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Model Based Adaptive Designs in Dose Finding, Including Case Studies Alun Bedding, Director, Quantitative Sciences Outline of the Seminar Introduction Why statistical modelling? Modelling and simulation in designing a trial Problems
Model Based Adaptive Designs in Dose Finding, Including Case Studies
Alun Bedding, Director, Quantitative Sciences
Outline of the Seminar
Introduction – Why statistical modelling? – Modelling and simulation in designing a trial Problems behind dose finding and adaptive designs Some examples
Why statistical modelling?
Let’s take an simple example Finding the right dose Might use a study with three doses and placebo in a parallel group design Analysis could be a hypothesis test comparing each dose and placebo at the 5% significance level – Is the dose different from placebo? Where are the problems?
Dose Response Curve
Dose Response 95%
Other issues
Multiple testing - the more you test the more you increase your chance
– 3 doses versus placebo – 3 tests – Need an adjustment for multiple comparisons many of which are conservative Using hypothesis tests does not give you a good idea of the dose response curve – it just tells you the dose is different to placebo May not give you the dose of interest
Answer is modelling
Deriving a model for the shape of the dose response does not involve hypothesis testing No need to correct for multiple testing Knowledge of the shape and location of the dose response curve will allow the determination of doses of interest ICH E4 - “Assessment of dose-response should be an integral component of drug development with studies designed to assess dose-response an inherent part of establishing the safety and effectiveness of the drug.”
“What about in other situations?”
Modelling together with simulation allows you to explore the design and better understand the drug’s properties For example in FTIH it might allow the skipping of doses to more efficiently reach the maximum tolerated dose – example later Give greater understanding of the inter-relationships between dose, exposure and response In a well designed and analysed dose response study allows you to find the minimally effective dose, dose which gives maximal response as well as ease of having to change dose post approval Look at effect of a study on drug supplies to prevent overage
Why Simulation?
We want to be sure we have picked the best design under degrees of uncertainty for various aspects
– Number of doses, number of subjects on each dose, analysis methods, dose selection criteria, decision criteria........
Reproduce the design under different scenarios
– Treatment differences, recruitment rates, dropouts, stopping and decision rules
Demonstrate the robustness of the design to customers
– Internal governance, regulators, ethics committees
Operating Characteristics of the design If there is no effect (in reality), how often do we find an effect?
Type I error Simulate data assuming no effect
If there is an effect, how often do we pick it up?
Power (1 – Type II error) Simulate assuming various effects
How often do we make the correct decision?
Dose Response
20% of post approval changes are to dose Best guess at doses in a pre-determined randomisation
Do not know dose response until the end of the study Significant number of subjects randomised to ineffective or potentially toxic doses Inefficient - may miss dose response curve
Dose Response Curve
Dose Response 95%
Dose Response Curves
Response Dose
Increase Number of Doses (~15)
Response Dose
Increase Number of Doses (~15)
Response Dose
Wasted Doses Wasted Doses
Increase Number of Doses – and adapt
Response Dose
Wasted Doses Wasted Doses
Definition
Adaptive Design – any design which uses accumulating data to decide how to modify aspects of the study without undermining the validity and integrity of the trial. An adaptive design should be adaptive by "design" not an ad hoc change of the trial conduct and analysis. Adaptation is a design feature, not a remedy for poor planning. To maintain study validity means providing correct statistical inference (such as adjusted p-values, unbiased estimates and adjusted confidence intervals), assuring consistency between different stages of the study, minimizing operational bias. To maintain study integrity means providing convincing results to a broader scientific community, preplanning, as much as possible, based on intended adaptations, and maintenance of blinding.
General Structure of Adaptive Designs
An adaptive design requires the trial to be conducted in several stages with unblinded access to the accumulated data. An adaptive design may have one or more rules:
– Allocation Rule: how subjects will be allocated to available arms. – Sampling Rule: how many subjects will be sampled at the next stage. – Stopping Rule: when to stop the trial (for efficacy, for harm, for futility). – Decision Rule: interim decisions (to change endpoint, to modify initial plan, optimal dose to be randomized to).
Tibaldi F.S, Beck B.H.L., Bedding A.W.- Implementation of a Phase 1 Adaptive Clinical Trial in a Treatment of Type 2 Diabetes, 2008, Drug Information Journal, Vol. 42, pp. 455–465
The Study Design
Phase I multiple dose study for safety and tolerability of a compound for diabetes Involves six active doses and placebo. Treatment of type 2 diabetes mellitus (T2DM) and includes a total of 36 subjects. Data from a single dose study of the same compound is available
placebo)
previous doses
Model for Safety Logistic model
– The dose-response model for non-tolerability – Let pij be the probability that a patient i who receives dose j experiences non-tolerability – j = 1,…..,7 where dose1 is used for placebo – i = 1,…..,36
( ) ( )
1
logit log
ij j
p dose = α + α
( )
( )
( )
( )
1 1
exp log 1 exp log
j ij j
dose p dose α + α = + α + α
Results
Probability of NT
Study purpose
A Phase IIb study in the treatment of moderate or severe asthmatics – Determine the minimum efficacious dose – Gain an understanding of the dose response Can we use a flexible design in this situation – Increase the chances of a successful trial – Reduce the chances of a failed study – Increase the probability of stopping a poor drug early
Study design
Single dose Parallel group Primary endpoint of FEV1 – Objective to find a dose which gives an increase in FEV1 of 150 mL (SD – 430 mL) Maximum number of subjects to be recruited – 600 Time of endpoint – 7 days Recruitment rate – 5 days per week Study already conducted in COPD suggests that doses – 15.5, 31.25, 62.5, 125 mg should be sufficient to characterise the dose response curve Placebo and active comparator included Assume Emax model
Adaptive design process (one example)
Placebo n=50 Interim Analysis (n=300) Drop arms
Stop for futility
Stop for success
AC n=50 15.5 mg n=50 31.25 mg n=50 62.5 mg n=50 125 mg n=50 Placebo n=50 AC n=50 62.5 mg n=50 125 mg n=50 Final Analysis Futility
Success
Simulation Results
Design Total N Power for Superiority of Dose vs Placebo Pairwise Comparisons Emax model Linear model 6 Trt Parallel Group 1038 (173/arm) 0.78 0.90 0.94 6 Trt, 3 Period crossover 360 (3/sequence) 0.83 0.96 0.85 6 Trt, 4 Period crossover 360 (1/sequence) 0.94 0.98 0.96
Non-adaptive design Adaptive design
On-Going Work
Cross-over study is possible in this population Investigate an adaptive cross-over study How to adapt – patients already in the study when an arm is dropped Do you adapt by sequence rather than treatment ?????
Other Therapeutic Areas for Adaptive Designs
Neuroscience – Migraine, MS, Alzheimer's Cardiovascular Ophthalmology Oncology
Final Remarks
Adaptivemethods can help better characterise the dose response for a compound Simulations need to be carried out to investigate the study operational characteristics Model based designs are ideal but adaptive are not a panacea in all situations