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East® Orientation Webinar Series: Phase 2 Dose-finding Studies with MCP and Modelling Techniques
Pantelis [email protected]
Agenda
• Methodology
• Case study
• Q/A
• Conclusion
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East gives you easy access to the adaptive designs that matter
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ESCALATEWide selection of model-based adaptive designs for Phase 1 dose escalation studies.
BASEPopular tools for fixed-sample clinical trials.
Multiple Comparison Procedure Modeling for Phase 2 dose-finding studies.
MCPmod ENDPOINTS
Strategic testing of multiple endpoints.
EXACTTools for small sample clinical trials with binomial endpoints.
SEQUENTIALTools for group sequential clinical trials with normal or binomial endpoints.
MULTIARMTools for multi-arm fixed-sample clinical trials.
Multi-arm multi-stage clinical trials.
MAMS ADAPTAllow for sample size re-estimation in trials with normal and binomial endpoints.
SURVIVALTools for group sequential clinical trials with survival endpoints.
ENRICHAllow for population enrichment in trials with survival endpoints.
SURVADAPTAllow for sample size re-estimation in trials with survival endpoints.
Predict future course of trial at outset and interim analyses.
PREDICT PROGRAMDesign through simulation.
Multi-arm multi-stage clinical trials.
MAMS
Today’s presentation
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BASEPopular tools for fixed-sample clinical trials.
ENDPOINTS
Strategic testing of multiple endpoints.
EXACTTools for small sample clinical trials with binomial endpoints.
SEQUENTIALTools for group sequential clinical trials with normal or binomial endpoints.
MULTIARMTools for multi-arm fixed-sample clinical trials.
ADAPTAllow for sample size re-estimation in trials with normal and binomial endpoints.
SURVIVALTools for group sequential clinical trials with survival endpoints.
ENRICHAllow for population enrichment in trials with survival endpoints.
SURVADAPTAllow for sample size re-estimation in trials with survival endpoints.
Predict future course of trial at outset and interim analyses.
PREDICT
ESCALATEWide selection of model-based adaptive designs for Phase 1 dose escalation studies.
PROGRAMDesign through simulation.
Multiple Comparison Procedure Modeling for Phase 2 dose-finding studies.
MCPmod
East MCPModMULTIPLE COMPARISON AND MODELING APPROACHES FOR DOSE FINDING
Pre-Requisites: East BASE
Functions: • MCPMod design: design dose-finding clinical trials• MCPMod Analysis: analyze continuous, discrete or count data using the MCPMod methodology
Benefits:
• Combination of MCP (Multiple comparison procedures) and Mod (modeling) approaches (Bornkamp et al., 2009) for fitting a dose-response • East accepts raw subject data, summarized data or data with a covariance matrix for discrete and count data and raw data for continuous endpoints. • Perform multiple contrast tests to control Type I error, adjusting for the fact that multiple candidate dose-response models are being considered.
Note: • Note: Cytel also offers Proc MCPMod as a SAS PROC to facilitate your usage of SAS to analyze your data using the modern MCPMod method.
SUCCESSFUL OUTCOME: Improve efficiency of identifying a more effective dose
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Methodology
Dose-Response Studies
Establish Proof-of-Concept (PoC)• Change in dose desirable change in endpoint of interest
Dose finding step• Select one (or more) “good” dose levels for confirmatory Phase III once
PoC has been established
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Traditional ApproachProof-of-Concept: Conducted using (multiple) active arms and placebo
Selection of Target Dose: 1. statistically significant at the proof-of-concept stage2. smallest of statistically significant doses but also clinically relevant
Dose-Response Modeling: 1. use data from PoC and earlier trials2. find a statistical model capturing the effects of target dose on dose-response
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Traditional ApproachStraight-forward approach
However:• focuses on narrow dose range where sponsors can have faith that they will establish a
clear dose-signal
• dose-response model should itself play a greater role in choosing the right dose
• focuses on modeling at the very end of the process
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MCP vs Mod
MCP: • Dose is a qualitative factor
• Inference about target dose restricted to the discrete set of doses used in the trial
Mod: Dose Response (parametric functional relationship)• Dose is quantitative
• Modeling approach validity depends on pre-specification of appropriate dose-response model
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Multiple Comparison Procedures – Modelling
MCP-step• Establish a dose-response signal (the dose-response curve is not flat) using multiple
comparison procedures
Mod-step• Estimate the dose-response curve and target doses of interest (ED50, ED90, MED, etc)
using modelling techniques (nonlinear regression)
Modelling is pre-specified at the design stage• Account for uncertainty in the dose-response model via a set of candidate dose response
models• Use model selection or model averaging to combine different models
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MCP + Mod = MCPModDesign Stage
• Pre-specification of candidate dose-response models
Analysis Stage (MCP-step)• Statistical test for dose-
response signal. Model selection based on significant dose response models
Analysis Stage (Mod-step)• Dose response and target
dose estimation based on dose-response modeling
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Tria
l Des
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Stag
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sis S
tage
MCP + Mod = MCPMod
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Tria
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Stag
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Tria
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Stag
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Design Stage• Pre-specification of candidate
dose-response modelsAnalysis Stage (MCP-step)
• Statistical test for dose-response signal. Model selection based on significant dose response models
Analysis Stage (Mod-step)• Dose response and target
dose estimation based on dose-response modeling
List of dose-response models
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MCP-Mod : ScopeDevelopment Phase
• Ph II dose-response studies to support dose selection for Phase III
Response can be continuous, binary, count, time-to-event
Number of doses, dose-range• Minimum: 2 active doses (for the MCP-step), 3 active doses (Mod step)• Recommendations (rules of thumb): 4-7 active doses, >10-fold dose range
Control• MCP-step makes most sense when there is a placebo control in the trial
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Bjorn Bornkamp, EFSPI Meeting, Nov 2015
Regulatory OpinionCHMP: First opinion issued in 2010, since then 12 qualification opinions (biomarkers, technologies/devices, simulation models)
• MCP-Mod first statistical methodology qualified
FDA: Issued its Fit-for-Purpose (FFP) designation for guiding dose selection for Phase III testing. • https://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/UCM508700.pdf
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Case Studies
Case Study 1: BIOM Trial (design)• randomized double-blind parallel group trial with patients being allocated to either
placebo or one of four active doses coded as 0.05, 0.20, 0.60, and 1
• Response variable is baseline adjusted abdominal pain score
• larger values correspond to better treatment effect
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The Design Part• One-sided type I error of 0.05• Calculate Power for a total sample size of 100
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The Design Part• Candidate Models:
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Patient AllocationOptimized:
Equal:
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Output Summary
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Output Details
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Power Plot for Mean Power function
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Sample Size for 90% Mean Power
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Case study 2: Schizophrenia study (analysis)Balanced design, total sample size of 240 patients randomly allocated to four
arms:• Placebo• Low dose (40 mg)• Medium dose (80 mg)• High dose (120 mg)Primary endpoint change from baseline in the Positive and Negative Syndrome Scale (PANSS) total score to Week 4; Low value = beneficial effect• Type I error 0.025, one-sided
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The dataThere are 240 rows in the data set (one record per patient) with the following variables:• Subjid: Patient ID.• Dose: Dose of the experimental treatment (0, 40, 80, or 120).• Improvement: Reduction from baseline to Week 4 in the PANSS total score. Note
that the reduction from baseline is a positive quantity, whereas the change from baseline is a negative quantity in this trial.
• Baseline: PANSS total score at baseline.• Gender: Patient's gender.
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Boxplots of improvement vs dose levels
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The Analysis PartThe Set Of Candidate Models Includes: 1. emax1: Emax(100)2. linlog1: Lin in Log Dose3. linear1: Linear4. expn1: Exponential(100)
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The Analysis PartSome More About The Options Used:• Significant models will be selected using the pValue method (adjusted p-values).
• The adequate model will be selected based on its AIC (default choice).
• Estimate: Target dose is specified as the dose level that achieves a target effect of Delta over placebo
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The Analysis Part
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Analysis/Candidate Models
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Analysis/Candidate models
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Analysis: Output
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Analysis: Output
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MCP Part: • Multiple Contrast test: Observe that the adjusted p-values Emax and Linear
model are less than 0.025.
Mod part:• Fit the significant models to the data and estimate the target dose.• Model selection is based on the AIC criterion
Analysis: Output
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Analysis: Output
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Analysis: Output
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Modelling part: • Based on the model selection criteria of minimum AIC, Linear is the adequate model.• The corresponding target dose is 120 (108.6 in continuous scale). • Note that the target dose selection might not be consistent across the clinically
relevant models (100 to 108.6mgs)• To develop a unified dose selection strategy, it is recommended to consider the model
averaging approach (AvgAIC)
References
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• Bornkamp, B. et al (2007) Innovative Approaches for Designing and Analyzing Adaptive Dose-Ranging Trials, Journal of Biopharmaceutical Statistics, 17, 965-995
• Bretz, F., Pinheiro, J.C., and Branson, M. (2005) Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics, 61, 738–748
• EMA (2014) Qualification opinion of MCP-Mod as an efficient statistical methodology for model-based design and analysis of Phase II dose finding studies under model uncertainty, http://goo.gl/imT7IT
• Pinheiro, J.C., Bornkamp, B., Glimm, E., and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models. Statistics in Medicine, 33, 1646–1661
Thank you!
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Pantelis Vlachos, Ph.D. [email protected] Inc. | Geneva
Connect with Pantelis on LinkedIn
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Thank you
Easy Access to the Adaptive Designs That Matter
Delivered by the Thought Leaders
Behind the Methods
Software that is Faster & Easier
to Use
Popular Fixed and Adaptive Designs at your Fingertips
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Global Products and Services
StrategicConsulting
Project-BasedServices
FunctionalServices
Provision (FSP)
StatisticalSoftware
Industry standard for trial design, including CID adaptive (East, EOD)
Leader in exact statistical solutions (Xact: StatXact, LogXact, Procs)
Operations software (e.g. ACES, EnForeSys, FlexRandomizer)
All 25 top biopharma companies, the FDA, EMA & PMDA use our software
PhD statisticians expert in innovative design & complex statistical questions
Experts in Data Science, PK/PD, Enrolment & Event Forecasting, Portfolio/Program Optimization (NPV)
Reliable Biometrics service provider delivering high quality, on time
Lead staff with over 15 years industry experience on average
Including biostatistics & programming, ISC, data management, PK/PD analysis, medical writing
Creation of dedicated teams operating within/as an extension of the client’s own biostatistics & programming, data management and PK/PD teams
Leader in offshoring of Biometrics competencies
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Q&A Session
Conclusion
Concluding Remarks
• The pairwise tests tend to be underpowered in Phase II trials, and it is recommended to use contrast-based tests instead
• The MCP-Mod procedure was introduced as an extension of multi-contrast tests.
• It simultaneously addresses the goals of hypothesis testing and estimation in dose-finding trials
• MCPMod improves efficiency of identifying a more effective dose
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Upcoming Webinars
Topic Date Time
Adaptive Umbrella Trial Using MAMS Module
Tuesday, April 14, 2020 11:00AM EDT | 16:00 GMT
Phase 1 dose escalation trials with ESCALATE
Wednesday, April 22, 2020 11:00AM EDT | 16:00 GMT
Phase 2 Dose-finding Studies with MCP and Modelling Techniques
Wednesday, April 29, 2020 11:00AM EDT | 16:00 GMT
Conducting Sample Size Reassessment with Time-to-event Endpoints
Wednesday, May 6, 2020 11:00AM EDT | 16:00 GMT
Refocus your Enrollment to the Subpopulation of Interest with ENRICH
Wednesday, May 13, 2020 11:00AM EDT | 16:00 GMT
Respond to survey in post-webinar thank you email to request certificate of attendance for today’s webinar. Recordings will be posted to www.cytel.com. 47
