Facts3webinar
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Transcript of Facts3webinar
FACTS 3 New Features – Dose finding
and Indication finding
Scott Berry
Tom Parke
Kert Viele
Webinar 5/21/2013
What is FACTS
• Platform for simulating clinical trials
– Virtual Subject Simulation
– Design Specification
– Execution Variables
• Allows complex adaptive designs
• Trial comparisons, efficiencies, and strengths
• Built on compiled low-level languages – very fast
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FACTS 3
• Rollout second half 2013
• New Engine – Adaptive Indication/Population Finding (AIPF), used for Enrichment designs
• Enhancements to all existing dose finding engines
• “Under the hood” enhancements in speed, random number generation, baseline adjustments, explicit blocking.
• Better flexibility for future modification to respond to client needs.
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AIPF
• For enrichment – given a set of populations, where is the treatment effective? – Adaptations on Groups
• AIPF has an option of hierarchical borrowing of information to make better decisions (borrows most when appropriate, less when not).
• Much current interest in this design within oncology – part (not all) of I-SPY 2
• Discussed in previous webinars, currently available.
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Continuous Dose Finding
• Baseline covariate simulation available
– can be used as arbitrary covariate
– customizable simulation of subject baseline
– can be used in longitudinal model simulation
– dropout imputation (new in general to FACTS 3) allows BOCF
– can include baseline in dose response modeling.
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BOCF available (as is LOCF) for dropouts
Can adjust dose response for baseline
Baseline simulation in Virtual subjects, including longitudinal modeling
Baseline Features
Dichotomous Dose Finding
• Dichotomous models now available which dichotomize continuous longitudinal data
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Dichotomous Dose Finding
• Absorbing Markov Chain model
– Subject can be “failure”, “stable”, or “response” at interim visits, with failure or response absorbing states.
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Time to Event Dose Finding
• Allows trial to stop based on number of events.
• Cox Model now available (utilizes partial likelihood in computing posterior distributions)
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Time to Event Dose Finding
• Allows incorporation of an early predictor (continuous, dichotomous, or time to event)
– Simulation of virtual subjects with predictor
– Predictor used to impute final endpoint values
– Can adaptively stop based on predictor information
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Multiple kinds of predictors Virtual subject customization Modeling of relationship between predictor and endpoint
Predictor Options
Multiple Endpoint Dose Finding
• All features in Continuous/Dichotomous available in Multiple Endpoint
• Allows utility functions and analysis of up to 4 endpoints.
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Also…
• Cohort enrollment
• Explicit treatment of superiority/noninferiority.
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Better control of followup and post-accrual interims
More arm dropping options
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More control over early stopping criteria (minimum enrollments, minimum events for TTE, minimum subjects on best dose)
Support for analysing results
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Aggregate results from selected scenarios
Open R with individual scenario or aggregated results loaded into data frames
Improved graphing
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More selection of what is displayed Control of y-axis range Graphs of frequentist analyses Graphs to explore evaluation and early stopping criteria
P(outcome) contours to help select thresholds
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Improved display of results
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Many, many results summarised and available, Now grouped logically
Improved …
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Tutorials Examples User Guides Links to webinars and recorded training