AN AGILE APPROACH


identified, we can tweak the isolated parameter in order to optimise it – in this case, to see whether a weighting of other parameters correlates with a better target outcome effect. The point is to divide a very large analytical challenge – identifying patterns in the huge amount of clinical trial data – into smaller pieces – individual parameters – to look for causative factors. This kind of closer examination might show that a group with higher stress levels, a recent job change or concomitant medications (con-meds) responds differently in a trial and therefore that one of these factors may have an unforeseen impact on the outcome. Alternatively, studying a female population in narrow age increments might point to menopause as a factor affecting outcomes. Since the menopause happens at different ages and for different durations in different people, this observation might indicate a need to revisit subject profiles


28 | Outsourcing in Clinical Trials Handbook


and add the menopause as a new parameter. Using agile refinement can tease out correlations that suggest opportunities for closer study. The critical, root-cause parameters may be unexpected, meaning that the analysis to identify them as the study goes along is essential.


Benefits of agility


Agility can increase the efficiency of the clinical trial. For example, it can allow potentially useful factors, including secondary factors, to be incorporated into trial design. During the execution of the trial, agile techniques allow trial overseers to follow the target endpoints and better assess the interplay of parameters. This process is set to become only more sophisticated, as technologies like artificial intelligence (AI) and machine learning (ML) can be leveraged to scan data for consistency and correlations – and identify any surprises.


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