Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
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Risk of acute myocardial infarction with NSAIDs in real world use: bayesian meta-analysis of individual patient data.Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly.Cautionary tales in the interpretation of observational studies of effects of clinical interventions.Accuracy of claims-based algorithms for epilepsy research: Revealing the unseen performance of claims-based studies.Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st CenturyClinical epidemiology in the era of big data: new opportunities, familiar challenges.Commentary: The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions.Examining Bias in Studies of Statin Treatment and Survival in Patients With Cancer.Real-world research and the role of observational data in the field of gynaecology - a practical review.PHEDRA: using real-world data to analyze treatment patterns and ibrutinib effectiveness in hematological malignancies.Ibrutinib versus previous standard of care: an adjusted comparison in patients with relapsed/refractory chronic lymphocytic leukaemiaThe value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening.Nature as a Trialist?: Deconstructing the Analogy Between Mendelian Randomization and Randomized Trials.Invited Commentary: Causal Inference Across Space and Time-Quixotic Quest, Worthy Goal, or Both?Comparing the Effectiveness of Dynamic Treatment Strategies Using Electronic Health Records: An Application of the Parametric g-Formula to Anemia Management Strategies.Comparison of dynamic monitoring strategies based on CD4 cell counts in virally suppressed, HIV-positive individuals on combination antiretroviral therapy in high-income countries: a prospective, observational study.Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses.Generalizing Study Results: A Potential Outcomes Perspective.Effectiveness of Screening Colonoscopy to Prevent Colorectal Cancer Among Medicare Beneficiaries Aged 70 to 79 Years: A Prospective Observational Study.Methodologic Issues When Estimating Risks in Pharmacoepidemiology.Target trial emulation: teaching epidemiology and beyond.Does water kill? A call for less casual causal inferences.Caution: work in progress : While the methodological "revolution" deserves in-depth study, clinical researchers and senior epidemiologists should not be disenfranchised.Colonoscopy and Risk of Infective Endocarditis in the Elderly.Strategies to Prioritize Clinical Options in Primary Care.Estimating the comparative effectiveness of feeding interventions in the paediatric intensive care unit: a demonstration of longitudinal targeted maximum likelihood estimation.Emulating a target trial of antiretroviral therapy regimens started before conception and risk of adverse birth outcomes.Instrumental Variable Analyses in Pharmacoepidemiology: What Target Trials Do We Emulate?Conditioning on future exposure to define study cohorts can induce bias: the case of low-dose acetylsalicylic acid and risk of major bleeding.Estimating Effects of Dynamic Treatment Strategies in Pharmacoepidemiologic Studies with Time-varying Confounding: A Primer.The effects of prescribing varenicline on two-year health outcomes: an observational cohort study using electronic medical records.Propensity Scores in Pharmacoepidemiology: Beyond the Horizon.Electronic medical records can be used to emulate target trials of sustained treatment strategies.Long-Term Effectiveness of the Live Zoster Vaccine in Preventing Shingles: A Cohort Study.Big data: Some statistical issues.Illustrating Informed Presence Bias in Electronic Health Records Data: How Patient Interactions with a Health System Can Impact Inference.Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making.Diclofenac use and cardiovascular risks: series of nationwide cohort studiesImproving reproducibility by using high-throughput observational studies with empirical calibration
P2860
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P2860
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
@ast
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
@en
type
label
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
@ast
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
@en
prefLabel
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
@ast
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
@en
P2860
P356
P1476
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.
@en
P2093
James M Robins
Miguel A Hernán
P2860
P304
P356
10.1093/AJE/KWV254
P407
P577
2016-03-18T00:00:00Z