The Case for Making All Studies Operationally ‘Adaptive’
By Deb Borfitz
July 20, 2009 | The time- and cost-saving benefits of adaptive clinical trials (ACT) will be lost on companies unless they can generate an uninterrupted flow of information on which adaptations will be based. Getting the best information as fast as possible is mission critical with ACTs because the decisions made reroute the study path and impact final outcomes, says Mike Ford, head of data management at Health Decisions.
Operationally, sponsor companies likewise need to be “adaptive” to optimize how studies actually get run, says Ford. Enabling study teams to make swift and smart choices boils down to four steps:
Adaptive monitoring practices also keep trials running smoothly by distributing CRAs’ workloads according to their individual abilities and capacities. Monitors with the lighter load might double up on site visits within a given geography. Those overburdened with work could get assistance from a co-monitor. But to make these types of adaptations, study teams need a way to visualize the hot spots and dead zones of monitoring activity before they are on site.
Traditional monitoring for a 2.5-year, 100-site study might involve 1,625 monitoring visits, Ford continues. The same study, using adaptive monitoring, could be accomplished with 38% fewer visits. The savings would accrue from orchestrating CRA travel around estimates of the database size (in this example, 1 million fields) and how many fields per day the average CRA can verify (1,000), as well as sending CRAs to sites only when there is sufficient data to fill a full day.
Successful execution of an ACT relies on sites’ ability to conveniently collect data, says Ford, so “alleviating site work load” should be a top concern. Study teams can’t identify preventable errors and take corrective action until data are submitted and analyzed. And that’s an important detail, given that the estimated cost of correcting an error on a typical clinical trial can cost as much as $350 per query. On a study with 1 million data fields, a 5% error rate would generate 50,000 queries. Even a single percentage point drop could result in up to $3.5 million in savings.
Refining trial operations in this way requires a “strong, integrated technology infrastructure capable of processing and interpreting huge amounts of both data and metadata,” says Ford. Health Decisions utilizes EDC technology it calls Smartpen, which seamlessly combines with its trial management system to automatically clean and analyze much of the data. The system then converts that data into relevant reports “designed to validate adaptive decisions about the future course of the trial.”
Health Decisions has only run three studies with adaptive design components, Ford says. But from an operational standpoint, “every trial we implement for sponsors is adaptive.”
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