect in all subgroups are treatment effect in all subgroups are in same direction in same direction-compare likelihood of data under null hypothesis-compare likelihood of data under null hypothesis to likelihood of data under alternative hypothesis to likelihood of data under alternative hypothesis

see Gail and Simon (1985) see Gail and Simon (1985) BiometricsBiometrics (41): 361-373 (41): 361-373


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Gail-Simon Test for Qualitative Gail-Simon Test for Qualitative InteractionInteraction

Mechanics of test (assuming two subgroups):Mechanics of test (assuming two subgroups):

i) if point estimate of HR in both subgroups on i) if point estimate of HR in both subgroups on same side of 1, then no evidence of qualitative same side of 1, then no evidence of qualitative interaction => test statistic is 0interaction => test statistic is 0

ii) if point estimates on opposite sides, standardize ii) if point estimates on opposite sides, standardize each by estimated standard error => test statistic is each by estimated standard error => test statistic is the one with smaller magnitude the one with smaller magnitude


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Summary- General ApproachSummary- General Approach

Subgroup analysis generally exploratorySubgroup analysis generally exploratory

Different types of interactions and methods for Different types of interactions and methods for subgroup analysis subgroup analysis

Look to biological plausibility or evidence from Look to biological plausibility or evidence from other studies to confirm observationsother studies to confirm observations


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II. Data from Other StudiesII. Data from Other Studies

HypertensionHypertension

-losartan label: “COZAAR was effective in -losartan label: “COZAAR was effective in reducing blood pressure regardless of race, reducing blood pressure regardless of race, although the effect was somewhat less in black although the effect was somewhat less in black patients (usually a low-renin population)”patients (usually a low-renin population)”

-similar statement on some labels for -similar statement on some labels for beta-blockersbeta-blockers


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Data from Other StudiesData from Other Studies

SOLVDSOLVD-two large, randomized trials comparing-two large, randomized trials comparingACE-inhibitor enalapril with placebo in patientsACE-inhibitor enalapril with placebo in patientswith left ventricular dysfunctionwith left ventricular dysfunction-authors reported a significant reduction in risk of -authors reported a significant reduction in risk of hospitalization among white patients, but not in hospitalization among white patients, but not in blacksblacks

see Exner et al. (2001) see Exner et al. (2001) NEJMNEJM 344(18):1351 344(18):1351


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V-Heft IIV-Heft II-Patients with LVH, reduced exercise tolerance -Patients with LVH, reduced exercise tolerance or history of heart failure randomized to or history of heart failure randomized to enalapril or hydralazine + isosorbide dinitrate enalapril or hydralazine + isosorbide dinitrate

-authors reported that a reduction in mortality was -authors reported that a reduction in mortality was observed in whites but not in blacksobserved in whites but not in blacks

see Carson et al. (1999) see Carson et al. (1999) J. Card FailureJ. Card Failure (5): 178 (5): 178


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““these conclusions ... must be viewed as these conclusions ... must be viewed as hypothesis generating… A prospective trial of hypothesis generating… A prospective trial of black patients would be needed to test this black patients would be needed to test this hypothesis” hypothesis”

see Carson et al. (1999) see Carson et al. (1999) J. Card FailureJ. Card Failure (5): 178 (5): 178


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III. LIFE StudyIII. LIFE Study

9193 hypertensive patients with LVH 9193 hypertensive patients with LVH randomized to losartan or atenolol randomized to losartan or atenolol 533 of patients were Black, nearly all 533 of patients were Black, nearly all Blacks from USBlacks from US

Primary endpoint: stroke/MI/CV death Primary endpoint: stroke/MI/CV death overall estimated HR = 0.869 overall estimated HR = 0.869 95% CI = (0.772, 0.979) p-value = 0.02195% CI = (0.772, 0.979) p-value = 0.021difference mainly in stroke componentdifference mainly in stroke component


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Favors Losartan Favors Atenolol

Overall

United States

Male

Female

Black

White

Age<65

Age 65 or over

Hazard Ratio and 95% CIs - Primary Endpoint


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Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.8

00

.85

0.9

00

.95

1.0

0White Patients

Atenolol

Losartan

Adjusted p-value = 0.004Adjusted Hazard Ratio = 0.83495% CI = (0.736, 0.944)

Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.8

00

.85

0.9

00

.95

1.0

0

Black Patients

Atenolol

Losartan


Survival Curves- Primary Endpoint- By Race


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Study Year

Eve

nts

pe

r 1

00

0 P

atie

nt Y

ea

rs

0 1 2 3 4 5 6

02

04

06

08

01

00

White Patients

Atenolol

Losartan

Study Year

Eve

nts

pe

r 1

00

0 P

atie

nt Y

ea

rs

0 1 2 3 4 5 6

02

04

06

08

01

00

Black Patients

Atenolol

Losartan

Annual Hazard Rates- Primary Endpoint- By Race


21Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.9

20

.94

0.9

60

.98

1.0

0

White Patients

Atenolol

Losartan


Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.9

20

.94

0.9

60

.98

1.0

0

Black Patients

Atenolol

Losartan


Survival Curves- CV Mortality- By Race


22Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.9

40

.95

0.9

60

.97

0.9

80

.99

1.0

0White Patients

Atenolol

Losartan


Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.9

40

.95

0.9

60

.97

0.9

80

.99

1.0

0

Black Patients

Atenolol

Losartan


Survival Curves- MI- By Race


23Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.8

80

.90

0.9

20

.94

0.9

60

.98

1.0

0

White Patients

Atenolol

Losartan

Adjusted p-value < 0.0001Adjusted Hazard Ratio = 0.69795% CI = (0.583, 0.833)

Time in years

Eve

nt-

fre

e s

urv

iva

l

0 1 2 3 4 5 6

0.8

80

.90

0.9

20

.94

0.9

60

.98

1.0

0

Black Patients

Atenolol

Losartan


Survival Curves- Stroke- By Race


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Relative Efficacy within Black Relative Efficacy within Black SubgroupsSubgroups

n/NBlackSubgroup Los. Aten.

Hazardratio

95% CI p-value

All 46/270 29/263 1.67 (1.04, 2.7) 0.03Female 19/115 10/132 3.10 (1.4, 6.8) <0.01Male 27/155 19/131 1.21 (0.67, 2.2) 0.53

Age <65 14/123 7/147 2.52 (1.0, 6.3) 0.048Age >65 32/147 22/116 1.31 (0.76, 2.3) 0.33


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Gail-Simon TestGail-Simon Test Nominal p-value for Black vs. Non-Black Nominal p-value for Black vs. Non-Black

Qualitative Interaction = 0.016.Qualitative Interaction = 0.016.

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


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How Likely Is This Due to How Likely Is This Due to Chance?Chance?

If true hazard ratio in all subgroups is 0.869:If true hazard ratio in all subgroups is 0.869:

Prob[ Prob[Blacks point estimate in opp. directionBlacks point estimate in opp. direction] = 0.28] = 0.28

Prob[ Prob[Blacks, Whites, Age <65, Age >65, U.S., Blacks, Whites, Age <65, Age >65, U.S., Non-U.S., Males, or Females have a point Non-U.S., Males, or Females have a point

estimate in opp. direction estimate in opp. direction] = 0.37] = 0.37

calculated as in Fleming T. (1995) calculated as in Fleming T. (1995) Drug Info. J.Drug Info. J. (29):1681S-1687S (29):1681S-1687S


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How Likely Is This Due to How Likely Is This Due to Chance?Chance?

If true hazard ratio in all subgroups is 0.869:If true hazard ratio in all subgroups is 0.869:

Prob[Prob[CI for Blacks shows a reversal of effectCI for Blacks shows a reversal of effect] = 0.003] = 0.003

Prob[Prob[CI for Blacks, Whites, Age <65, Age >65, CI for Blacks, Whites, Age <65, Age >65, U.S., Non-U.S., Males, or Females shows a U.S., Non-U.S., Males, or Females shows a

reversal of effect reversal of effect] = 0.005] = 0.005


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Other ApproachesOther Approaches

Assume effects in subgroups come from aAssume effects in subgroups come from adistribution, but can vary.distribution, but can vary.

Many assumptions neededMany assumptions needed- variability of effects?- variability of effects?- common mean?- common mean?

Cannot make strong conclusion without Cannot make strong conclusion without agreement on above.agreement on above.


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ConclusionsConclusions

Not rare for a subgroup to have point estimate in Not rare for a subgroup to have point estimate in wrong direction- but, rare to have CI in wrong wrong direction- but, rare to have CI in wrong direction.direction.

Exactly how rare is impossible to determine Exactly how rare is impossible to determine from a post hoc analysis. Generally, post hoc from a post hoc analysis. Generally, post hoc analyses are hypothesis generating.analyses are hypothesis generating.


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ConclusionsConclusions

Factors that may decrease strength of evidenceFactors that may decrease strength of evidence- Multiple subgroups; many chances to find unusual things- Multiple subgroups; many chances to find unusual things- No pre-specified analysis to control for multiplicity- No pre-specified analysis to control for multiplicity

Factors that may increase strength of evidenceFactors that may increase strength of evidence- Possible racial differences observed in other related studies - Possible racial differences observed in other related studies - Consistency of effect within Black subgroups - Consistency of effect within Black subgroups - Consistency in components of primary endpoint - Consistency in components of primary endpoint - Consistency across different analysis methods - Consistency across different analysis methods


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AcknowledgementsAcknowledgementsCDER/Office of BiostatisticsCDER/Office of Biostatistics

Jim Hung, Robert O’Neill, Charles Anello, and Jim Hung, Robert O’Neill, Charles Anello, and George ChiGeorge Chi

CDER/Division of Cardio-Renal Drug ProductsCDER/Division of Cardio-Renal Drug Products

Doug Throckmorton, Tom Marciniak, Norman Doug Throckmorton, Tom Marciniak, Norman Stockbridge, Abraham Karkowsky and Jogarao Stockbridge, Abraham Karkowsky and Jogarao

GobburuGobburu

CBER and CDRHCBER and CDRH

Gregory Campbell, Gene Pennello, Telba Irony,Gregory Campbell, Gene Pennello, Telba Irony,and David Banksand David Banks

dcrdp advisory committee meeting january 6, 2003 1 analysis of life study by ethnic demographic...

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