Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system.
about
Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring systemSignal detection and monitoring based on longitudinal healthcare dataA modular, prospective, semi-automated drug safety monitoring system for use in a distributed data environment.Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies.Commentary: Balancing automated procedures for confounding control with background knowledgeActive safety monitoring of new medical products using electronic healthcare data: selecting alerting rulesAn evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance.Near-real-time monitoring of new drugs: an application comparing prasugrel versus clopidogrel.Improving propensity score estimators' robustness to model misspecification using super learner.Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithmTargeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research.New methods for determining comparative effectiveness in rheumatoid arthritisObservational evidence and strength of evidence domains: case examplesCardiovascular risks associated with low-dose ibuprofen and diclofenac as used OTC.Observational studies of the association between glucose-lowering medications and cardiovascular outcomes: addressing methodological limitations.Addressing limitations in observational studies of the association between glucose-lowering medications and all-cause mortality: a review.DECISION-MAKING ALIGNED WITH RAPID-CYCLE EVALUATION IN HEALTH CARE.The Potential of High-Dimensional Propensity Scores in Health Services Research: An Exemplary Study on the Quality of Care for Elective Percutaneous Coronary Interventions.The past, present and perhaps future of pharmacovigilance: homage to Folke Sjoqvist.Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.Risk of pneumonia in new users of cholinesterase inhibitors for dementiaIncreased Computed Tomography Utilization in the Emergency Department and Its Association with Hospital Admission.High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions.Propensity score methods and unobserved covariate imbalance: comments on "squeezing the balloon".Design considerations in an active medical product safety monitoring system.Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system.Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.The U.S. Food and Drug Administration's Mini-Sentinel program: status and direction.Scalable collaborative targeted learning for high-dimensional data.Study protocol for the dabigatran, apixaban, rivaroxaban, edoxaban, warfarin comparative effectiveness research study.Effect Estimation in Point-Exposure Studies with Binary Outcomes and High-Dimensional Covariate Data - A Comparison of Targeted Maximum Likelihood Estimation and Inverse Probability of Treatment Weighting.Metrics for covariate balance in cohort studies of causal effects.Opportunities and Challenges in Using Epidemiologic Methods to Monitor Drug Safety in the Era of Large Automated Health Databases
P2860
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P2860
Using high-dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年学术文章
@wuu
2012年学术文章
@zh
2012年学术文章
@zh-cn
2012年学术文章
@zh-hans
2012年学术文章
@zh-my
2012年学术文章
@zh-sg
2012年學術文章
@yue
2012年學術文章
@zh-hant
name
Using high-dimensional propens ...... ct safety surveillance system.
@en
Using high-dimensional propens ...... ct safety surveillance system.
@nl
type
label
Using high-dimensional propens ...... ct safety surveillance system.
@en
Using high-dimensional propens ...... ct safety surveillance system.
@nl
prefLabel
Using high-dimensional propens ...... ct safety surveillance system.
@en
Using high-dimensional propens ...... ct safety surveillance system.
@nl
P2860
P356
P1476
Using high-dimensional propens ...... uct safety surveillance system
@en
P2093
Sebastian Schneeweiss
P2860
P356
10.1002/PDS.2328
P478
21 Suppl 1
P577
2012-01-01T00:00:00Z