In today’s environment, one of the keys to accelerating drug development decisions comes from ready access to historical data. The introduction of the Centralized Service Provision (CSP) enables data across study phases and programmes to be accessed as easily as within the same study, due to the commonality of structure.
Data leads to evidence-based decisions, and it is the resolution, accurate classification and speed of retrieval of data that makes the difference between taking a risk and making a judgement. The judicious use of historical data goes much further than informing optimal calculation of trial size, although this is an important objective in itself; the ability to cast a wider net and draw parallels across studies can lead to a wealth of data to inform design parameters such as selection of study population, optimization of visit frequencies and choice of endpoint.
Imagine a study team working within a CSP environment and holding design discussions for an early efficacy study, pre-proof -of -concept. Perhaps the first advantages the team will notice are the gains from data standardization. The team may wish to assemble a detailed perspective on the possible dose response profile of the target compound, by putting available data in context with previous results from other candidates and the current standard of care. For example, data from previous phase II and III studies ,from different compounds in the same indication, might be assembled to provide background information on the endpoint. Under the CSP approach data from all studies are prepared under similar programming conventions, held in the same format, and accessible under a single system. It is therefore straightforward to tabulate respective inclusion criteria, endpoints, baseline metrics and variance estimates, knowing that units and cross compatibility issues had already been addressed in the centralized database. The formation of a centrally held, multi-way spreadsheet holding study-specific parameters and assumptions could provide a key information resource to the team.
Once an historical data resource is established, the second key advantage of the CSP environment is the potential for visualization. Continuing the example above, a number of visualization displays could be used to examine variability of different endpoints. Simultaneous display of response distributions, cross-classifying week of study by dose, may allow patterns of response to emerge. The team may wish to examine a number of comparison scenarios including placebo and active comparator; a powerful visualisation tool allows flexibility to select subgroups, or vary the graphical architecture to accommodate non-standard data, drill down to individual values to query the variability assumptions of any specific study. Through this process, the team would hope to determine appropriate study decision criteria, based on a realistic expectation of improvement over comparators, and determine the most relevant time point to set these criteria. This would be in addition to visualization on operating characteristic curves, illustrating the probability of trial success across a range of assumptions about the target effect size that would normally be viewed for a given study. The ability to save and share visualizations at any particular scenario, returning to them later in the discussion, allows the team to compare and contrast the findings from previous studies to what might be seen in the trial under design.
Although this illustration focuses on the statistical aspects of efficacy decisions, the advantages of standardized data and integrated visualization can be applied to other design aspects. For example, examining site level performance and operational parameters can assist in optimising drug delivery and clinical data management. Elucidating trends in safety signals across multiple studies can bring important safety aspects into consideration.
Under the CSP model, further options exist for wider team participation in historical data review, allowing members to work in parallel on alternative scenarios and interrogate the data further, for example by presenting data summaries by pre-defined sub-groups
In summary, many of the advantages of CSP stem from two key aspects: data standardization and visualization, and these aspects can enable the full exploitation of historical data to inform the current trial design. CSP has the potential to offer more streamlined team working, relying on the foundation of harmonized data to compare across studies, and fine tune the data interpretation. These features should amount to both cost savings and inform a better chance of trial success.
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