Effects of adjusting for instrumental variables on bias and precision of effect estimates. - Wikidata - awgldk - TriplyDB

Effects of adjusting for instrumental variables on bias and precision of effect estimates.

Effects of adjusting for instrumental variables on bias and precision of effect estimates.

http://www.wikidata.org/entity/Q35661934

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Disease risk score as a confounder summary method: systematic review and recommendations Recommendations for the Design and Analysis of Treatment Trials for Alcohol Use Disorders Constructing Causal Diagrams for Common Perinatal Outcomes: Benefits, Limitations and Motivating Examples with Maternal Antidepressant Use in Pregnancy Problem drinking as a risk factor for tuberculosis: a propensity score matched analysis of a national survey.Evaluating performance of risk identification methods through a large-scale simulation of observational data.A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems.Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data.Propensity score methods for confounding control in nonexperimental research Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs.Causal inference, probability theory, and graphical insights.Commentary: Balancing automated procedures for confounding control with background knowledge Instrumental variable applications using nursing home prescribing preferences in comparative effectiveness research The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score Reducing Bias Amplification in the Presence of Unmeasured Confounding Through Out-of-Sample Estimation Strategies for the Disease Risk Score.Combining directed acyclic graphs and the change-in-estimate procedure as a novel approach to adjustment-variable selection in epidemiology.Model Misspecification When Excluding Instrumental Variables From PS Models in Settings Where Instruments Modify the Effects of Covariates on Treatment Suicide risk in Veterans Health Administration patients with mental health diagnoses initiating lithium or valproate: a historical prospective cohort study Invited commentary: understanding bias amplification Outcome modelling strategies in epidemiology: traditional methods and basic alternatives.Factors affecting time to maintenance dose in patients initiating warfarin.A simulation study on matched case-control designs in the perspective of causal diagrams.Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies.Role of disease risk scores in comparative effectiveness research with emerging therapies Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study Ambient air pollution and traffic exposures and congenital heart defects in the San Joaquin Valley of California.A tutorial on propensity score estimation for multiple treatments using generalized boosted models On the joint use of propensity and prognostic scores in estimation of the average treatment effect on the treated: a simulation study.A review of covariate selection for non-experimental comparative effectiveness research.Diagnostics for Confounding of Time-varying and Other Joint Exposures.Outcome-adaptive lasso: Variable selection for causal inference.Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context.The Potential of High-Dimensional Propensity Scores in Health Services Research: An Exemplary Study on the Quality of Care for Elective Percutaneous Coronary Interventions.Performance of the High-dimensional Propensity Score in a Nordic Healthcare Model.Risk of serious infections associated with use of immunosuppressive agents in pregnant women with autoimmune inflammatory conditions: cohort study.Outcome-wide Epidemiology.Short-term risk of liver and renal injury in hospitalized patients using micafungin: a multicentre cohort study.Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses.Evaluating possible confounding by prescriber in comparative effectiveness research.Increased Computed Tomography Utilization in the Emergency Department and Its Association with Hospital Admission.The ACCE method: an approach for obtaining quantitative or qualitative estimates of residual confounding that includes unmeasured confounding.

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P2860

Effects of adjusting for instrumental variables on bias and precision of effect estimates.

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Jeremy A Rassen

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Jessica A Myers

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Joshua J Gagne

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Kenneth J Rothman

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Krista F Huybrechts

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Marshall M Joffe

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Robert J Glynn

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P2860

Bayesian propensity score analysis for observational data.

High-dimensional propensity score adjustment in studies of treatment effects using health care claims data

Variable selection for propensity score models

The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration.

Principles for modeling propensity scores in medical research: a systematic literature review.

Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods

Instruments for causal inference: an epidemiologist's dream?

Estimating causal effects from large data sets using propensity scores.

Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

Confounding control in healthcare database research: challenges and potential approaches.

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