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Changing Trial Designs on the Fly. Janet Wittes Statistics Collaborative ASA/FDA/Industry Workshop September 2003. Context. Trial that is hard to redo Serious aspect of serious disease Orphan. Statistical rules limiting changes. To preserve the Type I error rate

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changing trial designs on the fly
Changing Trial Designs on the Fly

Janet WittesStatistics Collaborative

ASA/FDA/Industry Workshop

September 2003

context
Context
  • Trial that is hard to redo
    • Serious aspect of serious disease
    • Orphan
statistical rules limiting changes
Statistical rules limiting changes
  • To preserve the Type I error rate
  • To protect study from technical problems arising from operational meddling
challenge
Challenge

sense

rigor

challenge6
Challenge

senseless

rigor mortis

scale of rigor
Scale of rigor
  • Over rigid
  • Rigorous
  • Prespecified methods for change – preserves 
  • Unprespecified but reasonable change
  • Invalid analysis
    • responders analysis
    • outcome-outcome analysis
    • completers
consequences
Consequences
  • No change during the study

OR

  • Potential for the perception that change caused by effect
prespecified changes
Prespecified changes
  • Sequential analysis
  • Stochastic curtailing
  • Futility analysis
  • Internal pilot studies
  • Adaptive designs
  • Two-stage designs
problems
Problems
  • Technical Solved
  • Operational Risks accepted
  • Efficiency Understood
add a dmc
Add a DMC
  • What if it acts inconsistently with guidelines?
  • Something really unexpected happens?
    • DMC initiates change
    • Steering Committee initiates change
reasons for unanticipated changes
Reasons for unanticipated changes
  • Unexpected high-risk group
  • Changed standard of care
  • Statistical method defective
  • Too few endpoints
  • Assumptions of trial incorrect
  • Other
examples
Examples
  • Too much censoring; DMC extends trial
  • Boundary not crossed but DMC stops
  • Unexpected adverse event
  • Statistical method defective
  • Event rate too low; DMC changes design
1 endpoint driven trial
#1 Endpoint-driven trial
  • Trial designed to stop after 200 deaths
  • Observations different from expected
    • Recruitment
    • Mortality rate
  • At 200 deaths, fu of many people<2 mo
  • DMC: change fu to minimum 6 mo
  • P-value: 0.20 planned; 0.017 at end
2 boundary not crossed
#2. Boundary not crossed
  • Endpoint
    • Primary: 7 day MI
    • Secondary: one-year mortality
  • Very stringent boundary
what dmc sees
What DMC sees
  • Very strong result at 7 days
  • No problem at 1 year
  • Clear excess of serious adverse events
3 unexpected adverse event pert study of the whi
#3. Unexpected adverse event: PERT study of the WHI
  • Prespecified boundaries for

BenefitHarm

Heart attack Stroke

Fracture PE

Colon cancer Breast cancer

observations
Observations

BenefitHarm

----- Stroke

Fracture PE

Colon cancer Breast cancer

Heart attack

actions
Actions
  • Informed the women about increased risk of stroke, heart attack, and PE
  • Informed them again
  • Stopped the study
4 statistical method defective
#4. Statistical method defective
  • Neurological disease
  • 20 question instrument
  • Anticipated about 20% would not come
  • Planned multiple imputation- results:
    • Scale: 0 to 80
    • Value for ID 001: 30 38 ? 42 28 ?
    • MI values: -22, 176
5 too few endpoints
#5. Too few endpoints
  • Example: approved drug
  • Off-label use associated with AE
    • Literature: SOC event rate: 20 percent
  • Non-inferiority design -  = 5
  • Sample size: 800/group
observation
Observation
  • 400 people randomized
  • 0 events
  • What does the DMC do?
choices
Choices
  • Continue to recruit 1600
  • Stop and declare no excess
  • Choose some sample size
  • Tell the Steering Committee to choose a sample size
  • What if n=1? 2? 5? 10?
conclusions
Conclusions
  • Ensure that DMC understands role
  • Separate decision-making role of DMC and Steering Committee
  • Distinguish between reasonable changes on the fly and “cheating”
  • Expect fuzzy borders
technical
Technical
  • Changing plans can increase Type I error rate
    • We need to adjust for multiple looks
    • How do we adjust for changes?
operational
Operational
  • Unblind assessments
  • Subtle change in procedures
  • In clinical trials, the FDA and SEC