Shing Lee MedicReS World Congress 2015

The Role and Responsibilities of Statisticians in Clinical Trials

Shing Lee, PhDDepartment of Biostatistics

Mailman School of Public HealthColumbia University

October 19-25 | 2015 New York www.medicres.org

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…

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

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

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

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Clinical Trials

• A prospective study

• Under controlled conditions (an experiment)

• With an Intervention

• In Humans

• Comparison group (Not always)

Drug Development

7Pharmaceutical Research Manufacturers of America. 2013 Biopharmaceutical Research Industry Profile

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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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 population4. Detailed protocol5. Outstanding follow-up 6. Appropriate monitoring plan7. Clear interpretation and reporting of

results

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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

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

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

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

resultsOctober 19-25 | 2015 New York

www.medicres.org

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)14

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


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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?

Outcome Selection

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• 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 MTDwith Binary Endpoint

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3 + 3 DesignEnter 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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• 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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CRM

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– 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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• Model-based– Requires the specification of dose

toxicity model– It is important to select appropriate

model parameters – Requires statistician with knowledge

“Cons” CRM

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

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


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

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


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


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

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?


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?

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


Shing Lee MedicReS World Congress 2015

Data & Analytics

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