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Adaptive Design in Clinical Trials

Joseph Levy, PhDTeva Pharmaceutical Industries

MedicReS 2013 Istanbul

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Outline

What are adaptive designs? Review of possible adaptations Take home message

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The adaptive design principle

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Three definitions of adaptive designs

By adaptive design we refer to a clinical study design that uses accumulating data to decide how to modify aspects of the study as it continues, without undermining the validity and integrity of the trial.

PhRMA White Paper (2006)

A study design is called “adaptive” if statistical methodology allows the modification of a design element (e.g. sample-size, randomization ratio, number of treatment arms) at an interim analysis with full control of the type I error.

EMEA Reflection Paper (2007)

An adaptive design clinical study is defined as a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study.

FDA Draft Guidance for Industry (2010)

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Adaptation

An adaptation is defined as a change or modification made to a clinical trial before and during the conduct of the study.

Examples include Relax inclusion/exclusion criteria Change study endpoints Modify dose and treatment duration

etc.

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Prospective adaptations Adaptive randomization Interim analysis Stopping trial early due to safety, futility, or efficacy Sample size re-estimation

etc.

Concurrent adaptations Trial procedures - implemented by protocol amendments

Retrospective adaptations Statistical procedures - implemented by statistical analysis plan

prior to database lock and/or data unblinding

Types of adaptations

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Adaptive trial designs

Adaptive randomization Group sequential design Sample size re-estimation Drop-the-loser (or Pick-the-winner) Adaptive dose finding Biomarker-adaptive design Adaptive treatment-switching Adaptive-hypotheses design Seamless Phase I/II or Phase II/III Multiple adaptations

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Adaptive randomization

A design that allows modification of randomization schedules Unequal probabilities of treatment assignment Increase the probability of success

Types of adaptive randomization Treatment-adaptive Covariate-adaptive Response-adaptive

Comments Randomization schedule may not be available prior to the conduct of

the study. It may not be feasible for a large trial or a trial with a relatively long

treatment duration.

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Group sequential design

An adaptive design that allows for prematurely stopping a trial due to safety, efficacy/futility, or bothbased on interim analysis results

Well-understood design without additional adaptations

Overall type I error rate may not be preserved when there are additional adaptations (e.g., changes in hypotheses and/or

study endpoints) there is a shift in target patient population

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Jennison & Turnbull’s book

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CAPTURE trial background

Treatment of unstable angina requiring balloon angioplasty Endpoint: composite of death, myocardial infarction, urgent

repeat angioplasty/bypass surgery within 30 days of treatment

Treatments: equal randomization to 2-arms Control: standard therapy (heparin and aspirin) Experimental: control + abciximab

Expected event rates: Control: pC = .15 Experimental: pE = .10

fixed design sample size – 1372 (80% power)

Lancet, 349: 1429–1435, 1997

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CAPTURE design with O’Brien−Fleming bounds

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CAPTURE design with asymmetric bounds

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Covariate-adjusted adaptive randomization in a sarcoma trial

Patients with advanced/metastatic unresectable soft tissue sarcoma

Initial proposal: a single-arm trial of gemcitabine + docetaxel (G+D) with the aim to compare the results to historical data on G

Proposal of a randomized trial of G+D versus G created ethical concerns

Solution – Bayesian Adaptive Randomization

Thall PF, Wathen JK, Stat Med. 2005 Jul 15; 24(13):1947-64.

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Treatment plan

Up to four 6-week stages of chemotherapy Evaluation at each stage: Response, Stable, Failure Treatment continues if S, terminates otherwise Important baseline covariates Leiomyosarcoma - yes or no (LMS) Previous prior pelvic radiation (PPR) ⇒ Four subgroups: (LMS, PPR), (Not LMS, PPR), (LMS,

No PPR), (Not LMS, No PPR)

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Randomization procedure

Use a Bayesian generalized logistic regression model for the probabilities of overall treatment success

The model is based on data accumulated so far in the study Model covariates include: treatment, stage, baseline

covariates (LMS & PPR), and treatment-covariate interactions

Calculate for the new patient the probability of successful G+D treatment, π

Randomize the subject to G+D with probability π

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Sample size re-estimation

An adaptive design that allows for sample size adjustment or re-estimation based on the observed data at interim analysis blinded or unblinded?

Sample size adjustment or re-estimation is usually performed based on the various criteria such as: variability conditional power reproducibility probability

Can we always start with a small number and perform sample size re-estimation at interim?

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The Allegro trial

Phase III study to evaluate the effect of laquinimod in RRMS patients

Planned sample size – 1000 patients (1:1) Recruitment period: 18 months Treatment duration – 24 months Very important secondary endpoint: Time to confirmed

disability progression Under study assumptions – 260 events needed to provide

85% power for the disability endpoint, to detect Hazard Ratio of 0.7

N Engl J Med 2012;366:1000-9.

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Just before end of recruitment

15 months after trial start, 80 events have been observed Are we going to reach 260 events?

Management veto increase in sample size Acceptable alternative – increase treatment duration to 30

months

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Strategy – Whitehead’s framework

Estimate the overall survivor function from the blinded data Construct illustrative survival functions for treatment

groups consistent with assumed treatment effect and observed overall survival

Check whether trial is likely to produce number of events needed

Drug Information Journal, Vol. 35, 1387–1400, 2001

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Implementation

Maximum likelihood estimation from blinded data

Estimated number of events: At 24 months – 134 (55% power) At 30 months – 165 (64% power) Decision – end the study as planned, at the end of 24

months of treatment

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What really happened

Actual number of events at end of study – 132, but… Sample size was eventually 1106 the effect was higher than assumed: HR=0.64

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Drop-the-losers design

A multiple stage adaptive design that allows dropping the inferior treatment groups

General principles drop the inferior arms retain the control arm may modify or add additional arms

It is useful where there are uncertainties regarding the dose levels

Problem: dropped may contain valuable information regarding dose response

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Adaptive treatment-switching

Allowing the investigator to switch a patient’s treatment from an initial assignment to an alternative treatment if there is evidence of lack of efficacy or safety of the initial treatment

Commonly employed in cancer trials In practice, a high percentage of patients may

switch due to disease progression Estimation of survival is a challenge A high percentage of switching subjects maylead

to a change in hypotheses to be tested. Sample size adjustment for achieving a desired

power is critical to the success of the study.

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Adaptive-hypotheses design

A design that allows change in hypotheses based on interim analysis results

Examples switch from a superiority hypothesis to a non-

inferiority hypothesis Can the non-inferiority margin be adapted? Sample size calculation/re-estimation is needed

change in study endpoints Switch primary and secondary endpoints Switch from the primary endpoint to a co-primary

endpoint or a composite endpoint

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Seamless design

Combining two separate trials into one trial and would use data from patients enrolled before and after the adaptation in the final analysis Learning phase (e.g., phase II) Confirmatory phase (e.g., phase III)

Addresses study objectives of individual studies Utilizes data collected from both phases for final analysis Is it efficient?

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Seamless design scheme

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Example (Jennison &Turnbull, 2006)

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The results

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Combining results - BK method

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Combining results - TSE method

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Multiple adaptive design

Any combinations of two or more adaptation Very flexible Very attractive Very complicated Statistical inference is often difficult, if not

impossible to obtain Very painful

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Take home message

Adaptive Plan

… not Adaptive Plane