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Herding Ponies: How big data methods facilitate collaborative analytics.

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Presentation on theme: "Herding Ponies: How big data methods facilitate collaborative analytics."— Presentation transcript:

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2 Herding Ponies: How big data methods facilitate collaborative analytics

3 Changes in Outcomes Research  New monikers…  Patient Centered Outcomes Research  Health Services Research  Comparative Effectiveness Research  Safety and Surveillance  Changes in funding agencies  PCORI - AHRQ  FDA – CMS  NIH  Changes in research models  More multi-site studies  Larger “center-based” studies  Greater interest in Patient Generated Data  Greater interest in EHR-based data  Less interest in claims

4 Collaboration Frameworks From other disciplines  Open Science Grid Physics, nanotechnolgy, structural biology  OSG: 1.4M CPU-hours/day, >90 sites, >3000 users,  >260 pubs in 2010  LIGO Physics/Astrophysics  Established practices and metadata standards  1 PB data in last science run, distributed worldwide  ESGF  1.2 PB climate data  delivered to 23,000 users; 600+ pubs  Collage – Executable papers Computer science

5 “Why hasn’t Outcomes Research adopted collaborative methods used in physics, climate science, and genomics?” - Everyone in data-driven research - Everyone in data-driven research

6 1.Healthcare data are not collected for research  Not standardized  Not complete 2.Privacy protection has legal and ethical implications 3.Data is an asset 4.Data sharing is not incentivized supported by journals, funding agencies, or the business of healthcare  Obtaining consent is expensive  Data hoarding is rewarded and conservative Adapting to Collaborative Science

7 Are Federated Research Networks the solution? In federated models data are not centralized. AHRQ and PCORI have invested heavily this approach. 5.Each data holder independently assumes responsibility for “data wrangling” and standardization 6.Requires distributed analysis as opposed to traditional central data pooling and analysis.  If data are simply used to independently estimate one model per site, value-added for causal inference is similar to a meta-analysis 7.Requires greater levels of coordination of governance, standards, software, and policies. 8.High barriers to entry – what is the ROI?

8 Federated Meta-Analysis vs. Distributed Analysis Meta-analysis 1 Independently estimated model for each node in the network Not iterative Distributed Analysis One jointly estimated model using data from all sites Typically iterative Leverages computational power of the entire network

9 What does this have to do with “big data?”

10 Two (of 8) barriers to collaborative data science solved with “Big Data” methods  Privacy protection has legal and ethical implications  If data are simply used to independently estimate one model per site, value-added for causal inference is similar to a meta-analysis  Bonus – specialized software or hardware like SAS and CMS repositories can be replaced with parallelized systems

11 Parallel Evolution of Distributed Computing and Federated Research Networks

12 “Big Data” Analytics vs. Outcomes Research Analytics “Big Data” in Distributed Environments Outcomes Research in Federated Research Networks Analysis QuestionsPatterns Predictions Classification Causal Inference Predictions Hypothesis testing Data DistributionData can be randomly distributed across processors by a master Data are non-randomly anchored to sites # Nodes on network100s or more10s Data Governance constraints between network nodes Typically none or lowTypically very high Data set sizeVery largeRelatively small Query Distribution PlatformsApache Spark Hadoop Map-Reduce Apache Pig SHRINE PopMedNet TRIAD Common Analytic PlatformsR-Volution/R-Hadoop Apache Mahout Spark Machine Learning Lib Spark Graph X Lib R SAS Stata Size of developer community1000sDozens

13 “Big-Data” Methods are Incidentally “Privacy Preserving” FeatureClinical Research Rationale “Big Data” Rationale Federation in the form of multiple networked nodes or processing cores Multiple independently operating data partners Inefficient to rely on a single very powerful processor or specialized hardware Distributed computation across networked nodes (instead of central pooling of data) Transferring patient-level data incurs re-identification risks Inefficient to transfer large data sets across the network

14 Distributed Computing Frameworks  Grid Computing Architectures  Statistical Query Oracle Mostly an academic effort  Hadoop From Google  Hundreds of developers  591 Active projects and organizations  Apache Spark Berkeley Computer Science answer to Hadoop  Most rapidly growing user base  99 Active projects and organizations

15 Collaboration Frameworks In Outcomes Research  SHRINE for I2B2  PopMedNet – for MiniSentinel, PCORnet  TRIAD for CAGrid, SAFTINet DRN

16 What distributed methods in the standard biostats toolbox are already supported in “Big Data” vs. Clinical Frameworks? Algorithm/MethodApache Spark LibrariesMap-Reduce Multi- Core or RHadoop Federated Clinical Research Networks Linear regression (weighted)XX Logistic regressionXXX Cox Proportional HazardX Generalized Linear ModelsX Naïve BayesXX Gaussian Discriminative AnalysisX K-meansXX Neural Network BackpropagationX Matrix FactorizationX PCA*X ICA*X Support Vector MachineXXX Expectation MaximizationX Random Forest ClassifierX

17 No Longer a Technical Challenge We have the tools we need to overcome privacy and liability concerns. Now we “only” need to change culture.

18 Moving Collaborative Outcomes Science Forward  Policies (aka incentives)  Payer-driven incentives for better data hygiene and standardization  Payer incentives for sharing  Funding agency incentives for collaborative data management vs. data hoarding  Journal incentives  HIPAA Clarification  Infrastructure  As a community - adopt existing easy-to-use, flexible platforms for sharing code and data  Link clinical data and patient device infrastructure to research infrastructure  Culture  Clinician demand  Patient demand  Tenure and promotion transformation  Replace “not invented here syndrome” with collective credit and shared efficiencies


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