Logistics
In focusing only on attrition rates in phase 3, Mahlich et al’s projection may actually understate the advantages of adaptive trials. The FDA has argued that adaptive designs can yield better estimates of dose- response relationships – particularly in phase 2. Equally, Stephen Chick, Novartis chaired professor of healthcare management at INSEAD, notes that adaptive multi-arm trials can optimise how patients are allocated, and enable investigators to drop clearly inferior treatments and better focus their resources. “It’s possible to more efficiently learn when the goal is to identify the best alternative, as opposed to estimating the expected benefit of every alternative,” he explains. “So, if some alternatives are clearly inferior, based on preliminary data, you’re going to sample those less.”
For example, Mahlich and his co-authors point to the 2013 STAMPEDE trial, which simultaneously evaluated multiple treatments for prostate cancer compared with a control group, enabling the termination of treatment arms that did not outperform the common comparator. As they gather evidence, such trials can also focus on patient subpopulations that show a larger treatment effect than they do on the general populace. Adaptive thinking can even save money for sponsors who continue to focus on traditional trial designs. As Chick explains, it’s possible to use Bayesian statistical methods to adaptively compute results in the background of a standard RCT. “If it looks like you’re headed towards a result that would say, ‘Let’s drop the trial due to futility, it’s not an effective trial,’ you can stop the trial early and save a lot of money,” he says.
Adapt and learn
Chick is a member of the management team for the UK-led EcoNomics of Adaptive Clinical Trials (ENACT) project, which is exploring the potential of a new type of “value-adaptive” protocol that incorporates cost- effectiveness considerations into the design and management of a trial – putting logistics at the core of healthcare: “This is applying process-thinking to the innovation pipeline,” he says. “It’s looking at the logistics of new technology flows and asking how we learn to get the best technology flows in place.” In short, value-adaptive designs allow for changes to be made during a trial based on the assessment of its value for money and health. They can, for instance, enable investigators to determine whether the additional costs of continuing a trial are worth its potential benefits – incorporating relevant parameters such as trial expenses, monetary impacts and disease prevalence, which are difficult to build into a traditional trial. In a retrospective analysis of the ProFHER trial comparing surgical and non-surgical interventions for proximal humerus fractures, the ENACT team found that it could have sampled between 35% and 42% fewer
Clinical Trials Insight /
www.worldpharmaceuticals.net
patients and stopped early with the same finding, resulting in a 15% saving. Other retrospective studies showed more modest savings, and even some increases in trial length and sample sizes, but all ranked higher in expected value to the healthcare system. “People are saying we have to be more efficient about trials,” says Chick, “but more efficient doesn’t just mean fewer patients for a given budget. It also means adapting the learning so that you get a better treatment for all the patients that are going to be following the trial. It’s looking at the net health benefit for patients after the technology adoption decision, [minus] the cost of the trial itself.”
Value-adaptive designs allow for changes during a trial, based on its value for money and health.
“It’s possible to more efficiently learn when the goal is to identify the best alternative, as opposed to estimating the expected benefit of every alternative. So, if some alternatives are clearly inferior, based on preliminary data, you’re going to sample those less.”
Stephen Chick
Although value-adaptive frameworks are best suited to trials sponsored by healthcare systems, where incentives are very closely aligned with the concerns of the bodies making health technology adoption decisions, the ability to optimise trial design to create value – and to better tie investment to output – would also be extremely beneficial for pharmaceutical companies. As much as anything else, studies that do so may prove instrumental in helping the industry ensure it actually has a market that can afford its products in the decades to come. “It’s thinking about trials that inform health technology adoption decisions not as cost centres, but as value centres,” stresses Chick. A chance to reframe clinical logistics not as spending money – which any computer could do – but as creating tangible health benefits. ●
$2.56bn
The cost to develop a new drug therapy.
Journal of Health Economics 25
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