1. The Role and Responsibilities of Statisticians
in Clinical Trials
Shing Lee, PhD
Department of Biostatistics
Mailman School of Public Health
Columbia University
October 19-25 | 2015 New York
www.medicres.org

2. Common introductory remarks we get…
• We need a samples size and it should take you
a couple minutes…
• We were asked by our Institutional Review
Board to consult with a statistician…
• We submitted this manuscript and got the
reviewer’s comments back and it said that we
should consult with a statistician…

3. Conclusions from these remarks
• Investigators include statisticians because
they are being asked to
• Investigators do not know the value that a
statistician can add to their research project
• Investigators have not worked with
statisticians before
• Investigators do not understand the role
and responsibilities of a statistician in a
research project

4. What can a statistician help with?
• Depends on many factors:
– The stage of the research
– How much the statistician understands the
research project
– How much investigators are willing to discuss
the research project

5. Role of the statistician
• Consultant
– Only consult when trouble arises
– Only consult when absolutely needed
• Collaborative team member
– Involved in every aspect of the design and
implementation of study
– Good understanding of research project
– Active participant from beginning to end of
research project

6. Clinical Trials
• A prospective study
• Under controlled conditions (an experiment)
• With an Intervention
• In Humans
• Comparison group (Not always)
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7. Drug Development
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Pharmaceutical Research Manufacturers of America. 2013 Biopharmaceutical Research Industry Profile

8. Drug development
• Clinical phase:
– Early Development (Phase I): safety
– Middle Development (Phase II): safety and
preliminary evidence of efficacy
– Late Development (Phase III): evidence of
effectiveness compared to standard of care
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9. Key Components in Clinical Trials
1. Clear question to address and clear
hypothesis
2. Appropriate design to address the hypothesis
3. Clear definition of target population
4. Detailed protocol
5. Outstanding follow-up
6. Appropriate monitoring plan
7. Clear interpretation and reporting of results
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10. Research Team in Clinical Trials
• Clinical Researchers and Coordinators
• Biostatisticians
• Data management team
• Centralized resources – Pharmacy and Labs
• Funding Sources
• Institutional Review Boards
• Regulatory agencies
• External Monitors
• Patients
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11. Responsibilities of the statisticians
• Choice of design:
– Outcome selection
– Adequate design to answer research question
– Analysis plans
– Data quality (minimize missing data and bias)
– Sample size considerations

12. Responsibilities of the statisticians (2)
• Implementation of design:
– Data collection forms and databases
– Data quality (minimize data entry errors, data
entry time)
– Data monitoring (data queries, data sources)
– Monitor to ensure that design is implemented
correctly

13. Responsibilities of the statisticians
• Analysis and Interpretation of results:
– Appropriate analysis for the data
– Analysis matches the design of the study
– Analysis answers the research question of
interest
• Reporting and Dissemination of results
October 19-25 | 2015 New York
www.medicres.org

14. Early Development / Phase I
• Small studies N ~ 30; range (20-40)
• First in human
• Single arm (all patients receive intervention)
• Goals:
– To characterize the toxicities associated with a new
agent
– To determine the dose limiting toxicities (DLT)
– To determine the maximum tolerated dose (MTD)
– To assess the overall tolerability and suggest a
recommended phase II dose (RP2D)
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15. Early Development
• What can your statistician help with?
– Design selection
– Feasibility of study (need for trial, accrual,
resources)
– Implementation of design (dose assignments)
– Analysis and interpretation of results
– Reporting and dissemination of results
October 19-25 | 2015 New York
www.medicres.org

16. Primary outcome are adverse events:
– What are the adverse events of interest?
– How long after do they take to manifest?
– How often should they be collected?
– Are all of them equally severe?
– What is your tolerance for these adverse events?
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Outcome Selection

17. • Algorithms:
– 3+3 design
• Model-based
– Continual Reassessment Method (CRM)
(O'Quigley, Pepe and Fisher, 1990)
– Time to event CRM
(Cheung and Chappell, 2000)
Methods for estimating MTD
with Binary Endpoint
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18. 3 + 3 Design
Enter 3 patients
0 / 3 1 / 3 ≥ 2 / 3
Add 3 patients
1 / 6 ≥ 2 / 6
Escalate Prior Dose is MTD
 MTD = Maximum dose with fewer than 2/6
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19. • Pros:
– Easy to implement
– Does not require statistician or fancy software
• Cons:
– Cannot specify a target probability of toxicity
– Lack a quantitative interpretation of MTD
– “Unexpected” contingencies; e.g., 1/3 + 1/4?
– Sample size is not specified
Pros and Cons of 3+3
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20. CRM
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21. – MTD = A dose associated with percent toxicity
• p ≈ 20%—25% for cancer trials
• The principle can be extended to non-cancer trials
– Model-based adaptive design
• Continually update dose-toxicity model during a
trial
• Treat the next patient (or group of pts) at MTD
estimate
• May start at a middle dose instead of lowest
– Better performance
• Selects the MTD more often in simulation studies
– Specify sample size in the design
Pros CRM
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22. • Model-based
– Requires the specification of dose toxicity
model
– It is important to select appropriate model
parameters
– Requires statistician with knowledge
“Cons” CRM
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23. Goals in Middle Development
• Further evaluate safety and toxicity of the
treatment
• Assess for indication of efficacy to suggest
further development
• Assess the feasibility and outcomes with longer
duration of treatment
• Re-evaluate dose and schedule of the treatment

24. Middle Development
• What can your statistician help with?
– Defining hypothesis
– Design selection
• Methods for treatment assignment (if needed)
– Feasibility of study
– Implementation of design
– Interim Analysis (if needed)
– Analysis and interpretation of results
– Reporting and dissemination of results
October 19-25 | 2015 New York
www.medicres.org

25. Designs for Middle Development
• Screening designs:
– One arm with treatment only
– Using historical data for the control group
– Examples : simple, staged or adaptive designs
• Randomized selection:
– Two or more arms all with new treatments.
– Two or more arms with a control group.
• Other designs:
– Enrichment designs

26. Goals in Late Development
• Assess the effectiveness of a new treatment
compared to standard of care
• Further evaluate the safety of a new
treatment compared to standard of care
• It has to be convincing and definitive
October 19-25 | 2015 New York
www.medicres.org

27. Late Development
• What can your statistician help with?
– Defining hypothesis
– Design selection
• Methods for treatment assignment
• Blinding/Masking
• Interim analysis plan
– Feasibility of study
– Implementation of design
– Interim Analysis (if needed)
– Analysis and Interpretation of results
– Reporting and dissemination of results
October 19-25 | 2015 New York
www.medicres.org

28. Methods for Treatment Assignment
• How should treatments be assigned to avoid
bias?
• Should patients be assigned randomly?
• What type of randomization should be used?
– Simple, Stratified, Blocked, Adaptive
• How to implement the randomization?
– Web-based, independent personnel,
envelopes

29. Masking of Treatment
• Should treatment assignment be masked to
avoid bias?
• Is it feasible to blind or mask?
• Who should be masked?
– Participant, Investigator, Assessor
• What are the procedures for unmasking?
October 19-25 | 2015 New York
www.medicres.org

30. Interim Analysis
• Is interim analysis needed?
– To monitor for early benefits before the end
of the trial
– To ensure the safety of participants
– To ensure that participants are not exposed to
inferior treatments
• What data monitoring methods will be
used?
• What other monitoring will be done?

31. Summary
• Involve your statistician early in the
research study
• Statisticians are not only there to calculate
sample sizes and perform data analysis
• Statisticians can offer a different perspective
which can improve your design and ensure
that your research question is addressed
October 19-25 | 2015 New York
www.medicres.org