Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.
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Maximum likelihood, profile likelihood, and penalized likelihood: a primerDoubly robust estimation of causal effectsConfounding control in a nonexperimental study of STAR*D data: logistic regression balanced covariates better than boosted CART.Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative ComplicationsA novel approach for prediction of vitamin d status using support vector regression.The role of the c-statistic in variable selection for propensity score models.Weight trimming and propensity score weighting.The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity scoreModel Misspecification When Excluding Instrumental Variables From PS Models in Settings Where Instruments Modify the Effects of Covariates on TreatmentRisk of Ovarian Cancer Relapse score: a prognostic algorithm to predict relapse following treatment for advanced ovarian cancer.Improving propensity score estimators' robustness to model misspecification using super learner.Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation.Head to head comparison of the propensity score and the high-dimensional propensity score matching methods.On the use of propensity scores in case of rare exposure.Propensity Scoring after Multiple Imputation in a Retrospective Study on Adjuvant Radiation Therapy in Lymph-Node Positive Vulvar Cancer.Imputation approaches for potential outcomes in causal inference.Propensity score and proximity matching using random forestEstimating controlled direct effects of restrictivefeeding practices in the 'Early dieting in girls' study.Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research.A tutorial on propensity score estimation for multiple treatments using generalized boosted modelsSuper learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.An empirical comparison of tree-based methods for propensity score estimation.Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matchingPredictive model for 5-year mortality after breast cancer surgery in Taiwan residents.Use of machine learning theory to predict the need for femoral nerve block following ACL repair.The right tool for the job: choosing between covariate balancing and generalized boosted model propensity scores.Using classification tree analysis to generate propensity score weights.Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting.Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.The EffectLiteR Approach for Analyzing Average and Conditional Effects.Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records.A multicenter study of diet quality on birth weight and gestational age in infants of HIV-infected women.Performance of principal scores to estimate the marginal compliers causal effect of an intervention.Effect of dementia on outcomes of elderly patients with hemorrhagic peptic ulcer disease based on a national administrative database.Effects of Plasma Transfusion on Perioperative Bleeding Complications: A Machine Learning ApproachA comparison of two methods of estimating propensity scores after multiple imputation.The Balance Super Learner: A robust adaptation of the Super Learner to improve estimation of the average treatment effect in the treated based on propensity score matching.Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.A doubly robust approach for cost-effectiveness estimation from observational data.Pharmacoepidemiology in the era of real-world evidence.
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Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
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artigo científico
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bilimsel makale
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scientific article published on August 2010
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
Propensity score estimation: n ...... atives to logistic regression.
@en
Propensity score estimation: n ...... atives to logistic regression.
@nl
type
label
Propensity score estimation: n ...... atives to logistic regression.
@en
Propensity score estimation: n ...... atives to logistic regression.
@nl
prefLabel
Propensity score estimation: n ...... atives to logistic regression.
@en
Propensity score estimation: n ...... atives to logistic regression.
@nl
P2860
P1476
Propensity score estimation: n ...... atives to logistic regression.
@en
P2093
Michele Jonsson Funk
P2860
P304
P356
10.1016/J.JCLINEPI.2009.11.020
P577
2010-08-01T00:00:00Z