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