title: PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms.
abstract: Adaptive, seamless, multisponsor, multitherapy clinical trial designs executed as large scale platforms, could create superior evidence more efficiently than single-sponsor, single-drug trials. These trial PIPELINEs also could diminish barriers to trial participation, increase the representation of real-world populations, and create systematic evidence development for learning throughout a therapeutic life cycle, to continually refine its use. Comparable evidence could arise from multiarm design, shared comparator arms, and standardized endpoints-aiding sponsors in demonstrating the distinct value of their innovative medicines; facilitating providers and patients in selecting the most appropriate treatments; assisting regulators in efficacy and safety determinations; helping payers make coverage and reimbursement decisions; and spurring scientists with translational insights. Reduced trial times and costs could enable more indications, reduced development cycle times, and improved system financial sustainability. Challenges to overcome range from statistical to operational to collaborative governance and data exchange.
authors.affiliation: MIT, Center for Biomedical Innovation, Cambridge, Massachusetts, USA.
authors.forename: M R
authors.initials: MR
authors.lastname: Trusheim
authors.affiliation: MIT, Center for Biomedical Innovation, Cambridge, Massachusetts, USA.|Riptide Management, Cambridge, Massachusetts, USA.
authors.forename: A A
authors.initials: AA
authors.lastname: Shrier
authors.affiliation: Amgen, Thousand Oaks, California, USA.
authors.forename: Z
authors.initials: Z
authors.lastname: Antonijevic
authors.affiliation: Georgetown University Medical Center, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Washington, DC, USA.
authors.forename: R A
authors.initials: RA
authors.lastname: Beckman
authors.affiliation: Brown University, Providence, Rhode Island, USA.
authors.forename: R K
authors.initials: RK
authors.lastname: Campbell
authors.affiliation: Merck & Co., Philadelphia, Pennsylvania, USA.
authors.forename: C
authors.initials: C
authors.lastname: Chen
authors.affiliation: Massachusetts General Hospital Cancer Center, Boston, Massachusetts, USA.
authors.forename: K T
authors.initials: KT
authors.lastname: Flaherty
authors.affiliation: DataForeThought, Winchester, Massachusetts, USA.
authors.forename: J
authors.initials: J
authors.lastname: Loewy
authors.affiliation: European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium.
authors.forename: D
authors.initials: D
authors.lastname: Lacombe
authors.affiliation: Georgetown University Medical Center, Innovation Center for Biomedical Informatics, Washington, DC, USA.
authors.forename: S
authors.initials: S
authors.lastname: Madhavan
authors.affiliation: Tufts Medical Center and Tufts University, Institute for Clinical Research and Health Policy Studies and Tufts Clinical and Translational Science Institute, Boston, Massachusetts, USA.
authors.forename: H P
authors.initials: HP
authors.lastname: Selker
authors.affiliation: University of California San Francisco Medical Center, Carol Franc Buck Breast Care Center, San Francisco, California, USA.
authors.forename: L J
authors.initials: LJ
authors.lastname: Esserman
mesh_terms: D002986:Clinical Trials as Topic / Q000191:economics / Q000379:methods* / Q000458:organization & administration; D003299:Cooperative Behavior; D023381:Endpoint Determination; D006801:Humans; D018579:Patient Selection; D012051:Reimbursement Mechanisms; D012107:Research Design; D013997:Time Factors; D057170:Translational Research, Biomedical / Q000458:organization & administration