Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study - Wikidata - awgldk - TriplyDB

Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study

Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study

http://www.wikidata.org/entity/Q36518198

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Single-arm trial of the second version of an acceptance & commitment therapy smartphone application for smoking cessation.Methods for constructing and assessing propensity scores Model Misspecification When Excluding Instrumental Variables From PS Models in Settings Where Instruments Modify the Effects of Covariates on Treatment Preoperative statin administration does not protect against early postoperative acute respiratory distress syndrome: a retrospective cohort study.Comparative Safety and Effectiveness of Denosumab Versus Zoledronic Acid in Patients With Osteoporosis: A Cohort Study.Increased Computed Tomography Utilization in the Emergency Department and Its Association with Hospital Admission.Long-term benefits of inhaled corticosteroids in asthma: the propensity score method.Influence on quality of life from an early cinacalcet prescription for secondary hyperparathyroidism in dialysis.Propensity Scores in Pharmacoepidemiology: Beyond the Horizon.Chasing balance and other recommendations for improving nonparametric propensity score models.

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P2860

Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study

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2012 nî lūn-bûn

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Pharmacoepidemiology and Drug Safety

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Variable selection for propens ...... e outcomes: a simulation study

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Alan M Brookhart

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Cynthia J Girman

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Richard Wyss

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Robert J LoCasale

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P2860

Marginal structural models and causal inference in epidemiology

Data, design, and background knowledge in etiologic inference.

A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods.

Variable selection for propensity score models

Invited commentary: understanding bias amplification

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

Estimating causal effects from large data sets using propensity scores.

Constructing inverse probability weights for marginal structural models.

Improving automated database studies.

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

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77-85

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scholarly article

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10.1002/PDS.3356

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1

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22

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3540180

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