Practical and ethical advantages of Bayesian approaches in

adaptive clinical trial designs

Kristian Thorlund

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Background

This talk was previously given as an invited talk at a DSEN sponsored meeting on innovative clinical trials designs, Jan 31-2014, Ottawa

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Background

Health care is moving towards an era with a growing number of patient sub-populations that are rare and difficult to study in clinical trials

Within Canada, there is a very high demand for clinical trial methodologists that can think beyond the conventional parallel design RCT

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Objectives

Generate a healthy debate on how to move forward and be innovative, yet conscientious, about trial designs in Canada

Illustrate the ‘type of thinking’ that may be required for trial designs in challenging populations

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Outline

• Teaser

• Bayesian stereotypes and efficiency reflections

• Introduction to Bayesian analysis

• Introduction to Adaptive designs

• Illustrative example: hepatitis C

ECMO trials

• Phase I - CMT: 4/10 deaths; ECMO: 0/9 deaths • Phase II – ECMO 1/10 death • Within the scientific literature this was criticized

on both sides – No patients should have been randomized to CMT – Not enough patients were randomized to CMT

• Was O’Rourke’s approach a breaking point? – 4 babies deaths was all the staff could take

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Traditional hierarchy of evidence

RCTs

&

Systematic reviews

Cohort Studies

Case-series

Clinical expert opinions

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In small populations

Observational studies

RCTs and SRs expert opinion

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In small populations

Highly

Efficient

Study Design

Not so efficient study design

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Bayesian Statistics

Are Bayesian statistics the answer?

… when and how?

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Concepts of Bayesian Statistics (gross oversimplification)

Bayesian analysis is unique for allowing data to be mixed with ‘prior’ (external) evidence or opinions.

By contrast, the conventional Frequentist discipline of statistics is only data driven

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Bayesian Stereotypes

Not fully data driven Too complex

Hard to trust due to ‘subjectivity’ Buzz word.. Used by

the cool statisticians

Goes against conventional EBM

Incorporates evidence of ‘lower quality’

Too philosophical

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Bayesian Stereotypes

Please forget these stereotypes!

Bayesian analysis is flexible it can be what you want, need and/or require of it to be

• Easy or complex

• Rigorous or exploratory

• Objective or subjective

• Broad or narrow

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In small populations

Highly

Efficient

Study Design

Not so efficient study design

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In small populations

Bayesian

Adaptive

Designs

Conventional parallel design RCTs

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Concepts of Bayesian Statistics (gross oversimplification)

Bayesian analysis is unique for allowing data to be mixed with ‘prior’ (external) evidence or opinions.

By contrast, the conventional Frequentist discipline of statistics is only data driven

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Bayesian Statistical Inferences

Priors take the form of probability distributions, and are mixed with the likelihood to shape a posterior probability distribution from which inferences are drawn

Frequentist drawn inferences solely from the likelihood (but necessitates parametric distributional assumptions)

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Bayesian priors

Available data from RCTs (e.g. response to Tx)

Drug A vs Drug B Difference in response

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Bayesian priors

+ external data: clinical expert survey/obs. study

Drug A vs Drug B Difference in response

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Bayesian posterior distributions

Adding the two together forms a ‘posterior’ distribution

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Bayesian philosophy (again gross oversimplification)

Bayesians try to answer ‘given the data, what are the likely effects and degrees of uncertainty’

Frequentist try to answer ‘how likely are the estimated treatment effects to be observed in a study attempting to replicate the current data?’

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Bayesian thinking and adaptive designs

Considering the underlying philosophies, Bayesian statistics and adaptive clinical trials seem to go hand in hand.

Frequentist methods in adaptive clinical trials are conceptually counter-intuitive.

Note: many researchers apply frequentist methods, but Bayesian thinking (without knowing it)

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Adaptive designs

Adaptive designs typically allow for flexible change in randomization ratio or elimination of treatment groups after interim analyses

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Adaptive designs

For example, a trial examining placebo, low dose, mid dose, and high dose, may start with 1:1:1:1 randomisation, but gradually randomise more and more to higher doses based on better observed responses in the mid and high dose arms

This is Bayesian thinking relying only on data, or in other words, non-informed Bayesian statistics (i.e., employed priors are non-informative)

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Adaptive designs

Consider the placebo, low, mid, high dose example. After data is available for 200 patients, you make a decision on whether to change the 1:1:1:1 randomisation ratio

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Adaptive designs

Assume you see the following responses. What would your new randomization ratio be?

Placebo Low Dose

Mid Dose

High Dose

5/52

11/49

23/50 20/49

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Adaptive designs

Placebo

Low dose

Mid dose

High Dose 50 patients/arm 100 patients/arm

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Adaptive designs

Placebo

Low dose

Mid dose

High Dose 50 patients/arm 100 patients/arm

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Adaptive designs

Placebo

Low dose

Mid dose

High Dose 50 patients/arm 100 patients/arm

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Bayesian thinking in rare diseases

No major design challenges in larger populations

However, most diseases in pediatrics and rare populations are both rare and trialist face several difficulties enrolling a large number of patients

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Adaptive designs in pediatrics

Typically only a few centers (e.g., SickKids hospitals) can enroll patients and this limits the number of feasible candidate patients compared with adult trials

Adapting to small numbers is prone to errors by the play of chance. Thus, without innovative twists these designs typically have little value for informing clinical practice

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Adaptive designs in pediatrics

Consider again a trial that starts with 1:1:1:1 randomisation to placebo, low, mid, and high dose.

Say you can feasibly only enroll 50 patients over the trial period of 3 years. That is, no more than 50 patients will be available for any clinical study over the next 3 years.

How realible is randomisation adjustments then half-way through? How useful it is? (pros and cons?)

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Adaptive designs

Which adjustments would be comfortable with the following numbers?

Placebo Low Dose

Mid Dose

High Dose

1/7

2/6

3/5 3/6

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Adaptive designs

You only have 26 patients left and need to make the most our of the evidence.

Placebo Low Dose

Mid Dose

High Dose

1/7

2/6

3/5 3/6

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Adaptive designs

What is external evidence on the same trial population was available on placebo?

Placebo Low Dose

Mid Dose

High Dose

1/7

2/6

3/5 3/6

External Evidence

on Placebo

4/35 3/19

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Adaptive designs

How many more patients do we need to randomise to placebo with this evidence?

Placebo Low Dose

Mid Dose

High Dose

1/7

2/6

3/5 3/6

External Evidence

on Placebo

4/35 3/19

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Conventional vs Bayesian Adaptive

Consider two scenarios

1. We ignore the external evidence and keep randomising 1:1:1:1

2. We include the external placebo evidence, stop randomizing to placebo and randomise 1:1:1 with the three doses

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Scenario #1

Maintain 1:1:1:1 randomisation, no statisticallly significant difference detected

Placebo Low Dose

Mid Dose

High Dose

2/13

4/12

6/11 7/14

P=0.11

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Scenario #2

Stop randomising to placebo

Placebo Low Dose

Mid Dose

High Dose

6/18

9/17 9/18

External Evidence

on Placebo

4/35 3/19 1/7

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Scenario #1 versus Scenario #2

0% 10% 20% 30% 40% 50% 60% 70% 80%

Placebo Response

Mid Dose Response P=0.11

#1

0% 10% 20% 30% 40% 50% 60% 70% 80%

P>0.05

#2

Placebo Response Evidence

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Scenario #1 versus Scenario #2

0% 10% 20% 30% 40% 50% 60% 70% 80%

Placebo Response

Mid Dose Response P=0.11

#1

0% 10% 20% 30% 40% 50% 60% 70% 80%

#2

Posterior Placebo Response

‘P<0.05’

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Motivating example - HCV

An increasing number of highly potent agents are available for treating hepatitis C in adults

Conventional therapy, peginterferon+ribavirin is known to eradicate the virus children at the same rate as in adults and have similar or better safety profile than in adults

(Druyts et al CID 2012, systematic review of 8 trials)

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Motivating example - HCV

Some of the most potent newer direct acting agents (DAAs) eradicate the virus in 90% of adults without co-administration of interferon (and are thus more safe)

How are these agents likely to work in children?

Can they reduce adverse events (e.g., anemia)?

Can they avoid reduction in growth?

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Expected efficacy

Peg-Riba DAA+ Peg-Riba

DAA

90% 80-90%

50%

Motivating example - HCV

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Choosing the randomisation scheme

Peg-Riba DAA+ Peg-Riba

DAA

n? n?

n?

Motivating example - HCV

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Choosing the randomisation scheme

Peg-Riba DAA+ Peg-Riba

DAA

n? n?

n?

Motivating example - HCV

Peg-Riba External Evidence

50%

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Expected safety (anemia, neutropenia, rash, …)

Peg-Riba DAA+ Peg-Riba

DAA

25%

10%

20%

Motivating example - HCV

Peg-Riba External Evidence

20%

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Expected safety (anemia, neutropenia, rash, …)

Peg-Riba DAA+ Peg-Riba

DAA

n?

n?

n?

Motivating example - HCV

Peg-Riba External Evidence

20%

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Motivating example - HCV

Bayesian adaptive design:

• Borrows strength from systematic review to stop placebo randomization

• Could also borrow strength from adult population (confirm signal)

• Stop early for benefit/safety based on Bayesian ‘significance’

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This is where we started

Not fully data driven Too complex

Hard to trust due to ‘subjectivity’ Buzz word.. Used by

the cool statisticians

Goes against conventional EBM

Incorporates evidence of ‘lower quality’

Too philosophical

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Hopefully this is where we are going

Incorporates all relevant evidence Sufficiently flexible

Allows for transparent analysis Only used by the

cool statisticians (same as before)

Takes the step into the era beyond EBM

Helps us think about efficiency

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THANK YOU!