Adaptive Trial Design – a programmer’s view
The FDA defines an adaptive design as
…an adaptive design is defined as a clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial.
The most common adaptations of the trial design which impact on programming are:
- Change in the treatment groups
- Change in the randomisation ratio
- Focusing recruitment on patients most likely to benefit
PHASTAR’s extensive experience with CDISC standards, when combined with clear dataset specifications, can be leveraged to facilitate the changes which may be required due to Adaptive Trial Design. This could include additional data being collected and reported, or further treatment groups or regimens being added to the trial design. With standard scalable dataset specifications, it follows that forethought applied to output design and programming will aid the accommodation of changes to the data and/or trial design.
The primary method for reviewing the data during the lifecycle of a trial with an adaptive design may require an interim analysis at one or more scheduled or pre-determined timepoints. The interim analysis will be defined in the Statistical Analysis Plan (SAP) but may have a different focus to the main study analysis in order to determine if the trial needs to be modified in any way. Attention needs to be paid to the quality of the data and the analysis and may require a focus on data of specific interest for the interim analysis, including the primary endpoint, key safety and demographic data, disposition and baseline characteristics.
The main challenge facing a programming team supporting the production of outputs for an adaptive trial design is the risk of being exposed to the unblinded data required for the interim analysis. This is contrary to the standard situation where the data is blinded up to the point that the clinical database is declared locked and ready for analysis. The requirement for an interim analysis to report on unblinded data whilst the main study team remain blinded presents several challenges to efficient and quality-driven programming.
An unblinded team has the following requirements:
- Independent from the main study team
- Can work across multiple studies within a project or sponsor
- Will have a Lead Programmer to manage the work and ensure co-operation with the main study team
- Common code used by the blinded team
- Separate programming areas with restricted access
By utilising appropriate and documented security models at PHASTAR the unblinded team and the blinded team can access the same codebase whilst not being able to access and view any of the data or outputs that the other team produces, retaining the key factor of independence whilst enabling co-operation on the production and update of common code.
Maintaining consistency between the two groups is key. The data and outputs required should, ideally, be aligned to those being produced for the CSR although this is not always the case as there may be additional requirements for the interim analysis outputs. The CDISC standards framework includes SDTM and ADaM standards which provide a clear and easy switch between the blinded and unblinded status (which will also be required when the data is unblinded for final analysis).
With the above framework in place PHASTAR is able to act as both the blinded study team and the (separate) unblinded team working on the interim analysis. This methodology and structure can be utilised when PHASTAR is providing one part of the analysis, whether the main blinded analysis or an unblinded interim analysis; the main point to consider in this situation is whether the code is to be provided to the PHASTAR team and shared with other party.
These principles allow PHASTAR to accommodate projects whether the work is undertaken our own reporting system or using a sponsor’s own reporting environment. They facilitate the delivery of outputs for the interim analysis as well as accommodating any updates to the SAP the review of the interim analysis recommend whilst continuing to support the statistical analysis of the clinical trial data.