Real‐World Data and Physiologically‐Based Mechanistic Models for Precision Medicine

2020 ◽  
Vol 107 (4) ◽  
pp. 694-696 ◽  
Author(s):  
Ken Wang ◽  
Neil Parrott ◽  
Andres Olivares‐Morales ◽  
Sherri Dudal ◽  
Thomas Singer ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Real World ◽  
Mechanistic Models ◽  
Real World Data ◽  
World Data ◽  
Physiologically Based

scholarly journals Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

2019 ◽  
Vol 22 (4) ◽  
pp. 439-445 ◽  
Author(s):  
Paul Arora ◽  
Devon Boyne ◽  
Justin J. Slater ◽  
Alind Gupta ◽  
Darren R. Brenner ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Precision Medicine ◽  
Risk Prediction ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals Virtual Trials: Causally-validated treatment effects efficiently learned from an observational brain cancer registry

2021 ◽  
Author(s):  
Asher Wasserman ◽  
Al Musella ◽  
Mark Shapiro ◽  
Jeff Shrager
Keyword(s):  
Randomized Controlled Trials ◽  
Precision Medicine ◽  
Real World ◽  
Brain Cancer ◽  
Treatment Effects ◽  
Information Gain ◽  
Controlled Trials ◽  
Real World Data ◽  
World Data ◽  
Randomized Controlled

Randomized controlled trials (RCTs) offer a clear causal interpretation of treatment effects, but are inefficient in terms of information gain per patient. Moreover, because they are intended to test cohort-level effects, RCTs rarely provide information to support precision medicine, which strives to choose the best treatment for an individual patient. If causal information could be efficiently extracted from widely available real-world data, the rapidity of treatment validation could be increased, and its costs reduced. Moreover, inferences could be made across larger, more diverse patient populations. We created a "virtual trial" by fitting a multilevel Bayesian survival model to treatment and outcome records self-reported by 451 brain cancer patients. The model recovers group-level treatment effects comparable to RCTs representing over 3200 patients. The model additionally discovers the feature-treatment interactions needed to make individual-level predictions for precision medicine. By learning causally-valid information from heterogeneous real-world data, without artificially-limiting the patient population, virtual trials can generate more information from fewer patients, demonstrating their value as a complement to large randomized controlled trials, especially in highly heterogeneous or rare diseases.

The City of Hope POSEIDON enterprise-wide platform for real-world data and evidence in cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18813-e18813
Author(s):  
Samir Courdy ◽  
Mark Hulse ◽  
Sorena Nadaf ◽  
Allen Mao ◽  
Alex Pozhitkov ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Real World ◽  
Genomic Data ◽  
Data Sources ◽  
Genomic Profiling ◽  
Real World Data ◽  
World Data ◽  
Data Elements ◽  
The City ◽  
Disease Specific

e18813 Background: The City of Hope Center for Precision Medicine developed an enterprise-wide platform and precision medicine program to unlock the research potential and clinical value of complex and unique datasets by combining patient data with comprehensive genomic profiling and proprietary analytics. POSEIDON (Precision Oncology Software Environment Interoperable Data Ontologies Network) is a secure, cloud-based Oncology Insights Engine enabling exploration, analysis, visualization, and collaboration on our patient clinico-genomic data along with public data sources. This platform enables investigators to access and visualize data from clinical and multi-omics data and provides an engine that can be utilized for cohort discovery and exploration, preliminary feasibility testing to deriving patient specific insights based on real world data (RWD) and real-world evidence (RWE). Patients are consented through an IRB-approved protocol with active, opt-in participation. Methods: The POSEIDON Common Data Model (PCDM) is a standard, extensible data schema that incorporates patient data to support Precision Medicine. Data are incorporated from disparate data sources and stored in a combined harmonized manner promoting consistency of data and meaning across downstream applications. A multi-step process was created to capture and structure multiple data types into the PCDM. Natural language processing (NLP) tools are deployed to automate and structure valuable data elements from unstructured documents including pathology reports and clinical notes. NLP augmented software tools were developed to assist manual data abstractors to capture more complex terms and disease specific data elements which can include disease progression, progression free survival, and other outcomes. Results: Comprehensive data from 175,000 City of Hope patients are included within this environment for cohort exploration, longitudinal follow-up, outcomes, hypothesis development, and queries for synthetic controls. Data from disease specific-research registries constitute a rich dataset within POSEIDON by disease and tumor type, including lung cancer, colorectal cancer, breast cancer, leukemia, lymphoma and multiple myeloma, among other disease types. Automated genomic workflows were created to gain access to genomic profiling and whole exome sequencing. Genomic data is associated with the clinical data in the PCDM. Automated data flows from the Enterprise Data Warehouse EDW include data that is captured in discrete formats in the EDW and provided for in the PCDM and further enrich the data that flows from the disease registries. Statistically rigorous methods for de-identification are applied for collaborative studies. Conclusions: The City of Hope Center for Precision Medicine and the POSEIDON platform offer an exceptional resource for collaborative RWD & RWE studies.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data
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