This article was originally published as part of a series by pharmaphorum in association with Cytel and is reproduced with their permission.
Ever felt like you ended up somewhere unexpected, not quite knowing what path you took to get there? This is the situation pharma companies can face without the right input from statisticians on clinical trial design – and the results can be very costly. As part of a series of articles in association with Cytel, pharmaphorum spoke with Natasa Rajicic about the vital role statisticians play in improving trial design and preventing expensive mistakes and trial failures.
Everyone would like to think that the clinical trial process is perfect, and that it protects us all from drugs that are dangerous, or don’t work, or a combination of both of these problems. But history has shown that although most of the time bodies such as the FDA, working closely with pharma companies, do get things right when it comes to regulation, sometimes things go awry.
And there is nothing a pharma or biotech CEO hates more than having to re-run an expensive phase 3 trial knowing that the uncertainties could have been resolved if the development process had been better designed in the first place.
According to Cytel’s Natasa Rajicic, the input of a statistician with broad experiences in different quantitative strategies in the trial design process can help to mitigate these kinds of problems, particularly when trials need to be changed or amended midway.
“Smaller development teams often assume they will know the best time to reach out for a certain specialty, but that doesn’t always work. Including the statistician early in the design can help de-risk the trial and the programme from the beginning,” said Rajicic.
It may seem like common sense to involve statisticians throughout given the complex nature of clinical trials and the specialist input needed to design and run them. Indeed, big pharma companies may well have in-house statisticians to call on – however this may not be readily available to smaller biotechs who need to outsource this function due to limited resources.
But not having a statistician close by at all times may well turn out to be a bad move if the trial hits problems or needs to be tweaked, according to Rajicic. Non-statistical functions do not necessarily know when a statistician’s input is needed, so not having a statistician as a core member of the team can prevent the team from having a valuable and timely strategic input.
“Smaller development teams often assume they will know the best time to reach out for a certain specialty, but that doesn’t always work.”
Up and running
Rajicic points out that statisticians play a vital role helping to get trials in shape in the early stages.
They can help not just the individual trials, but the entire development programmes. A statistician given a broad development view can synthesise the existing information to better inform the planning of future trials, help plan for the right data to be available at each step of the development path and beyond, as well as forecast the optimal timing of various decision time points. They can also look at existing data and feed that into the design of the trial or identify potential for the use of innovative features such as a historical control group design. Historical control groups match the response seen in a treatment arm against data collected in previous standard of care trials, which can save time and money recruiting patients, and reduce the number of people exposed to a potentially inferior treatment. Conversely when a statistician is brought in too late, it’s harder for them to make changes to the design and provide this kind of expertise.
“A lot of times they’re brought in with the design already in mind and they just have to look at the design that’s in front of them.”At that point, the statistician is often reduced to only providing input on the sample size and the analysis plan.
As trials continue, they can progress as planned, but may throw up unforeseen results and outcomes, and statisticians come into their own in all these situations. If events look like they unfolded as expected the statistician can help with the leg work involved by helping implement the statistical analysis plan.
But a situation may arise where a trial has to be adapted – and the statistician can be on hand
to ensure that the results of the trial will still have integrity once its parameters have been tweaked
While scrimping on statisticians’ input can lead to increased costs, Rajicic argues that investing in them can lead to significant cost-savings by allowing for adaptive trial designs.
She cited the example of the TAPPAS trial in advanced angiosarcoma, which was designed for greater power, smaller trial size and shorter duration of treatment compared with conventional trial design.
TAPPAS achieved this using the ‘adaptive enrichment’ trial methodology, which allows trial sponsors to assess the performance of drugs in two different sub-populations before deciding whether to enrol more patients into one, or both, of the arms.
It effectively allows sponsors to hedge their bets before deciding how to get optimal results from the larger, more expensive patient group.
The statisticians’ input is vital in these circumstances, said Rajicic. “(It’s about) having steps in place to preserve the integrity of the trial, given that you’re adaptively modifying things midstream,” she said.
Regulators on side
All of this planning is vital when it comes to getting trial plans approved by regulators, as well as the final stages of the clinical trial process when sponsors are aiming to run data past the agencies.
Having the statistical expertise at the start of the clinical trial process and being able to demonstrate how data integrity will be maintained is vitally important, according to Rajicic.
In cases where adaptive trial models are used the statistician will have drawn up an adaptation plan spelling out how information will be used, and details such as the interim analysis.
Rajicic said: “Having all of that spelled out and planned in advance gives the regulators confidence that you know what you’re getting into, and that you’ve thought it through and have contingency plans.”
It also reduces the chance of messy results that may have been affected by poor decision-making during the clinical trial process. In this situation the trial failure scenario is much more likely, or the dreaded rejection scenario when, due to poor planning, pharma companies have to re-run trials just to reassure regulators about the merits of a drug that is later found to be approvable.
Her message is clear – the journey through clinical trials is as important to regulators as the outcome, and statisticians can provide vital advice and insight throughout to keep things on track.
“In this unknown space of possibilities, you don’t know which path you actually ended up on because you didn’t lay it out from beginning to the end,” Rajicic warned.
With so much at stake, pharma and biotech companies should think carefully about how they plan to use a whole range of quantitative strategists, including the statisticians, not only in the development process, but also in the post-approval space.
Statisticians will always be involved one way or another – but not involving the statisticians from the start or keeping them at bay when it comes to overall strategic planning of the drug development process has some considerable risks that may not become apparent until it’s too late. Statisticians play an important role in ensuring trials remain focused, even if they generate unusual or unexpected results.
Those moneys saved by not including an experienced statistician early in the development plan could suddenly feel like something of a false economy
Are you looking for support with your clinical development planning? Get in touch and arrange a discussion with our team.
About the interviewee
Natasa Rajicic is a Principal, Strategic Consulting at Cytel. She has been a practicing biostatistician for over 20 years. At Cytel, she helps clients explore and apply appropriate study designs and address difficult clinical development problems. Her experiences range from studies employing advanced methods to dose escalation to late-stage regulatory interactions on product development issues. Before Cytel, Natasa was a biostatistician at Pfizer in New York where she provided statistical reviews of business opportunities and due diligence evaluations and oversaw statistical input on regulatory interactions related to submissions, label extensions and negotiations, and product safety inquiries.