Real-world evidence

Data from routine care rather than clinical trials, used to check whether results hold outside the trial population.

Sources: Flatiron/Foundation clinico-genomic database, Tempus, registries, claims. Used for external control arms, label expansions, and detecting under-performance in older or sicker patients than trials enrolled.

Trials: what this kind of term is about · animated schematic, not to scale

Category

Trials

Connected

technologies

4

Cancer registries and population surveillance Digital twins and virtual control arms Oncology EHR and real-world data platforms Systematic drug repurposing

institutions

22

Barbara Ann Karmanos Cancer Institute Cancer Care Alberta (Alberta Health Services)Clinical Data Interchange Standards Consortium FDA Oncology Center of Excellence Gemeinsamer Bundesausschuss / IQWiG Haute Autorité de Santé HL7 International Intergroupe Francophone de Cancérologie Thoracique Intermountain Health Cancer Center ISPOR – The Professional Society for Health Economics and Outcomes Research Kaiser Permanente Division of Research Karolinska University Hospital National Institute for Health and Care Excellence Pharmaceutical Benefits Advisory Committee Rigshospitalet – Copenhagen University Hospital Scottish Medicines Consortium The Ottawa Hospital Cancer Centre / Ottawa Hospital Research Institute UNICANCER University of Hawai'i Cancer Center VCU Massey Comprehensive Cancer Center Vivli Yale University Open Data Access (YODA) Project

terms

7

Cancer health disparities and equity Clinical trial External and synthetic control arms Health technology assessment (HTA), reimbursement and QALYs OMOP common data model and OHDSI Randomised trial Rare cancers

ideas

54

A fast route to the matched drug when it is licensed for another cancer A global federated real-world evidence network at regulatory grade A pre-registered standard for emulating trials with real-world data A public rulebook for when an external or synthetic control arm is acceptable A real-world sequencing analysis within a year of every new approval A registry of every treatment sequence patients actually receive, with outcomes A shared library of pooled control arms to shrink and speed future trials A single oncology trial data trust with mandatory deposit within eighteen months A structured registry for every off-label cancer drug use Accredited trusted research environments with curated cancer tables After approval, a pragmatic trial in the patients the pivotal trial excluded An automated pipeline emulating trials of every common drug against every cancer An open library pairing completed cancer trials with real-world emulations Automatic weekly linkage of cancer registries to deaths, prescriptions and imaging Emulate combination trials from real-world data to triage which ones to run Emulate the trial in real-world data first to decide which trials to run Every patient on an accelerated-approval drug enrolled in a registry until confirmation Good practice standards and inspection for real-world data sources Link bariatric and GLP-1 registries to cancer registries in every country that has both Linked prescribing and outcome data to find drug interactions with cancer therapy Mandatory interval-cancer audit for every blood-based screening test Mandatory real-world reporting for patients excluded from pivotal trials No mCODE, no payment: tie oncology reimbursement to a minimal structured record One international registry, not one per country, for conditional approvals One outcome record for every donated or discounted cancer drug pack Open, benchmarked algorithms for lines of therapy and progression from routine data Outcome-based annuity payments for potentially curative one-time therapies Patient-reported symptoms captured as standard structured data in every clinic Pay for new biomarker tests only while evidence of clinical utility is being collected Pay for residual disease tests only inside a trial or registry Payers cover off-label combinations only inside registry-randomised trials Payers fund cancer drugs conditionally on a registry with a pre-specified analysis Point-of-care randomisation built into the oncology record Pool every immunotherapy trial's biomarker data into one commons Post-marketing safety monitoring stratified by ancestry and sex, with label updates Pre-registration and results reporting for real-world cancer studies Randomise inside the cancer registry: registry-based trials for everyday questions Randomise inside the registry that already follows every patient Real-world dose intensity and toxicity monitoring to revise labelled doses Reassess cancer drug prices at three years using real-world outcomes Record what happens when doctors use cancer drugs off-label Registry-based randomised trials for oncology comparative effectiveness Registry-embedded randomisation of treatment order in routine care Require genetic and target-trial evidence before funding any repurposing phase 3 Require post-approval evidence in patients over 75 and update labels accordingly Send the code to the data: a federated analytics network of cancer centres Simulate eligibility against real-world data before every protocol is locked Standards for external control arms built from federated real-world data Treat resistance like an infectious disease and run national surveillance Universal tumour and germline sequencing at diagnosis feeding a shared learning system Validate real-world progression against central imaging review Validate real-world progression endpoints so pragmatic trials can use them Watch routine care for drug combinations that quietly make cancer treatment worse Wearable activity data as a validated real-world endpoint

collections

1

AACR Project GENIE

people

1

Deborah Watkins Bruner

bottlenecks

5

Data silos No incentive to repurpose cheap drugs Prices and value Trial design, endpoints and cost Weak real-world evidence and registries