AN AGILE APPROACH


to make alterations as the trial proceeds, in response to observations. For example, sample size may be flexible by allowing for “unblinded sample size re-estimation”, meaning that changes can be made easily during the execution of the trial, based on interim results. Another common process is dose escalation, beginning with a low dose in a small population and gradually escalating to higher doses until an adverse event (AE) incident or safety requirement is failed. The aim of this is to identify patterns and always pursue the endpoint. Agile design anticipates and looks out for differential responses in trial data to identify patterns by considering a wide “net” of observations. This net can include comments from site staff, patients, diaries, online and secondary comments. An agile trial can take all this data into account.


Agile execution Agile execution looks out for opportunities where adjustments could improve the process as it progresses. A key application of agility is the identification of sub-components of a larger system – this means picking out important details from a much bigger picture. Clinical trials generate huge amounts of data. Each subject can be characterised by an array of factors, including age and gender. On top of this, more subject data is constantly gathered. Basic vital sign readings can include pulse, blood pressure (BP), weight and height, and a simple blood chemistry panel can give 12 measurements. The trial may study 10 symptoms, graded from mild to severe, which are measured in a series of assessments over the course of subject screening, enrolment, interim visits, end-of-therapy and post-therapy stages of the trial. More relevant data includes dose levels, enrolment profile, site information, predisposition and more. In short, the amount of data is staggering, and we still can’t know whether we’ve understood all the important parameters.


In a clinical trial, a subset of this data is tested to see if there are any useful correlations between any subject characteristic and an outcome parameter. The important outcome parameters of a study are the frequency or severity of any AEs and the favourable value of the endpoint – that is, the effects, positive and negative, on the subject. These factors are usually correlated with dose and sometimes with therapeutic protocol parameters, such as duration.


Agile trial execution allows us to identify whether some other parameter correlates with an outcome parameter. This parameter could be something as specific as blood potassium levels. If this parameter correlates with an outcome parameter, this may warrant further study. Identifying this kind of statistically differentiable outcome requires analysing the process as it is executed. For example, perhaps favourable outcomes seem to be more strongly correlated with creatinine clearance values, especially in 20- to 40-year-old males. When this is 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 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 asses 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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