Statistics 542 Intro to Clinical Trials Data Monitoring, Monitoring Committee Function

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Statistics 542Intro to Clinical TrialsData
Monitoring, Monitoring Committee Function
Statistical Methods
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Some References

• Texts/Chapters
• 1. Friedman, Furberg DeMets (1998) 3rd
  edition, Fundamentals of Clinical Trials,
  Springer-Verlag, NY, NY
• 2. Pocock (1983) Clinical Trials, Wiley.
• 3. Ellenberg S, Fleming T and DeMets D Data
  Monitoring Committees in Clinical Trials A
  Practical Perspective. John Wiley Sons, Ltd.,
  West Sussex, England, 2002.
• 4. Jennison C and Turnbull B (2000) Group
  Sequential Methods with Application to Cinical
  Trials. Chapman Hall, NY.
• 5. DeMets DL (1998) Data and Safety Monitoring
  Boards. In Encyclopedia of Biostatistics.
  John Wiley and Sons, West Sussex, England, Vol.
  2, pp. 1067-71.
• 6. DeMets and Lan. The alpha spending function
  approach to interim data analysis. In, Recent
  Advances in Clinical Trials Design and
  Analysis. Kluwer Academic Publishers, Boston,
  MA, 1995.

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Some References

• Review Papers
• 1. Greenberg ReportOrganization, review, and
  administration of cooperative studies.
  Controlled Clinical Trials 9137-148, 1988.
• 2. DeMets and Lan (1994) Interim analyses
  The alpha spending function approach.
  Statistics in Medicine, 13(13/14)1341-52, 1994.
• 3. Lan and Wittes. The B-value A tool for
  monitoring data. Biometrics 44579-585, 1988.
• 4. Task Force of the Working Group on
  Arrhythmias of the European Society
  of Cardiology The early termination of clinical
  trials causes, consequences, and control.
  Circulation 89(6)2892-2907, 1994.
• 5. Fleming and DeMets Monitoring of clinical
  trials issues and recommendations. Controlled
  Clin Trials 14183-97, 1993.
• 6. DeMets, Ellenberg, Fleming, Childress, et al
  The Data and Safety Monitoring Board and AIDS
  clinical trials. Controlled Clin Trials
  16408-21, 1995.
• 7. Armstrong and Furberg Clinical trial data
  and safety monitoring boards The search for a
  constitution. Circulation 1, Sess6, 1994.

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Data MonitoringRationale

• 1. Ethical
• 2. Scientific
• 3. Economic

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A Brief History

• A 35-year history
• Greenberg Report (1967)
• Coronary Drug Project (1968)
• NIH Experience and Guidelines
• Industry and ICH Guidelines
• Department of Health Human Services Policy
  (Shalala, 2000)

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Greenberg Report Recommendations

• Develop a mechanism to terminate early if
• Question already answered
• Trial cant achieve its goals
• Unusual circumstances
• Hypothesis no longer relevant
• Sponsor decision to terminate should be based on
  advice of external committee

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Coronary Drug Project (CDP)

• References
• Design (Circulation, 1973)
• Monitoring Experience (CCT, 1981)
• Major Outcome (JAMA 1970, 1972, 1973, 1975)
• Tested several lipid lowering drugs in post MI
  patients
• Multicenter study
• Mortality as primary outcome
• Began recruitment in 1965

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Coronary Drug Project

• First trial to benefit from Greenberg Report
• Policy Advisory Board
• Senior Investigators, External Experts, NHI
• Initially reviewed interim data
• Data Coordinating and Statistical Center
• Safety Monitoring Committee formed (1968), after
  trial was underway

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Early NHLBI CT Model
Funding Agency

Policy Advisory Board
Data and Safety Monitoring Board
Steering Committee
Central Lab(s)
Multiple Clinics
Working Committees
Data Coordinating Center Data Management Statistic
al Analysis
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NHLBI CT Model

Funding Agency
Data Monitoring Committee
Steering Committee
Central Lab(s)
Coordinating Data Center
Clinics
Working Committees
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NIH DMC Activity

• Ref Statistics in Medicine (1993)
• CDP became model for National Heart, Lung, and
  Blood Institute (NHLBI)
• heart, lung, blood disease trials
• National Eye Institute (NEI) (1972)
• Diabetic Retinopathy Study
• National Institute Diabetes, Digestive and Kidney
  (NIDDK)
• Diabetes Complication and Control Trial (1980)
• National Cancer Institute (NCI)
• Prevention Trials, Cooperative Group Therapeutic
  Trials
• National Institute Allergy and Infectious Disease
  (NIAID)
• AIDS Clinical Trial Group (ACTG) (1986)

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Industry/FDA/ICH

• Industry sponsorship of RCTs expanded
  dramatically since 1990 in several disease areas
  (e.g. cardiology, cancer, AIDS)
• Industry use of DMCs growing as well
• FDA 1989 guidelines very brief mention of data
  monitoring and DMCs
• International Conference on Harmonization (ICH)
• ICH/E9
• Section 4.5 Interim Analyses
• Section 4.6 Independent DMCs
• ICH/E6

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Independent DMCsWhen are they Needed?

• Department of Health and Human Services Policy
• Shalala (NEJM, 2000) All NIH FDA trials must
  have a monitoring plan, for some a DMC may be
  required
• NIH policy (1998)
• all sponsored trials must have a monitoring
  system
• safety, efficacy and validity
• DMC for Phase III trials
• FDA guidelines (Nov 2001)

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Need for Independent DMCs

• Phase I Trials (dose)
• Monitoring usually at local level
• Phase II Trials (activity)
• Most monitoring at local level
• Some randomized, blinded, multicenter Phase II
  trials may need IDMC
• Phase III IV (effectiveness, risk, benefit)
• Most frequent user of IDMC
• Structure of monitoring depends on risk (e.g.
  Phase I-IV)

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Data Monitoring Committee

• FDA suggests a need for an
• Independent DSMB for
• Pivotal Phase IIIs
• Mortality or irreversible
• morbidity outcome

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Industry-Modified NIH Model
Pharmaceutical Industry Sponsor
Steering Committee
Regulatory Agencies

Independent Data Monitoring Committee (IDMC)
Central Units (Labs, )
Data Management Center (Sponsor or CRO)
Statistical Analysis Center
Clinical Centers
Institutional Review Board
Patients
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DMC Relationshipsand Responsibilities

• Patients
• Study Investigators
• Sponsor
• Local IRBs
• Regulatory Agencies

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Early Administrative AnalysisDMC and Executive
Committee

• 1. Recruitment/Entry Criteria
• 2. Baseline Comparisons
• 3. Design Assumptions
• a. Control only
• b. Combined groups

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Design Modifications

• 1. Entry Criteria
• 2. Treatment Dose
• 3. Sample Size Adjustment
• 4. Frequency of Measurements

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DMC Data ReviewInterim Analysis

• 1. Recruitment
• 2. Baseline Variables
• -Eligibility
• -Comparability
• 3. Outcome Measures
• -Primary
• -Secondary
• 4. Toxicity/Adverse Effects
• 5. Compliance
• 6. Specified Subgroups

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DMC Recommendations

• 1. Continue Trial / Protocol Unmodified
• 2. Modify Protocol
• 3. Terminate Trial

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Reasons for Early Termination

• 1. Serious toxicity
• 2. Established benefit
• 3. Futility or no trend of interest
• 4. Design, logistical issues too serious to fix

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DMC Decision Making Process Complex (1)

• Recruitment Goals
• Baseline risk and comparability
• Compliance
• Primary and secondary outcomes
• Safety

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DMC Decision Making Process Complex (2)

• Internal consistency
• External consistency
• Benefit/Risk
• Current vs future patients
• Clinical/Public impact
• Statistical issues

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DMC Decision Making Role

• DMC makes recommendations, not final decisions
• Independent review provides basis for DMC
  recommendations
• DMC makes recommendations to
• Executive Committee who recommends to sponsor,
  or
• Sponsor
• DMC may, if requested, debrief Executive
  Committee and/or sponsor
• Rarely are DMC recommendations rejected

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DMC Meeting Format

• Open Session
• Progress, blinded data
• Sponsor, Executive Committee, DMC, SAC
• Closed Session
• Unblinded data
• DMC, SAC
• Sponsor Rep? (Not recommended)
• Executive Session
• DMC only
• Debriefing Session
• DMC Chair, Sponsor Rep, Executive Committee Rep

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DMC Relationships

• Regulatory Agencies (e.g. FDA)
• Could perhaps brief DMC about specific concerns
  at Open Session
• Should not participate in DMC Closed Sessions
• Should be briefed about DMC recommendations/decisi
  ons ASAP following Executive Committee

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DMC Membership

• Monitoring is complex decision process and
  requires a variety of expertise
• Needed expertise
• Clinical
• Basic science
• Clinical trial methodology
• Biostatistics
• Epidemiology
• Medical ethics
• Helpful expertise
• Regulatory
• Some experience essential

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DMC Confidentiality

• In general, interim data must remain confidential
• DMC may rarely release specific/limited interim
  data (e.g. safety issue)
• Members must not share interim data with anyone
  outside DMC
• Leaks can affect
• Patient Recruitment
• Protocol Compliance
• Outcome Assessment
• Trial Support

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DMC Liability

• Recent events (eg Cox-IIs, Vioxx) have raised the
  potential for litigation
• Members have been gotten a subpoena
• DMC Charters for industry trials now often cover
  indemnification clauses
• No indemnification yet for NIH trials

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DMC Needs On-LineData Management and Analysis

• DMC reluctant to make decisions on old data
• Minimize data delay and event verification (e.g.
  NOTT, ACTG 019)
• Be prepared from start (e.g., CAST)
• Focus on key variables, not complete case reports
  (delays can be problematic)

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Levels of Independence

• Totally Inhouse Coordinating Center
• Internal DM, Internal SAC, External DMC
• Internal DM, External SAC, External DMC
• External DM(e.g. CRO), External SAC, External DMC

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Industry-Modified NIH Model
Pharmaceutical Industry Sponsor
Steering Committee
Regulatory Agencies

Independent Data Monitoring Committee (IDMC)
Central Units (Labs, )
Data Management Center (Sponsor or CRO)
Statistical Analysis Center
Clinical Centers
Institutional Review Board
Patients
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DMC Summary

• NIH Clinical Trial Model - long history of
  success
• Adaptation for industry can be made
• SC, DMC, SAC or DM are critical components
• Independence of DMC essential
• Best way to achieve this goal is for external SAC
  and external DMC

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Data Monitoring Process

• 1. DMC and the decision process
• 2. A brief introduction to statistical
  monitoring methods
• a. Group Sequential
• b. Stochastic Curtailment
• 3. Examples
• Ref BHAT, DeMets et al. Controlled
  Clin Trials,1984

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Decision Factors

• 1. Comparability
• 2. Bias
• 3. Compliance
• 4. Main effect vs. Potential side effects
• 5. Internal Consistency
• a. Outcome measures
• b. Subgroups
• c. Centers
• 6. External Consistency
• 7. Impact
• 8. Statistical Issues/Repeated Testing

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Beta-blocker Heart Attack Trial (BHAT)

• Preliminary Report. JAMA 2462073-2074, 1981
• Final Report. JAMA 2471707-1714, 1982
• Design Features
• Mortality Outcome 3,837 patients
• Randomized Men and women
• Double-blind 30-69 years of age
• Placebo-controlled 5-21 days post-M.I.
• Extended follow-up Propranolol-180 or 240 mg/day

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BHATAccumulating Survival Data

• Date Data Monitoring
• Committee Meeting Propranolol Placebo Z(log
  rank)
• May 1979 22/860 34/848 1.68
• Oct 1979 29/1080 48/1080 2.24
• March 1980 50/1490 76/1486 2.37
• Oct 1980 74/1846 103/1841 2.30
• April 1981 106/1916 141/1921 2.34
• Oct 1981 135/1916 183/1921 2.82
• June 1982
• Data Monitoring Committee recommended
  termination

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Beta-Blocker Heart Attack Trial October 1,
1981LIFE-TABLE CUMULATIVE MORALITY CURVES
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Beta-Blocker Heart Attack TrialBaseline
Comparisons

• Propranolol Placebo
• (N1,916) (N1,921)
• Average Age (yrs.) 55.2 55.4
• Male () 83.8 85.2
• White () 89.3 88.4
• Systolic B.P. 112.3 111.7
• Diastolic B.P. 72.6 72.3
• Heart rate 76.2 75.7
• Cholesterol 212.7 213.6
• Current smoker () 57.3 56.8

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Beta-Blocker Heart Attack TrialTotal
Mortality(Average 24-Month Follow-Up)

• Propranolol Placebo
• Age 30-59 5.9 7.1
• 60-69 9.6 14.4
• Sex Male 7.0 9.3
• Female 7.1 10.9
• Race White 6.7 9.0
• Black 11.0 15.2

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Beta-Blocker Heart Attack TrialTotal
Mortality(Average 24-Month Follow-Up)

• Propranolol Placebo
• Risk Group I 13.5 16.9
• Risk Group II 7.8 11.4
• Risk Group III 5.2 7.1

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DMC Interim Analysis

• Ethical, scientific and financial reasons
• Repeated analysis of accumulating data causes a
  statistical problem

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Coronary Drug Project
Cumulative morality rate
Month of Follow-up
Life-table cumulative mortality rates, Coronary
Drug Research Project Group
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Coronary Drug Project Research Group
Placebo Superior
Clofibrate Superior
z values for clofibrate-placebo differences in
proportion of deaths by calendar month since
beginning of study (Month 0 March 1966, Month
100 July 1974)
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Repeated Significance Testing

• Repeated testing increases Type I error AMR
  (JRSS, 1969)
• Example
• Critical Value 1.96
• Reject H0 if Z gt 1.96
• No. Of Looks (Planned) Type I Error
• 1 0.05
• 2 0.08
• ...
• 5 0.14
• ...
• 10 0.20
• Must adjust interpretation of z to be
  conservative.

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Three Procedures for Conservative Interim
Monitoring

• 1. Group Sequential
• A modification of classical sequential
• 2. Curtailed Sampling/Conditional Power
• 3. Bayesian (not discussed here)
• FIRST TWO METHODS HAVE PRIMITIVE VERSIONS IN
  CORONARY DRUG PROJECT

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Group Sequential BoundariesBasic Idea

• Compute summary statistic at each
  interim analyses, based on additional group of
  new subjects (events)
• Compare statistic to a conservative critical
  value
• 2? 0.05 overall
• Various Methods
• Haybittle-Peto (1971, 1976)
• Pocock (1977)
• OBrien-Fleming (1979)
• Slud and Wei (1982)
• Lan and DeMets (1983)

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Group Sequential Boundaries
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Haybittle-Peto Group Sequential Boundaries

• Simple, Ad Hoc
• For interim analyses, use very conservative
  critical value
• e.g., 3.0
• For final analysis, no adjustment really needed
• e.g., 1.96
• Significance level approximate
• e.g., 0.05
• References Haybittle. British Journal of
  Radiology, 1971
• Peto et al. British Journal of Cancer, 1976
• Refinement
• Fleming, Harrington, OBrien. Controlled
  Clinical Trials, 1984

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Classical Group Sequential Boundaries

• Proposal
• Analysis after groups of 2n subjects
• Maximum of K groups, independent
• Same critical value at each analysis
• Basic Model
• Zj statistics for jth group
• j 1, 2, ..., K (K groups, 2n each)
• Zj N(?,1), independent
• H0 ? 0
• References
• Armitage, McPherson, and Rowe, J Royal
  Statistical Society, 1969
• McPherson and Armitage, Journal of the Royal
  Statistical Society, 1971
• Pocock. Biometrika, 1977

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Summary Statistic

• Add up statistic for each sequential group
• That is
• This is often the usual test statistic

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Summary Statistic

• - Let
• - H0 ? 0
• - Decision
• 1. Continue if -Zp ltZilt Zp for i lt K
• 2. Otherwise, stop and reject H0 for i lt K
• - Pick Zp to give overall ?
• 3. If i K
• Reject H0 if Zi gt Zp
• Accept H0 if Zi lt Zp

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• the summary statistics for all data in
  the 1st i groups
• Binomial a.
• Normal b.
• Survival c.
• Process assumes independent increments

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Pocock (1977) Group Sequential Boundaries

• Boundaries
• Zp
• Example
• - " 0.05, K 5, Zp 2.413
• - 1 - ? 0.90 ? ? 1.59
• Compare Two Sample Means where H0µC µT

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Pocock (1977) Group Sequential Boundaries

• Design
• - e.g., HA µT - µC 0.5 ?
• -
• 20
• - 2nK 2(20)5
• 200 subjects

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OBrien-Fleming (1979)Group Sequential Boundary

• Basic Model
• Continue if
• for i lt K
• Critical value decreases as i increases
• For i K, Reject H0 if ZKgtZOBF
• Boundary Example
• Values
• i1 2.04 v(5/1) 4.88
• i2 2.04 v(5/2) 3.36
• i3 2.04 v(5/3) 2.68
• i4 2.04 v(5/4) 2.29
• i5 2.04 v(5/5) 2.04
• Reference OBF, Biometrics, 1979

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OBrien-Fleming (1979)Group Sequential Boundary

• Design Example
• Reference OBF, Biometrics, 1979

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Fixed Sample SizeComparison of Means

• Sample size 2? .05 Power .90 ?/? .5

N 84 2N 168
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Group Sequential Boundaries

• 2? .05
• Power .90, .80
• OBrien-Fleming Pocock
• ?OBF ?P
• K ZOBF 90 80 ZP 90 80
• 1 1.96 3.24 2.80 1.96 3.24 2.80
• 2 1.98 2.30 1.99 2.18 2.40 1.98
• 3 1.99 1.88 1.63 2.29 2.01 1.63
• 4 2.03 1.64 1.42 2.36 1.76 1.53
• 5 2.04 1.46 1.27 2.41 1.59 1.38

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Group Sequential Boundaries

• 2? .01
• Power .90, .80
• OBrien-Fleming Pocock
• ?OBF ?P
• K ZOBF 90 80 ZP 90 80
• 1 2.58 3.86 3.42 2.58 3.86 3.42
• 2 2.58 2.73 2.42 2.77 2.84 2.55
• 3 2.59 2.23 1.98 2.87 2.36 2.12
• 4 2.60 1.94 1.72 2.94 2.07 1.86
• 5 2.62 1.74 1.54 2.99 1.87 1.67

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Group Sequential Boundaries
63
BHAT GSB
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Lan-DeMets (1983)Group Sequential Boundaries

• Criticism of classical GSB
• Number of analyses specified in advance
• Equal increments in information
• Lan and DeMets specify ?(t)
• ?(t) defines rate at which Type I error is used
  where t  is proportion of total information
  accumulated by calendar time tC
• So 0 lt t lt 1
• Thus ?(t) increasing
• ? (0) 0
• ?(1) ?

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• Information and Calendar Time
• t proportion of information accumulated by tc
• Examples
• A. Immediate Response
• x1, x2, . . . . , xn, . . . ., xN,
• y1, y2, . . . . , yn, . . . ., yN,
• tc
• t 2n/2N n/N
• B. Failure Time (e.g. logrank)

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Group Sequential Methods
K 5, ? .05
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Boundary Crossing Probability

• E.g., K 5, ? 0.025
• Upper Boundary
• C1 C2 C3 C4 C5
• Pocock (2.41, 2.41, 2.41, 2.41, 2.41)
• OBF (4.56, 3.23, 2.63, 2.28, 2.04)
• Pocock OBF
• 1. P Z1 gt C1 0.0079 (0.000)
• 2. P Z1 gt C1 or Z2 gt C2
• 0.0079 0.0059 0.0138 (0.0006)
• 3. P Z1 gt C1 or Z2 gt C2 or Z3 gt C3
• 0.0138 0.0045 0.0183 (0.0045)
• 4. P Z1 gt C1, ..., Z4 gt C4
• 0.0183 0.0036 0.0219 (0.0128)
• 5. P Z1 gt C1, ..., Z5 gt C5
• 0.0219 0.0031 0.0250 (0.0250)

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(No Transcript)
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(t2) - ? (t1)
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Examples of ?(t)

• Approximates
• 1. OBF
• 2. ?2 (t) ? ln 1 (e - 1)t Pocock
• 3. ?3 (t) ?t
• Comparison of Boundaries (? .025, N 5)
• Values C1 C2 C3 C4 C5
• 1. OBF 4.56 3.23 2.63 2.28 2.04
• ?1(t) 4.90 3.35 2.68 2.29 2.03
• 2. Pocock 2.41 2.41 2.41 2.41 2.41
• ?2(t) 2.44 2.43 2.41 2.40 2.38
• 3. ?3(t) 2.58 2.49 2.41 2.34 2.28

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Cardiac Arrhythmia Suppression Trial (CAST)

• Ref NEJM 321(6)406-12, 1989
• Cardiac arrhythmias associated with increased
  risk of sudden death
• New class of drugs (eg, encainide, flecanide)
  suppressed arrhythmias
• CAST designed to test effect on sudden death

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CAST GSB

• ? spending function approach
• ?(t) ½ ? t t lt 1
• ? t 1
• for benefit ? 0.025
• Used symmetric ? 0.025 boundary for harm

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CAST Interim DataSudden Death

• Time Placebo Drug LogRank ZL ZU
• 9/01/88 5/576 22/571 -2.82 -3.18 3.01
• 3/30/89 9/725 33/730 -3.22 -3.04 2.71
• Initially expected 100 events/arm

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CAST Sequential Boundaries
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Estimation of Treatment Effect

• If terminated early
• Need adjusted estimates
• Naive estimate typically too low
• Naive confidence interval (CI) does not have
  correct coverage
• Need an adjusted CI
• Some References
• Classical GSB Tsiatis, Rosner, Mehta.
  Biometrics, 1984
• Fan DeMets, 2000.

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Repeated Confidence Intervals

• Use RCIs
• Calculate RCI using GSB boundary
• Specify as least medically significant
  difference
• Otherwise continue
• References
• Jennison and Turnbull. Controlled Clinical
  Trials, 1984
• Jennison and Turnbull. Biometrika, 1985

zL(i)lower sequential boundary zU(i)upper
sequential boundary
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Repeated Confidence Intervals
d



1.0
78
Repeated Confidence Intervals
d
o
o
o
1.0
information fraction
79
Symmetric or Asymmetric GSBs

• For two existing or alternative treatments
• Symmetric GSBs
• Level of evidence same in either direction
• For a new treatment vs. standard of care
• Asymmetric GSBs
• Level of evidence less for negative (harmful)
  trends than for positive (beneficial) trends
• If new treatment not proven but already in
  practice, may require more symmetric GSBs than
  asymmetric GSBs
• Need convincing evidence to discourage or stop a
  practice
• Reference
• DeMets, Pocock Julian, Lancet, 1999

80
Futility

• Generally defined as a trial not being able to
  meet its primary objective
• Benefit
• Effect in either direction
• Non-inferiority
• May also involve other secondary objectives

81
Types of Decision Errors

• Type I making a false claim of an intervention
  effect
• Beneficial
• Harmful
• Type II making a false claim of no intervention
  effect

82
Negative Trends

• Trends are variable, especially early
• Different trend patterns
• Settle down on no difference
• Reverse to become beneficial (positive)
• Drift downwards to demonstrate harm
• Plunge towards harm

83
Negative Trend Examples

• Inotropic drugs for chronic heart failure
• Milrinone/PROMISE
• Vesnarinone/VEST
• Anti-arrhythmic drugs for cardiac arrhythmia
  patients/CAST
• Hormone replacement therapy (HRT) / Womens Health
  Initiative (WHI) HERS
• Coumadin vs Aspirin/ CARS
• Atenolol vs Placebo in MI / ISIS-I

84
Methods for Assessing Negative Trends

• Harm
• Symmetric Boundaries such as group sequential
  boundaries
• Asymmetric Group Sequential Boundaries
• Futility
• Beta Spending Functions
• SPRT/Triangular Boundaries
• Conditional Power
• Predictive Power (Bayesian)

85
Group Sequential Boundaries 1? 0.25
86
Prospective Randomized Milrinone Survival
Evaluation (PROMISE)New England Journal of
Medicine, 1991

• Test Milrinone to reduce mortality in class III
  and
• IV congestive heart failure patients
• Milrinone known to increase cardiac contractility
• Randomized, double-blind
• 119 clinical centers
• Outcome
• Primary All cause mortality
• Secondary Cardiovascular mortality
  Hospitalizations

87
PROMISE

• 1088 patients randomized (561 M, 527 P)
• Median follow-up 6 months (0, 20 months)
• 90 compliance
• None lost to follow-up
• DSMB stopped PROMISE 5 months early
• 168 deaths on Milrinone (30)
• 127 deaths on placebo (24)
• 95 deaths cardiovascular

88
Survival Curves for the PROMISE Trial
89
Group Sequential Boundaries for the PROMISE Trial
90
PROMISE MORTALITY (milrinone vs. placebo)
Group Sequential Bounds (1a .025)
91
PROMISE DMC

• Agonizing negative trend
• Issue of symmetric vs. asymmetric GSB
• PROMISE crossed zone of futility
• Milrinone approved for IF use for high risk
  patients
• Milrinone improved cardiac function
• Needed to rule out neutrality
• from harm on mortality

92
Conditional Power

• One method to assess likelihood of achieving a
  statistically significant result at the trial
  conclusion.
• Compute probability of rejecting Ho at the trial
  conclusion, given current trend and assumed
  effect for remaining portion of the trial

93
Stochastic Curtailed Sampling

• Also called Conditional Power
• Likelihood of a Trend Reversal
• Lan, Simon Halperin.
• Communications in Statistics-C 1207-219, 1982
• Lan and Wittes, Biometrics, 1988.
• Bound Type I Type II Error

94

• In General
• Let Z(T) test statistic at end of trial
• Z(t) current value at time t
• R rejection region
• R acceptance region
• P Z(T) ? R H0 ?
• P Z(T) ? R HA ?
• or P Z(T) ? R HA 1 - ?

95

• Lan, Simon, Halperin (1982) compute
• Decision Compute When
• reject P Z(T) ? R H0, Z(t) ?0 positive
  trend
• accept P Z(T) ? R HA, Z(t) ?A negative
  trend
• Then
• P Type I error ? ?/?0 ??
• P Type II error ? ?/?A ??

96

• Example (? 0.05)
• ?0 ??
• 0.70 0.071
• 0.80 0.063
• 0.85 0.059
• Note
• If ?0 1 or ?A 1
• ? Curtailment

97
B-ValueA Method for Computing Conditional
PowerLan Wittes (1988) Biometrics

• Let t n/N (or d/D)
• Z(t) current standardized statistic
• Now Z(1) B(1) and
• ( observed remaining)

98
Visual Aid
H0 ? 0 HA e.g. ?
B(1)
B(t)
99
Conditional Power

• PZ(1) ? Z? Z(t), ?)

100
Conditional Power
1. Survival D total events 2. Binomial N
total sample size
101
Conditional Power (2)
3. Means N total sample size
102
Conditional Power Table

• Typically, compute conditional power for a series
  of assumed intervention effects (for observed
  negative trends)
• Observed effect to date
• Assumed effect
• Null effect
• Effects in between
• For a specified conditional power (e.g. 10, 20
  30), boundaries can be generated

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Conditional Power Boundaries
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Example BHAT

• Expected Deaths D 398
• Observed Deaths 183 Placebo d 318
• 135 Propranolol D d 80 398
• Observed logrank Zd 2.82 t 318/398 .80
• Compute Conditional Power under H0
• 1 - ? - 1.25
• 0.89

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BHAT Stochastic Curtailed Sampling
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Conditional Power

• One method to assess likelihood of achieving a
  statistically significant result at the trial
  conclusion.
• Compute probability of rejecting Ho at the trial
  conclusion, given current trend and assumed
  effect for remaining portion of the trial

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Response Adaptive Designs

• Adjust/increase sample size if treatment effect
  assumed was too large
• Traditionally, this approach discouraged
• Recent methodology suggests possible approaches

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Proschan Hunsberger Method

• Simple method may make Type I error substantially
  less than 0.05
• Developed method to obtain exact Type I error as
  a function of Z1 and n2, using a conditional
  power type calculation

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Proschan Hunsberger
Conditional Power and p value required in stage 2
as a function of R n2/n1 for the NHLBI Type II
study example
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Proschan Hunsberger

• Allows for sample size adjustment based on
  observed treatment effect
• Requires increasing final critical value

111
Conditional Power Method

• Lan, Simon Halperin (1982, Com in Stat)
• Lan Wittes (1988, Biometrics)
• Lan Zucker (1995, Stat in Med)

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Conditional Power Method

• Cui, Hung Wang (1997, ASA Biopharm Proceedings)
• CP(?) gt 1.2 CP(?) Decrease N
• CP(?) lt 0.8 CP(?) Increase N
• Properties not well characterized

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Conditional Power Method

• Chen, DeMets Lan (2004, Stat Med)
• Can extend to a second stage if CP gt 50 for
  current trend
• Lan Trost (Proceeding of Biopharm, ASA, 1997)

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Lan Trost

• If CP (n) lt cl , then terminate for futility
  accept null (REQUIRED)
• If CP(n) gt cu , then continue with no
  change
• If cl lt CP(n) lt cu , then increase sample size
  from N0 to N to get conditional power to desired
  level
• Type I error is controlled at nominal level

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Chen, DeMets Lan

• Increase N0 if interim result promising
• Conditional power gt 50 for current trend
• Increase in N0 not greater than 1.75 times
• Under these conditions, Type I error is not
  increased and no practical loss in power

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Why does it work?
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Chen, DeMets Lan
118
Adaptive Design Remarks

• A need exists for adaptive designs (even FDA
  statisticians agree)
• Technical advances have been made through several
  new methods
• Adaptive designs are still not widely accepted
  subject to (strong) criticism
• May be useful for non pivotal trials
• Practice precedes theory, perhaps in time

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Some Data Monitoring Examples
120
Vesnarinone in Heart Failure

• Two Trials
• First Trial
• (New Engl J of Med 329149-155, 1993)
• Second Trial
• (New Engl J of Med 339 1810-16, 1998)

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Vesnarinone in Heart Failure Trial I

• First Trial (New Engl J of Med 329149-155, 1993)
• Patients with Class II-III heart failure
• A vasodilator and inotrope
• Randomized double blind
• Vesnarinone (120 mg, 60 mg) vs. placebo
• Mortality outcome

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VESNARINONE- Trial I

• A 60 mg dose had no effect on exercise tolerance
  or symptoms
• A 120 mg dose increases exercise tolerance and
  reduces symptoms
• 120 mg arm stopped early with increased mortality
  (6 vs. 16, plt.01
• 60 mg arm continued observed a 60 reduction in
  mortality

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VESNARINONETrial I

• Plbo 60mg P
• Mortality 33/238 13/239 .002
• Mortality and
• Morbidity 50/238 26/239 .003

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Vesnarinone in Heart Failure Trial II - VEST

• Second Trial (New Engl J of Med 3391810-16,
  1998)
• Two doses (30 60 mg) vs. placebo
• NYHA Class III/IV patients, LVEF LT 30
• Randomize double blind
• Mortality outcome

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VEST(NEJM, 1998)

• 3833 patients randomized
• Primary Outcome observed an increase in
  mortality on vesnarinone HR 1.2, unadjusted
  p 0.02
• Secondary Outcome
• Worsening Mortality plus CHF hospitalization
• Improved Quality of Life

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VESTSurvival in the Three Groups
127
Acumulating Results for VEST

• Information Fraction Logrank Z value
• (high dose)
• .43 0.99
• .19 - 0.25
• .34 - 0.23
• .50 - 2.04
• .60 - 2.32
• .67 - 2.50
• .84 - 2.22
• .90 - 2.43
• .95 - 2.71
• 1.0 - 2.41

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VEST MORTALITY (high dose vs. placebo)
129
Conditional Power for VEST

• RR Information Fraction
• .50 .67 .84
• .50 .46 .01 lt.01
• .70 .03 lt.01 lt.01
• 1.0 lt.01 lt.01 lt.01
• 1.3 lt.01 lt.01 lt.01
• 1.5 lt.01 lt.01 lt.01

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MERIT-HF Study Design

• Chronic heart failure patients
• Randomized placebo controlled
• Metoprolol (a beta-blocker) vs. placebo
• Two-week placebo run in (compliaance)
• Entered 3991 patients
• Terminated early
• Mean followup approximately one year
• The International Steering Committee on Behalf of
  the MERIT-HF Study Group,
• Am J Cardiol 1997 80(9B)54J-58J. The MERIT-HF
  Study Group, ACC, March 1999.

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MERIT-HF Entry Characteristics
Meto CR/XL Placebo Mean age (years) 64 64 Male
sex () 77 78 NYHA class II () 41 41
III () 56 55 IV () 3.5 3.8 Ejection
fraction 0.28 0.28
Data unblinded by ISaC The MERIT-HF Study Group,
ACC, March 1999
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Total Mortality
133
MERIT-HF Monitoring Bounds for Total Mortality
X3.807 corresponds to a marginal p-value of
approx. p0.00015
DeMets, Julian, Chatterjee Guidelines for
Interim Analyses in MERIT-HF
134
Relative Risk and 95 Confidence Interval
135
Relative Risk
136
Diabetic Retinopathy Study (DRS)

• Randomized placebo controlled
• Randomized eyes
• photocoagultion vs. no therapy
• Outcome visual loss
• Patients (1700) with proliferative retinopathy
• Planned 5 yr. follow-up
• Protocol change at 2 yrs.
• Unanticipated dramatic early benefit (RR .4, Z
  5.5)
• No long term track record for photocoagulation
• Decision
• Treat all untreated eyes at high risk
• Publish interim results
• Follow all patients for long term "adverse" and
  beneficial effects

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Diabetic Retinopathy Study (DRS)Ref DRS Group
(1980) Ophthalmology
138
ISIS-I Trial

• Ref ISIS-I. Lancet, 1986
• A RCT evaluating the early use of atenolol vs.
  placebo in post MI patients

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Data Monitoring Summary

• DMCs useful to assess risk/benefit
• Treatment effect trends emerge with variability
  over time
• Statistical methods available to minimize false
  positive claims
• No single method adequate

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Downtown Madison Monona Terrace
142
More Details on Conditional Power
143
B-ValueA Method for Computing Conditional
PowerLan Wittes (1988) Biometrics

• Let t n/N (or d/D) n tN
• Now ZN B(1)
• and
• ( obs remaining)

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Conditional Power

• B(1) B(t) B(1) - B(t)
• Properties of B(t) B(1) - B(t)
• (i) B(t) B(1) - B(t) Normal Independent
• (ii) E B(t) ?t
• E B(1) - B(t) ? - ?t ? (1-t)
• (iii) V B(t) t
• V B(1) - B(t) 1 - t

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Conditional Power

• Conditional Distribution
• EB(1) B(t) B(t) ? (1-t)
• V B(1) - B(t) 1 - t
• PZN B(1) ? Z?B(t), ?)