Too many covariates and too few cases? – a comparative study
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Qingxia Chen | Frank E Harrell | Hui Nian | F. Harrell | Qingxia Chen | Yuwei Zhu | H. Nian | M. Griffin | H. Talbot | Marie R Griffin | Yuwei Zhu | H Keipp Talbot
[1] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[2] S. Cessie,et al. Ridge Estimators in Logistic Regression , 1992 .
[3] J. Friedman,et al. A Statistical View of Some Chemometrics Regression Tools , 1993 .
[4] Qingxia Chen,et al. Effectiveness of seasonal vaccine in preventing confirmed influenza-associated hospitalizations in community dwelling older adults. , 2011, The Journal of infectious diseases.
[5] S. Lahiri,et al. Bootstrapping Lasso Estimators , 2011 .
[6] Charles E McCulloch,et al. Relaxing the rule of ten events per variable in logistic and Cox regression. , 2007, American journal of epidemiology.
[7] R. Tibshirani,et al. A SIGNIFICANCE TEST FOR THE LASSO. , 2013, Annals of statistics.
[8] Qingxia Chen,et al. Effectiveness of influenza vaccine for preventing laboratory-confirmed influenza hospitalizations in adults, 2011-2012 influenza season. , 2013, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.
[9] Donald Rubin,et al. Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.
[10] R. D'Agostino. Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .
[11] Peter C Austin,et al. A comparison of 12 algorithms for matching on the propensity score , 2013, Statistics in medicine.
[12] Xiao-Hua Zhou,et al. Generalized propensity score for estimating the average treatment effect of multiple treatments , 2012, Statistics in medicine.
[13] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[14] Sebastian Schneeweiss,et al. Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses. , 2015, American journal of epidemiology.
[15] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[16] Peter C Austin,et al. The performance of different propensity score methods for estimating marginal hazard ratios , 2007, Statistics in medicine.
[17] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[18] I-Feng Lin,et al. Shrinkage methods enhanced the accuracy of parameter estimation using Cox models with small number of events. , 2013, Journal of clinical epidemiology.
[19] J. Robins,et al. Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.
[20] J. Concato,et al. A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.
[21] Robert Gray,et al. Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis , 1992 .
[22] Marshall M Joffe,et al. On the estimation and use of propensity scores in case-control and case-cohort studies. , 2007, American journal of epidemiology.
[23] M Soledad Cepeda,et al. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. , 2003, American journal of epidemiology.
[24] J. Goeman. L1 Penalized Estimation in the Cox Proportional Hazards Model , 2009, Biometrical journal. Biometrische Zeitschrift.
[25] Thomas Agoritsas,et al. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. , 2011, Journal of clinical epidemiology.
[26] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[27] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[28] M B Keller,et al. A dynamic adaptation of the propensity score adjustment for effectiveness analyses of ordinal doses of treatment , 2001, Statistics in medicine.
[29] Elizabeth A Stuart,et al. Improving propensity score weighting using machine learning , 2010, Statistics in medicine.
[30] S. Vansteelandt,et al. On regression adjustment for the propensity score , 2014, Statistics in medicine.
[31] Peter C. Austin,et al. The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies , 2009, Medical decision making : an international journal of the Society for Medical Decision Making.
[32] S. Geer,et al. On asymptotically optimal confidence regions and tests for high-dimensional models , 2013, 1303.0518.
[33] R. Tibshirani,et al. On the “degrees of freedom” of the lasso , 2007, 0712.0881.
[34] Peter C Austin,et al. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study , 2007, Statistics in medicine.
[35] A. Mebazaa,et al. Propensity score estimators for the average treatment effect and the average treatment effect on the treated may yield very different estimates , 2016, Statistical methods in medical research.
[36] D. Rubin. Propensity score methods. , 2010, American journal of ophthalmology.
[37] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[38] Peter C Austin,et al. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study , 2007, Statistics in medicine.
[39] D. Rubin,et al. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score , 1985 .
[40] D. Rubin,et al. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .