Glossary

Overview

This page provides definitions of terms, acronyms, and abbreviations that are commonly used across FACTS documentation.

Table 1

Name	Definition
3+3	3+3 The conventional 3-person cohort phase 1 design for Oncology trials with fixed dose escalation and de-escalation rules based only on the toxicities observed in the last cohort.
Across Groups	A key feature of the Enrichment Design is the inclusion of analysis both within individual groups and across all the groups, and the ability to make decisions using both analyses. The across groups analysis is more than just pooling all the data and should be fully understood if it is to be used correctly.
Active Comparator	A treatment arm where subjects are treated with some existing therapy which the user either wishes to compare the novel treatment to (in addition to control), or wishes to use to check that the study has been properly designed to show a difference from control.
Arm Selection	In a Staged Design, there are a number of ways the user can specify which of the available arms are used in the second stage. The selection of the arms takes place at or after the specified time after the analysis at which the first stage decides to “graduate” to the second stage, and before the second stage starts.
Baseline	The subject’s baseline is their endpoint value before treatment on the trial is started. In FACTS, the subject’s baseline is measured at their first visit at follow-up time 0, any prior visits (e.g. for screening to see if the patient is eligible for the trial) are not included in the simulation.
bCRM	A “bi-variate” form of the CRM that analyses both an efficacy and a toxicity endpoint.
Cap	A limit on the number of subjects recruited. In FACTS N-CRM users can specify a cap on the overall number of subjects to be recruited in the trial (the ‘Overall Cap’) and specify stopping rules to define when the trial should stop before it reaches cap.
Control	Is the treatment arm with which the novel treatment(s) are principally being compared. Control may be placebo, or some existing standard of care, or therapy, against which the novel treatment has to be benchmarked in order to determine its likely usefulness.
Core	FACTS Core: A mode of FACTS for designing trials where multiple treatments, (possibly different doses of a novel treatment) are tested against a control and optionally an active comparator.
CRM	Continual Reassessment Method – a dose escalation design where the dose-toxicity is estimated using a simple Bayesian model, and the resulting estimates used to control the dose escalation and estimate the Maximum Tolerated Dose (MTD).
CSD	Clinically Significant Difference
CSHRD	Clinically Significant Hazard-Ratio Difference
DE	Dose Escalation: a mode of FACTS where subjects are treated in cohorts and dose escalation is determined by the number of toxicities observed.
Dichotomized endpoint	A dichotomous endpoint is created by measuring a continuous endpoint and scoring a subject as a responder if their score is above or below a specified threshold. FACTS allows the underlying continuous endpoint to be modeled longitudinally in order to provide a better prediction of whether a subject will be a responder at their final visit, or not.
Dose Response Model	A model used in the statistical analysis of the final response as a function of the treatment dose strength. FACTS includes both parametric and non-parametric models, including ‘no model’.
ED	Enrichment Designs: a mode of FACTS for designing trials where the same treatment is testing in different settings for example different sub-populations or different but related indications.
Endpoint	An endpoint is a measure of the subject’s health that is being analyzed in order to learn about the effects of the treatments being studied in the trial.
FACTS	Fixed and Adaptive Clinical Trial Simulator.
Final Endpoint	The value, or state, of a subject’s endpoint at the last visit in the follow-up schedule.
Graduation	This is the analysis at which the first stage decides to stop, having decided that the second stage should be run. At an early interim Stage 1 can decide to stop the whole trial for success or futility, or to graduate to the second stage: this is referred to as Early Graduation. If Stage 1 reaches its final analysis, either because all the subjects in the first stage have completed follow-up, or a Stage 1 guillotine – a date when Stage 2 must start - has been reached, then at this final analysis it can again be decided to stop the whole trial for success or futility; otherwise the trial graduates to the second stage: this is referred to as Late Graduation.
Group	The very neutral term ‘groups’ is used in ED, because the ED design could be used in a number of different settings. The groups could be for instance different patient sub-populations with the same disease, different disease sub-populations such as different sarcoma types, or even different but related diseases that the same treatment might be effective against (migraine, dental pain and post-operative pain for instance). The key characteristic is that there is a need to study each independently (with their own control) but also look across the different groups for some purpose – this might be for analysis where there is an expectation of commonality in response on control, or in the difference from control on the treatment arms; or operationally where there is limited budget, drug supply or time across the groups; or for the purposes of decision making – where the treatment has to be successful in more than one group to be worth considering for further development.
GUI	Graphical User Interface, the visual part of the FACTS application that the user interacts with.
Historical Control	A ‘historic control’ arm is used when no control arm is randomized to in the study, and the response on the arms where the novel treatment administered are compared to combined data from control arms from other already complete studies.
Imputation	When the Bayesian statistical models are fitted to the simulated data within FACTS, if longitudinal modeling has been included, then multiple imputation is used for missing subject final endpoint values, whether missing due to the subject having dropped out or their final visit simply not occurred yet. The value is separately sampled at each iteration of the MCMC, from the posterior distribution of the longitudinal model, given the subject’s interim endpoint values.
Information	The timing of the first interim timing is in terms of when a specified amount of information has been collected – this can be number of subjects enrolled, number of subjects complete (up to a specified visits) or subjects who could have completed (up to a specified visit) – i.e. it includes those who would have completed if they hadn’t dropped out. Subsequent interims can be defined in terms of time (e.g. every 4 weeks) or information.
Interim Visit	A visit between the baseline visit and final visit, at which a subject’s endpoints are measured.
Intermediate Endpoint	The value of a subject’s endpoint at a visit after their baseline visit, but before their final visit. Intermediate endpoint values can be used to estimate, using a longitudinal model, what the subject’s final endpoint will be (or would have been) thus providing additional information for analyses. Multiple imputation is used ensure that these estimated responses are included in the analysis with all due uncertainty. Sometimes called an early endpoint.
Longitudinal Model	An analysis model that models the relationship between a subject’s interim endpoint values and their final endpoint value. In FACTS, all longitudinal models are simply multiple imputation models that can be used to impute a subjects final endpoint value when it is not available.
Method	In the FACTS documentation we try to reserve the term ‘method’ for the algorithms used in the simulation (as opposed to the analysis) part of the program. In the analysis part we use the term ‘Model’, see below.
Model	In the FACTS documentation we try to reserve the term ‘model’ for the statistical models used in the analysis of the trial data (in the ‘design’ section of the FACTS user interface). Where mathematical algorithms are used for other purposes in FACTS (for instance in the generation of the simulated data) we try to use the term ‘method’. It is common for users to confuse the data generation and the trial implementation components of FACTS, and using distinct terminology may help to reduce this.
MTD	The dose most likely to be the Maximum Tolerated Dose (MTD) – the dose with the highest Pr(MTD).
MTD+	The dose most likely to be the MTD+ – the dose with the highest Pr(MTD+).
Multiple Imputation	When the Bayesian statistical models are fit to simulated data within FACTS, if longitudinal modeling has been included, then multiple imputation is used for missing subject final endpoint values. This includes subjects whose final endpoint data are missing due to the subject having dropped out and subjects who have not yet had enough follow-up time to observe their final visit response yet. Imputed final endpoint values are separately sampled at each iteration of the MCMC from the posterior distribution of the longitudinal model, given the subject’s interim endpoint values.
NIM	Non-Inferiority Margin
NIHRM	Non-Inferiority Hazard-Rate Margin
OSD	For those designs with both a toxicity and efficacy endpoint the OSD is the Optimum Selected Dose, this will be the MED if the MED is below the MTD, otherwise it will be the MTD.
OSD+	For those designs with both a toxicity and efficacy endpoint the OSD+ is the Optimum Selected Dose, this will be the MED+ if the MED+ is below the MTD+, otherwise it will be the MTD+.
Pr(MTD)	A dose’s probability of being MTD is the probability that it is the dose with the highest probability of having a toxicity rate in the acceptable toxicity band, and (if a threshold has been specified) does not have a probability of excess or unacceptable toxicity above the threshold. This estimate of MTD is constrained to select one of the available doses.
Pr(MTD+)	A dose’s probability of being the MTD+ is the probability that it is the dose with the highest probability of having a toxicity rate in the acceptable band, and (if a threshold has been specified) does not have a probability of excess or unacceptable toxicity above the threshold. Unlike Pr(MTD), Pr(MTD+) includes estimating whether a dose below or a dose above the range of those being tested is more likely to have a toxicity in the acceptable band than any of the doses in the range.
Profile	A profile is a specification of one aspect of a scenario. Examples of these aspects are Accrual Rate, VSR, Dropout Rate, and others. A scenario is made up of one profile of each of the required types for the type of trial being simulated. FACTS allows the user to specify multiple profiles of each type and then presents all the possible combinations of profiles as scenarios that can be used to drive simulations.
QOI	Quantity Of Interest - A value to be calculated because it is of interest to the proceeding of the simulated trials. Quantities may be of interest because they are to be used in an interim decision, to adapt allocation, for final evaluation, or to ensure it is value is written out in the FACTS results files for analysis outside of FACTS.
Response	A synonym of endpoint value. May be the value of a subjects final endpoint, or a change in a subject’s endpoint compared to their baseline state.
Restricted Markov	A form of the Dichotomous endpoint where, rather than the subject’s endpoint being either 0 or 1 and able to switch between them from visit to visit, their endpoint value is ‘stable’ until it becomes either ‘response’ or ‘failure’. Once their state has become ‘response’ or ‘failure’ it can then not change. For example ‘failure’ could be “subject has resumed smoking” in a smoking cessation trial, or death in an oncology trial.
Scenario	A scenario is the complete specification of the unknown external factors that determine the data observed on the trial and its timing. The exact factors depend on the type of trial being simulated, but typically include: the distribution of the final change from base line, or probability of response or rate of events in the different treatment groups the properties of subjects’ early responses and the correlation with their final outcome the rate at which subjects are recruited into the trial the rate at which subjects drop out of the trial.
SPEC	The Design Engine Specification document, describes the system algorithms, and meaning of parameters in a more technical context. SPEC documents have been deprecated as of FACTS 7.1.1
Subject	An entity recruited onto a clinical trial for the purposes of learning about the properties of a treatment. Depending on the type of trial they might be patients or they might be healthy volunteers.
Treatment Arm	An arm being studied in a clinical trial. A treatment arm may refer to different doses of the same treatment or completely separate therapies. Subjects, upon entering a study, are randomized to a treatment arm.
UG	The User Guide document - describes in detail how to use a FACTS engine.