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Dose-adaptive study designs offer benefits for proof-of-concept / Phase IIa clinical trials, as well as raise issues for continued research. OUTLINE: Dose-Adaptive Designs & Examples Definition & Introduction (Jim) Frequentist Designs, including Random Walk Designs (Jim) 3+3 Design for cancer

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slide1

Dose-adaptive study designs offer benefits for proof-of-concept / Phase IIa clinical trials,as well as raise issues for continued research

OUTLINE: Dose-Adaptive Designs & Examples

  • Definition & Introduction (Jim)
  • Frequentist Designs, including Random Walk Designs (Jim)
    • 3+3 Design for cancer
    • Up&Down Design
    • Biased Coin Designs
    • Simulations of Up&Down Design for Dental Pain Clinical Trial
  • Bayesian-type Designs (Inna)
    • Continual Reassessment Method (CRM)
    • Bayesian D-optimal Design
    • Other related approaches
    • Bayesian 4-parameter logistic
  • Case Study (adaptive cross-over design)
    • CytelSim Software demo
  • Summary & Recommendations (Inna)
  • References

Jim Bolognese & Inna Perevozskaya, Sept. 12, 2008

continual reassessment method crm
Continual Reassessment Method (CRM)
  • Most known Bayesian method for Phase I trials
  • Underlying dose-response relationship is described by a 1-parameter function family
  • For a predefined set of doses to be studied and a binary response, estimates dose level (MTD) that yields a particular proportion (P) of responses
  • CRM uses Bayes theorem with accruing data to update the distribution of MTD based on previous responses
  • After each patient’s response, posterior distribution of model parameter is updated; predicted probabilities of a toxic response at each dose level are updated
  • The dose level for next patient is selected as the one with predicted probability closest to the target level of response
  • Procedure stops after N patients enrolled
  • Final estimate of MTD: dose with posterior probability closest to P after N patients
  • The method is designed to converge to MTD

Innovative Clinical Drug Development Conference

continual reassessment method cont
Continual Reassessment Method (cont.)

Choose

initial estimate

of response

distribution

& choose

initial dose

Update Dose

Response Model

& estimate

Prob. (Resp.)

@ each dose

Obtain next

Patient’s

Observation

Next Pt. Dose

= Dose w/

Prob. (Resp.)

Closest to

Target level

Stop.

EDxx = Dose w/

Prob. (Resp.)

Closest to

Target level

Max N

Reached?

no

yes

escalation with overdose control ewoc bayesian design
Escalation With Overdose Control (EWOC) Bayesian Design
  • Assigns doses similarly to CRM, except for overdose control
    • predicted probability of next assignment exceeding MTD is controlled (Bayesian feasible design)
    • this distinction is particularly important in oncology
  • Assumes a model for the dose-response curve in terms of two parameters:
    • MTD
    • probability of response at dose D1
  • EWOC updates posterior distribution of MTD based on this two-parameter model
  • Free software available here:
    • http://sisyphus.emory.edu/software_ewoc.php
  • Reference:
    • Z.Xu, M. Tighiouart, A. RogatkoEWOC 2.0: Interactive Software for Dose Escalation in Cancer Phase I Clinical Trials Drug Information Journal 2007 : 41(02)

Babb, et al., 1998

decision theoretic approaches
Decision Theoretic Approaches
  • Similar to CRM
  • Incorporates elements of Bayesian Decision Theory
  • Designed to study a particular set of dose levels D1, . . ., Dk
  • Two-parameter model for dose response with prior distributions on the parameters
  • Loss function minimizes asymptotic variance of dose which yields a particular proportion of responses
  • Posterior distribution estimates of the 2 parameters used to derive next dose, i.e., that estimated to have desired response level

Whitehead, et al., 1995

bayesian d optimal sequential design
Bayesian D-Optimal Sequential Design
  • Based on formal theory of optimal design (Atkinson and Donev, 1992)
  • Similar to EWOC, a constraint is added to address the ethical dilemma of avoiding extremely high doses
  • Uses a two parameter logistic model for dose response curve
    • Slope & location
    • Binary endpoint
    • Minimum response rate fixed at 0%, maximum at 100%
  • Sequential procedure assigns dose at each stage which minimizes variance of posterior distribution of model parameters

Haines, et al., 2003

simulated bayesian d optimal design for ed50 http haggis umbc edu cgi bin dinteractive inna1 html
Simulated Bayesian D-Optimal Design for ED50 (http://haggis.umbc.edu/cgi-bin/dinteractive/inna1.html)
  • Efficacy: Percent of patients with “Response”

assumed underlying distribution

Dose: 1 2 3 4 5 6 7

%Response: 30 40 55 65 75 75 75

  • Prior estimates: ED25 between doses 1 and 2

ED50 between doses 2 and 3

  • 6 patients in Stage 1 for seeding purposes
    • D-Optimal Design: 3 pts at dose 1, 2 at dose 3, 1 at dose 4

# responses: 1 1 1

  • 24 subsequent patients (total 30 patients) entered sequentially at doses yielding minimum variance of model for ED50 estimate
  • Response / non-response assigned to approximate targeted %G/E distribution above
slide8
Simulated Bayesian D-Optimal Designfor ED50 – Results (http://haggis.umbc.edu/cgi-bin/dinteractive/inna1.html)
simulated bayesian d optimal design for ed50 summary
Simulated Bayesian D-Optimal Designfor ED50 – Summary
  • Results from a single implementation

Dose: 1 2 3 4 5 6 7

assumed %Response: 30 40 55 65 75 75 75

#Responses: 4 - 2 1 8 - -

#patients: 13 0 4 1 12 0 0

observed %Response: 31 - 50 100 67 - -

  • Bayesian estimated ED50 = dose 2.3
  • However, few observations at other than 2 doses due to optimal design for particular dose-response model
  • Dose-Response curve between those two doses could be interpolated by the underlying fitted model
    • Should not extrapolate from model outside observed range
    • 2-parameter model (slope, location) forced through 0% and 100%
bayesian design for the 4 parameter logistic model
Bayesian Design for the 4-parameter Logistic model
  • Underlying model:

Available doses:

Yijis (continuous) response of the j-th subject on the i-thdose, di

 is the vector of parameters of the distribution f

  • Patients are randomized in cohorts
  • Within each cohort, fixed fraction (e.g. 25%) is allocated to placebo,
  • For the remaining patients within cohort, dose is picked adaptively out of
  • Doses are picked so that QWV (Quantile Weighted Variance) utility function is minimized

Developed by S. Berry for CytelSim (~2006)

bivariate models penalized adaptive d optimal designs
Bivariate Models: Penalized Adaptive D-optimal Designs
  • Addresses safety and efficacy simultaneously
    • Design is characterized by two dependent binary outcomes (efficacy and toxicity)
    • Similar to univariate model: involves dose-escalation and early stopping rules
  • Similar to Bayesian Sequential D-Optimal Design:
    • Model-based approach with formal optimality criteria:

“maximize the expected increment of information at each dose”

    • Instead of Bayesian posterior update, Maximum Likelihood Estimates of current trial data used for next dose selection
  • “Penalized” design: Introduces various constraints that can be flexible to reflect ethical concerns, cost, sample size, etc.

Dragalin, 2005

bayesian type designs pros cons
Bayesian-type DesignsPros (+) & Cons (-)

+ Minimize observations at doses of little interest (too small or large)

+ CRM assigns doses which migrate & cluster around EDxx

- little info on dose-response away from targeted dose (e.g., ED50)

+ can compensate by targeting 2 or 3 response levels

+ Bayesian D-optimal design efficiently estimates model-based dose-response curve (& targeted EDxx)

- yields most observations at 2 dose levels to optimally fit model

- model restrictive

- forced through 0% and 100% response levels

- should not extrapolate response levels beyond observed doses

bayesian type designs pros cons13
Bayesian-type DesignsPros (+) & Cons (-)

- Subjective nature of assignment of prior (starting) distribution

- could take many observations to overcome an incorrect prior

- Models underlying current methods not general enough for efficacy endpoints

- 4-parameter model needed to estimate min & max response levels

- Co-factors not included; could confound estimates

+ execute designs within important co-factor levels

- Computations complex; little software available

- Difficult to explain to clients

- Not yet proven substantially better than up-and-down or t-statistic s designs when aim is estimation of dose-response curve

logistics for conduct of a dose adaptive designed trial
Logistics for Conduct of a Dose-Adaptive Designed Trial
  • Response observable reasonably quickly
  • Increased statistical computations / simulations to justify dose-adaptive scheme in protocol
  • Need on-call person to assess previous response data and generate dose for next subject
    • For model-based dose-adaptive designs, need on-call unblinded statistician for associated analyses
    • OR, this could be automated via web-based interface (increases cost)
    • Rapid transfer of needed data
  • Need special packaging or unblinded pharmacist at site to package selected dose for each patient
remarks 2
Remarks (2)
  • Logistics of implementation more complicated than usual parallel group design
    • Frequent data calls / brief simple analyses
    • Close contact with sites re: dose assignments
    • Special packaging (IVRS??)
    • Drug Supply – needed sufficiently for many possibilities
  • Tolerability rule(s) can be added for downward dose-assignment if pre-specified AE criteria are encountered
    • This has been studied in context of Bayesian dose-adaptive designs, but not in context of up&down designs
  • Number of placebo patients maintained as designed for intended precision vs. that group; could be down-sized, though
dose adaptive design summary
Dose-Adaptive DesignSummary
  • Allocation of dose for next subject based on response(s) of previous subject(s)
    • Random Walk designs: only last subject’s response
    • T-statistic (frequentist) designs: all previous subjects’ responses
    • Bayesian-type designs: all previous subjects’ responses
  • High potential to limit subject allocation to doses of little interest (too high / too low)
    • Maximize information gathered from fixed N
    • Ethical advantage over fixed randomization
    • More attractive to patients / subjects
  • Inference conditional on doses assigned by design, but not overly important in early development
  • Requires more statistical up-front work (simulation)
  • No pre-specified allocation schedule; requires ongoing communication with site regarding allocation
dose adaptive design summary17
Dose-Adaptive Design Summary
  • Bayesian-type designs preferable to estimate dose-response curve; can also estimate a dose-response quantile of interest (e.g., EDxx) or (part of?) region of increasing dose-response
    • Complex; heavy computations
  • Random Walk & T-statistic Designs focus on quantile(s) of interest
    • Easy to understand & program
    • Consider as starting point for implementing dose-adaptive design
    • Let other design features guide towards other adaptive techniques based on particular experimental situation
  • Ongoing incomplete simulations have yet to identify major advantage of Bayesian-type designs over RW & T, unless prior information is important to consider.
  • Study, comparison, & refinement of these dose-adaptive designs continues
slide18

Dose-adaptive study designs offer benefits for proof-of-concept / Phase IIa clinical trials,as well as raise issues for continued research

OUTLINE: Dose-Adaptive Designs & Examples

  • Definition & Introduction (Jim)
  • Frequentist Designs, including Random Walk Designs (Jim)
    • 3+3 Design for cancer
    • Up&Down Design
    • Biased Coin Designs
    • Simulations of Up&Down Design for Dental Pain Clinical Trial
  • Bayesian-type Designs (Inna)
    • Continual Reassessment Method (CRM)
    • Bayesian D-optimal Design
    • Other related approaches
    • Bayesian 4-parameter logistic
  • Case Study (Bayesian and Adaptive cross-over designs)
    • CytelSim Software demo
  • Summary & Recommendations (Inna)
  • References

Jim Bolognese & Inna Perevozskaya, Sept. 12, 2008

case study example

Case Study Example

Adaptive Dose-Ranging POC study

By I. Perevozskaya and Y. Tymofyeyev

study background
Study Background
  • Development phase: Ib
  • Strategic objective: generate preliminary D-R info to optimize dose selection for Phase IIb study
  • Caveats:
    • Phase Ib will be run using surrogateendpoint
    • Future Phase IIb will be driven by clinical endpoint (chronic symptoms)
    • There is no formally established relationship between surrogate and clinical endpoints dose-response curves, but…
    • Dose selected as “sub-maximal” using surrogate endpoint D-R curve is believed to be “sub-therapeutic” for the clinical endpoint.
study objectives
Study objectives
  • (Broad) to demonstrate that a single administration of drug, compared with placebo, provides response that varies by dose
  • (Specific)
    • Find “sub-maximal” dose (e.g. ED75 defined as the dose yielding 75% of the placebo-adjusted maximal response )
    • Meaningfully describe dose-response relationship
    • Demonstrate that at least one dose is significantly different from placebo
study design challenges and adaptive design opportunity
Study Design challenges and Adaptive design opportunity
  • Easy to miss informative dose range with traditional design
    • Dose-response (D-R) can be relatively steep in the sloping part of the D-R curve
    • Dose-range & shape of curve = Unknown (and “unknowable” using PK)
    • Dose range to explore is very wide (6 active doses potentially considered)
  • Logistics:
    • Primary endpoint captured electronically within 1 day
    • The expected subject enrolment rate is not too high
  • Endpoint suitable for cross-over design
    • Following single-dose administration, 3-7 day washout is sufficient
    • 3-period (or even 4-period) cross-over could be reasonable
bayesian adaptive design description
Bayesian Adaptive Design Description
  • 6 active doses and placebo available
  • Design uses frequent looks at the data and adaptations (dose selections) are made after each IA
    • Patients are randomized in cohorts
    • Cohort is a small group of patients randomized between IAs
    • Within each cohort, fixed fraction (e.g. 25%) is allocated to placebo
    • For the remaining patients within cohort, dose is picked adaptively out of D1, ….D6.
    • Once endpoints for the whole cohort become available, decision is made about next cohort allocation using Bayesian algorithm (QWV utility function)
bayesian adaptive design description cont
Bayesian Adaptive Design Description (cont.)
  • The algorithm will try to cluster dose assignments around the “interesting” part of dose-response curve (e.g. ED75)
    • but there will be some spread around it (i.e. not all patients within cohort will go to the same dose).
    • The “target” will be moving for each cohort to be randomized
      • will depend on the trial information accumulated to the moment:
        • previous cohort’s dose allocations and responses.
bayesian adaptive design for the 4 parameter logistic model details
Bayesian Adaptive Design for the 4-Parameter Logistic Model: Details
  • Underlying model:

Available doses:

Yijis (continuous) response of the j-th subject on the i-thdose, di

 is the vector of parameters of the distribution f

  • Patients are randomized in cohorts
  • Within each cohort, fixed fraction (e.g. 25%) is allocated to placebo,
  • For the remaining patients within cohort, dose is picked adaptively out of
  • Doses are picked so that QWV (Quantile Weighted Variance) utility function is minimized

Developed by S. Berry for CytelSim (~2006)

implementation details of bayesian algorithm
Implementation details of Bayesian Algorithm
  • Developed by Scott Berry
  • Implemented in Cytel Simulation Bench software developed by Cytel in collaboration with Merck
  • Core idea: algorithm utilizes Bayesian updates of model parameters after each cohort
    • Components of ( , , ,) in 4-param. logistic model are treated as random with prior distribution (usually flat) placed upon them
    • After each cohort’s response, the (posterior) parameter distribution is updated and model D-R is re-estimated
    • The algorithm utilizes Minimum Weighted Variance utility function for decision making during adaptations
      • In our example, that translates into next cohort’s dose assignments are picked so that the variance of the response at the current estimate of ED75 is as small as possible
bayesian adaptive design allocation example
Bayesian Adaptive Design Allocation Example
  • Sample size modeled: N=120
    • 10 cohorts of 12 patients in parallel design setting
  • Different dose allocations of 12 patients in each cohort are represented by different colors (dark blue is Pbo=D0, D1 is blue, …., brown is D6).
bayesian adaptive design example cont
Bayesian Adaptive Design Example (Cont.)

Dose-response curve and Patient allocations to each dose at the end of this example trial

    • Assume the "true" max. effect dose (i.e., the dose to be "discovered" by the study) is "midpoint between Dose 2 and Dose 3
  • D-R curve captured "Dose 3" as correct start of plateau.
    • Most patients were close to “Dose 3”
    • Few patients were on plateau
bayesian algorithm allocation rule
Bayesian Algorithm Allocation rule
  • The algorithm clusters dose assignments around the “interesting” part of dose-response curve
  • With some “spread” around it (i.e. not all patients within cohort will go to the same dose).
  • The “target” is data-dependent and may change after each IA
performance of bayesian ad under various dose response scenarios
Performance of Bayesian AD Under Various Dose-Response scenarios
  • 8 different dose-response scenarios were studied, varying in:
    • Magnitude of maximum treatment effect
    • Location of the sloping part of the DR curve
    • Steepness of the sloping part of the DR curve
  • Allowing "true" ED75 to vary over the dose-range
performance of bayesian ad under various dose response scenarios cont
Performance of Bayesian AD Under Various Dose-Response scenarios (cont.)
  • Performance evaluated via simulations (using CytelSim Software)
  • Key criteria for evaluation included
    • Subject allocation pattern
    • Precision of picking “right dose” correctly
    • Power and Type I error for detecting dose-response
    • Precision of overall D-R estimation across all doses (measured by MSE)
power and type i error for detecting dose response
Power and Type I Error for Detecting Dose-Response

*

*Power for D-R curve ID 1 is Type I error

mse efficiency plots
MSE Efficiency Plots

Curve ID 3 (centered) Target dose is D3

Curve ID 5 (left-shifted) Target dose is between D2&D3

mse efficiency plots cont
MSE Efficiency Plots (cont.)

Curve ID is 4 (right-shifted) target dose is D5

Curve ID is 8 (flat) target dose- NA

summary of bayesian design simulations
Summary of Bayesian Design Simulations
  • In all 7 non-flat D-R scenarios, the design maximized allocations around the “true” ED75.
  • In case of flat D-R, most patients were allocated to max dose and placebo with very little in between
  • Type I error was preserved
  • Power to detect a dose-response is at least 90%
  • Power to detect a significant difference between the best dose and placebo is at least 89.5%
  • For all scenarios, AD design was uniformly more efficient than fixed design of the same sample size (measured by MSE ratio across doses )
further steps
Further Steps
  • Simulations have shown that Bayesian AD design may adequately address the Ib study objectives:
    • A definitive single dose for Phase III was NOT needed
    • General idea about D-R needed: upper/lower plateau, sloping part
  • Due to absence of readily available software for crossover design, these computer simulations used N=120 in a parallel design setting
    • It was anticipated that similar results for power and Type 1 error could be obtained using N=30 subjects each contributing 4 measurements
further steps cont
Further Steps (cont.)
  • Crossover-like framework preferable to parallel design framework
    • between/within subject variability => sample size considerations ( 30 vs. 120)
    • short drug half-life -> short washout period
  • Option 1: modify Bayesian design so that each subject can contribute multiple measurements
    • incorporate repeated-measures in modeling and simulations
    • Required involvement of external vendor and extra time to complete both simulator and randomizer
  • Option 2: consider true crossover design but change doses adaptively
    • Non-Bayesian approach
    • Could be accomplished in-house within approximately the same timeframe due to lower computational complexity
    • Can be reduced to “standard” crossover if no dose adjustment takes place
adaptive crossover design highlights
Adaptive Crossover Design Highlights
  • Doses explored: {D1, …, D6} of Merck-X + pbo
  • Based on: 4 period crossover
    • Pbo + active doses A,B,C
    • Values of A, B, C are subset of {D1, …, D6} and change dynamically after each interim look (~twice weekly)
    • Time-to-endpoint + washout is 1 week
  • Decision rule: pick a subset of doses {A, B, C} from {D1, …, D6} based on (non-Bayesian) utility function
  • Utility function: cumulative score describing proximity of each dose to target ED75 according to current estimate of D-R
  • D-R estimation: based on isotonic regression model
adaptive x over algorithm details score function
Adaptive X-over Algorithm details: Score function

For each 3 dose combination, say {A,B,C}, the score is S(qA)+S(qB)+S(qC).

adaptive x over algorithm details selection of 3 dose combination
Adaptive X-over Algorithm details: Selection of 3-dose combination

Randomly select one of these 3 combinations and use until next adaptation.

adaptive x over algorithm details interim d r estimation at using isotonic regression
Adaptive X-over Algorithm details: Interim D-R estimation at usingisotonic regression

3 best dose combinations (based on proximity to ED75):

1. {3, 4, 5}

2. {3, 4, 6}

3. {3, 4, 7}

Algorithm randomly choose one combination out of the 3 best combinations

adaptive cross over design performance characteristics via simulations
Adaptive Cross-Over Design Performance Characteristics via Simulations
  • Several D-R scenarios were explored
  • Allocation pattern:
      • similar to Bayesian design, the algorithm allocates subjects to the neighborhood of the effective and the highest sub-effective dose levels
  • N=60 patients adequate to achieve ~80% or better power for “best dose” vs. placebo comparison
    • Type I error is preserved
  • Caveat:
      • effect sizes were smaller than those explored for Bayesian AD
      • This contributed to sample size increase from N=30 (30 patients*4 obs.=120obs) for Bayesian AD to N=60 (60 patients*4 obs. =240 obs.) for the adaptive crossover design
case study summary
Case Study Summary
  • After careful examination of many options (3 adaptive+ 3 fixed), dose-adaptive crossover design was selected
  • Efficiency of crossover AD over “standard” design:
    • A “default” 4 period cross-over design will require 44 subjects to detect the same effect sizes w/ similar power (44*4 = 176 observations)
    • But it would explore only 3 doses and placebo
  • Adaptive crossover design uses 240=60*4 observations to explore 6 doses + pbo
  • It is 240 vs. 176 (i.e. 36% more) observations than the standard 4 period cross-over (as a cost of exploringadditional 3 doses)
  • However, if 6 doses were explored with standard cross-over design
    • prohibitive study duration (7 periods)
    • would have 42*7=294 observations
    • True efficiency of AD: 294/240=1.23 (23% more efficient)
  • Protocol is approved and study is underway.
slide52

Dose-adaptive study designs offer benefits for proof-of-concept / Phase IIa clinical trials,as well as raise issues for continued research

OUTLINE: Dose-Adaptive Designs & Examples

  • Definition & Introduction (Jim)
  • Frequentist Designs, including Random Walk Designs (Jim)
    • 3+3 Design for cancer
    • Up&Down Design
    • Biased Coin Designs
    • Simulations of Up&Down Design for Dental Pain Clinical Trial
  • Bayesian-type Designs (Inna)
    • Continual Reassessment Method (CRM)
    • Bayesian D-optimal Design
    • Other related approaches
    • Bayesian 4-parameter logistic
  • Case Study (Bayesian and Adaptive cross-over designs)
    • CytelSim Software demo
  • Summary & Recommendations (Inna)
  • References

Jim Bolognese & Inna Perevozskaya, Sept. 12, 2008

cytel simulation bench cytelsim software for adaptive designs background and history
Cytel Simulation Bench (CytelSim) Software for adaptive designs: Background and History
  • Developed by Cytel Statistical Software in collaboration with Merck Team
  • Currently exists as an in-house tool
  • Production version planned for near future
  • Merck (BARDS) development team:
    • Jim Bolognese
    • Inna Perevozskaya
    • Yevgen Tymofyeyev
    • Jason Clark
cytel simulation bench cytelsim software for adaptive designs background and history cont
Cytel Simulation Bench (CytelSim) Software for adaptive designs: Background and History (cont.)
  • Focus: model – based designs for early stage adaptive dose-ranging studies with multiple IAs
  • Development started shortly after 1st BARDS AD grand rounds in July 2005
    • Rationale for development: surging interest in innovative design methodologies for dose-ranging studies
    • Simulations are crucial step for implementation of such designs
    • Original goal: to compare performances of a Bayesian design and up-and-down (Frequentist) design on the same platform
    • Other methods and more sophisticated interface added later as project needs grew
  • This project is still work–in-progress
cytelsim underlying methodology overview
CytelSim Underlying Methodology Overview
  • Applicable to early stage adaptive dose-ranging studies
  • Provides simulations for model-based design and analysis for such studies
  • General set-up:
    • Doses of the drug and of the placebo are available for administration
    • Subjects are enrolled and treated in groups, known as cohorts.
    • The size and composition of each cohort can be user-specified and may include a fixed number of subjects assigned to placebo
    • Active control arm may be included as well
cytelsim methodology overview cont
CytelSim Methodology Overview (cont.)
  • Study Endpoints:
    • Binary
    • Continuous
  • Bayesian Methods:
    • 4-parameter logistic model
    • CRM
  • Frequentist Methods:
    • Up & Down Design
    • Two Up & Down Sequence Design
    • T-test based Up & Down Design
    • Design for Umbrella-shaped D-R curve

Note: All methods are available for both endpoints except for CRM (available for binary endpoint only)

Reviewed through examplesand demo

t statistic methodology highlights
T-statistic methodology highlights
  • Frequentist method designed to cluster allocations around target dose of interest
  • Can be ran for 1 or 2 targets
  • Target is defined as difference from placebo in response or an absolute value of response variable
  • After each IA, T-test Based Up&Down Design assigns all subjects in a next cohort to a single dose
  • Dose chosen by comparing a t-statistic (based on all previous responses at a dose) to a set of user-defined dose escalation rules:
    • -2, -1, 0, +1, and +2 doses
  • More details in Example 1.

Note: differences from Bayesian design are highlighted

bayesian methodology highlights
Bayesian Methodology highlights
  • Bayesianparametric model-based method designed to cluster allocations around multiple targets of interest
  • Can be ran for virtually any number of targets
  • Target is defined as a percentage of maximal response or difference from placebo or an absolute value of a response variable
  • Priority of targets can be reflected through placing weights on them
  • After each IA, Bayesian Design assigns all subjects in a next cohort to multiple doses
  • Doses and allocation proportions are chosen by computing Quantile Weighted Variance (QWV) function (based on all previous responses at all doses)
    • Doses that give the best value of QWV (  smallest variance of response estimate) are given preference in allocation

Note: differences from T-stat design are highlighted

cytelsim interface overview
CytelSim Interface Overview

User Interface (GUI)

Computational Algorithm

Input and store simulation parameters

Graphic summaries

Output file in tabular format

Export to Excel Workbook

references
References

Rosenberger WF, Haines LM. Competing designs for phase I clinical trials: a review. Statistics in Medicine. 2002; 21:2757-2770

Haines LM, Perevozskaya I, Rosenberger WF. Bayesian optimal designs for phase I clinical trials. Biometrics. 2003; 59:561-600 (Bayesian D-optimal Design)

Stylianou M and Flournoy N. Dose Finding Using the Biased Coin Up-and-Down Design and Isotonic Regression. Biometrics. 2002; 58:171-177. (Biased Coin and Up&Down Designs)

Krams M, Lees KR, Hacke W, et al. Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN), an adaptive dose-response study of UK-279,276 in Acute Ischemic Stroke. Stroke. 2003; 34:2543-2548

Dougherty TB, Porche VH, Thall PF. Maximum tolerated dose of nalmefene in patients receiving epidural fentanyl and dilute bupivacaine for postoperative analgesia. Anesthesiology, 2000; 92(4):1010-1016.

Bolognese JA. A monte carlo comparison of three up-and-down designs for dose ranging. Controlled Clinical Trials, 1983; 4:187-196. (Up&Down Designs)

Bolognese JA, Gomez HJ, Tobert JA, Rucinska EJ. The up-and-down design for dose ranging and its use in various clinical settings. ASCPT, Atlanta, 1984.

Ivanova, A., Bolognese, J. and Perevozskaya, I. (2008). Adaptive design based on t-statistic for dose-response trials. Statistics in Medicine, 27, 1581-1592. (T-statistic Design)

He W, Liu J, Bincowitz B, Quan, H. A model-based approach in the estimation of maximum tolerated dose in phase I cancer clinical trials. MRL Technical report #102, June 2004. (3+3 Design)

references cont
References (cont.)

Geller NL. Design of phase I and II clinical trials in cancer: a statistician’s view. Cancer Investigations 1984; 2:483–491. (3+3 design in cancer)

Durham SD, Flournoy N. Random walks for quantile estimation. In Statistical Decision Theory and Related Topics, Gupta SS, Berger JO (eds). Springer: New York, 1994; 467–476. (RWR)

O’Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for phase I clinical trials in cancer. Biometrics 1990; 46:33–48. (CRM)

Babb J, Rogatko A, Zacks S. Cancer phase I clinical trials: efficient dose escalation with overdose control. Statistics in Medicine 1998; 17:1103 –1120.

Whitehead J, Brunier H. Bayesian decision procedures for dose determining experiments. Statistics in Medicine1995; 14:885–893 ( decision-theoretic approach)

Dragalin V., Fedorov V. Adaptive model-based designs for dose-finding studies. To appear in Journal of Statistical Planning and Inference, 2005 (bivariate models)

Reiner E, Paoletti X, O’Quigley J. Operating characteristics of the standard phase I clinical trial design. Computational Statistics and Data Analysis 1999; 30:303 –315.

Atkinson AC, Donev AN. Optimum Experimental Designs. Oxford: Clarendon, 1992.

slide66

Dose-adaptive study designs offer benefits for

proof-of-concept / Phase IIa clinical trials,

as well as raise issues for continued research

QUESTIONS

/ COMMENTS

/ DISCUSSION

backups
Backups
  • Additional example of Bayesian Design: ASTIN trial
  • Select Simulation results for adaptive crossover design
customized bayesian adaptive design acute stroke example astin trial
Customized Bayesian Adaptive DesignAcute Stroke example (ASTIN Trial)
  • Single IV infusion of UK-279,276 (neutrophil inhibitory factor) or placebo within 6 hours of stroke symptom onset
  • Bayesian sequential design with real-time efficacy data capture
    • Continuous reassessment of dose-response
  • Patients randomized to
    • Placebo (at least 15%), or
    • The 1 of 15 doses which minimizes expected variance of ED95 response

Krams, et al. STROKE (2003)

customized bayesian adaptive design acute stroke example
Customized Bayesian Adaptive DesignAcute Stroke example
  • Pre-defined stopping rules:
    • Minimum 500 pts before futility rule could be invoked
    • Minimum 250 pts before stopping rule for efficacy could be invoked
    • Maximum sample size = 1300 pts
  • If successful, designed for extension to definitive Phase III trial
  • Simulations used to characterize behavior (power) of design
    • For flat dose-response, 661 median sample size for futility stopping w/ 5% false positive rate
    • For 3-point efficacy benefit, 85% of simulations correctly stopped with median sample size 595
      • (w/ 4 points, 97% power, median ss 320).

Krams, et al. STROKE (2003)

customized bayesian adaptive design acute stroke example results
Customized Bayesian Adaptive DesignAcute Stroke example - results
  • ΔSSS/Dose curve (diff from pbo) w/ 95% posterior credible intervals
  • E at 2 -> efficacy threshold
  • F at 1 -> futility threshold

Dose of UK-279,276(mg)

setup to evaluate performance of ad under various dose response scenarios
Setup to Evaluate Performance of AD Under Various Dose-Response scenarios
  • AD uses 60 * 4 =240 total observations from 60 subjects.
  • 12 different dose-response scenarios were studied, varying in:
    • Location of the sloping part of the DR curve
    • Steepness of the sloping part of the DR curve
  • Performance of AD was addressed by simulations incorporation the following features
    • Poisson distributed subject enrollment
    • Response generated according to the underlying true D-R, imposing appropriate correlation structure regarding between- and within-subject variability.
    • Delay in response / washout period
    • Missing observations
performance of selected ad
Performance of Selected AD
  • Key criteria for evaluation included
    • Subject allocation pattern
    • Power for testing superiority of a dose level versus placebo; Type I error
    • Dose selection properties ( right identification of D-R plateau )
    • Simultaneous confidence interval methodology for isotonic regression is developed in the literature and can be used for dose-response curve estimation
conclusions from simulations
Conclusions From Simulations
  • On average, the algorithm allocates subjects to the neighborhood of the effective and the highest sub-effective dose levels for the studied D-R scenarios.
    • Depending on the underlying dose-response scenario, the average number of observations on the dose with highest number of subjects ranged from 43 to 60.
  • Good estimation of the D-R plateau on set
  • High power to declare effective and highest sub-effective dose levels superior to placebo.
    • Depending on the scenario, the power of the AD to declare the dose with highest number of subjects significantly different from placebo ranged from 75 to 91% with most scenarios yielding power 80% or greater.