analysis of life study by ethnic demographic subgroup
DESCRIPTION
Analysis of LIFE Study by Ethnic Demographic Subgroup. John Lawrence Mathematical Statistician FDA/CDER/Division of Biometrics I. I. General Issues in Analysis of Subgroups II. Other Relevant Studies III. LIFE study IV. Summary. Outline. I. General Issues in Analysis of Subgroups. - PowerPoint PPT PresentationTRANSCRIPT
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
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-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
Analysis of SubgroupsAnalysis of Subgroups
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Analysis of SubgroupsAnalysis of Subgroups
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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Analysis of SubgroupsAnalysis of Subgroups
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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Data from Other StudiesData from Other Studies
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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Data from Other StudiesData from Other Studies
““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-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.80
0.85
0.90
0.95
1.00
White Patients
Atenolol
Losartan
Adjusted p-value = 0.004Adjusted Hazard Ratio = 0.83495% CI = (0.736, 0.944)
Time in years
Eve
nt-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.80
0.85
0.90
0.95
1.00
Black Patients
Atenolol
Losartan
Adjusted p-value = 0.033Adjusted Hazard Ratio = 1.6695% CI = (1.04, 2.66)
Survival Curves- Primary Endpoint- By Race
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Study Year
Eve
nts
per 1
000
Pat
ient
Yea
rs
0 1 2 3 4 5 6
020
4060
8010
0White Patients
Atenolol
Losartan
Study Year
Eve
nts
per 1
000
Pat
ient
Yea
rs
0 1 2 3 4 5 6
020
4060
8010
0
Black Patients
Atenolol
Losartan
Annual Hazard Rates- Primary Endpoint- By Race
DCRDP Advisory Committee Meeting January 6, 2003
21Time in years
Eve
nt-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.92
0.94
0.96
0.98
1.00
White Patients
Atenolol
Losartan
Adjusted p-value = 0.12Adjusted Hazard Ratio = 0.85395% CI = (0.699, 1.04)
Time in years
Eve
nt-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.92
0.94
0.96
0.98
1.00
Black Patients
Atenolol
Losartan
Adjusted p-value = 0.24Adjusted Hazard Ratio = 1.4895% CI = (0.764, 2.88)
Survival Curves- CV Mortality- By Race
DCRDP Advisory Committee Meeting January 6, 2003
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Eve
nt-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.94
0.95
0.96
0.97
0.98
0.99
1.00
White Patients
Atenolol
Losartan
Adjusted p-value = 0.69Adjusted Hazard Ratio = 1.0495% CI = (0.848, 1.28)
Time in years
Eve
nt-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.94
0.95
0.96
0.97
0.98
0.99
1.00
Black Patients
Atenolol
Losartan
Adjusted p-value = 0.14Adjusted Hazard Ratio = 2.0795% CI = (0.786, 5.47)
Survival Curves- MI- By Race
DCRDP Advisory Committee Meeting January 6, 2003
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Eve
nt-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.88
0.90
0.92
0.94
0.96
0.98
1.00
White Patients
Atenolol
Losartan
Adjusted p-value < 0.0001Adjusted Hazard Ratio = 0.69795% CI = (0.583, 0.833)
Time in years
Eve
nt-fr
ee s
urvi
val
0 1 2 3 4 5 6
0.88
0.90
0.92
0.94
0.96
0.98
1.00
Black Patients
Atenolol
Losartan
Adjusted p-value = 0.030Adjusted Hazard Ratio = 2.1895% CI = (1.08, 4.40)
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 ProductsDoug Throckmorton, Tom Marciniak, Norman Doug Throckmorton, Tom Marciniak, Norman Stockbridge, Abraham Karkowsky and Jogarao Stockbridge, Abraham Karkowsky and Jogarao
GobburuGobburu
CBER and CDRHCBER and CDRHGregory Campbell, Gene Pennello, Telba Irony,Gregory Campbell, Gene Pennello, Telba Irony,
and David Banksand David Banks