PHASTAR, the global CRO offering statistical consulting, clinical trial reporting and data management services to the pharmaceutical and biotech industries, is delighted to be announced as winners of the inaugural award for Innovation in the Management of Clinical Data at this years ACDM Conference.
The annual awards were established in 2019 to further showcase the achievements in the management of clinical data of ACDM members, with winners being announced at the annual ACDM conference, this year taking place in Amsterdam.
Speaking on the win, Kevin Kane, CEO of PHASTAR, stated: “We at PHASTAR are delighted to be winners of this inaugural award in Innovation in the Management of Clinical Data. Our Data Management team’s number one focus is quality of work – we have a 100% success rate with our clients – and an innovative approach on handling data is one of the ways we ensure this. I am exceptionally proud of our data management team”.
The annual European DIA conference took place in Vienna this year. After the opening speech (which involved a flash mob of attendees doing a waltz across the main conference hall!), the conference kicked off with the first key note session of the week. Dave de Bronkart, aka ‘ePatient Dave’ was given 24 weeks to live upon a diagnosis of cancer. Dave described how he became one of the first ‘ePatients’, a term used for patients who use the internet to treat, and sometimes cure, their own conditions.
Things have changed since the times where medical professionals implored their patients not to use the internet to self-diagnose and treat. Dave presented a strong case for the need to engage patients and to open the internet as a tool to be used for patient empowerment. This shift will impact the industry greatly. When we set up clinical trials, we need to consider that patients are now a lot closer to their data than they used to be. When designing data capture systems and devices, we now need to ensure these will be user-friendly and will encourage patient engagement more than ever before. The FDA has even suggested that the degree of subject involvement in a study may be used as a quality indicator.
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.
A key objective of Phase I dose escalation trials is to establish a recommended dose and/or schedule for an experimental drug or combination for further testing in Phase II. They balance avoiding unnecessary exposure of patients to subtherapeutic doses with maintaining safety and rapid enrolment.
Traditional “3+3” designs and their variations are simple to implement, based upon pre-specified rules to guide how to react to accumulating data but lack firm statistical foundation. These types of designs only “memorise” the toxicities in the previous step of dose-escalation and do not take into consideration cumulative toxicities. As a result, a large proportion of patients may receive low, non-effective doses and the estimated maximum tolerated dose (MTD), the highest dose at which a pre-specified proportion of patients experience a dose-limiting toxicity (DLT), may be inaccurate. To overcome the uncertainty about a recommended dose for future studies and slow dose escalation, model-based Bayesian adaptive designs have been developed:
- The target toxicity at the recommended dose for future studies would be explicitly defined by seeking a suitable quantile of the dose-response curve, such as the probability of toxicity at the target dose being 20%;
- The prior guess of the target dose on the pre-assumed dose-response curve before the trial starts would form the starting dose;
- The decision criteria, such as treating future patients at the current estimated MTD or minimising the variance of the dose-response model parameters, would then be used to identify the next dose after the model is updated to take consideration of all available data from the trial.
- A set of safety constraints can be put in place to control the risk of overdosing the patients, for example pre-specifying a maximum dose increase.