Quantitative assessment of unobserved confounding is mandatory in nonrandomized intervention studies.
暂无分享,去创建一个
[1] K. Nichol,et al. Effectiveness of influenza vaccine in the community-dwelling elderly. , 2007, The New England journal of medicine.
[2] Jonathan A C Sterne,et al. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. , 2007, American journal of epidemiology.
[3] D. Rubin. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials , 2007, Statistics in medicine.
[4] J. Robins,et al. Instruments for Causal Inference: An Epidemiologist's Dream? , 2006, Epidemiology.
[5] J. Robins,et al. Estimating causal effects from epidemiological data , 2006, Journal of Epidemiology and Community Health.
[6] A. Hoes,et al. Benefits of influenza vaccine in US elderly--appreciating issues of confounding bias and precision. , 2006, International journal of epidemiology.
[7] S. Schneeweiss. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics , 2006, Pharmacoepidemiology and drug safety.
[8] Wiebe R. Pestman,et al. Instrumental Variables: Application and Limitations , 2006, Epidemiology.
[9] V Demicheli,et al. Efficacy and effectiveness of influenza vaccines in elderly people: a systematic review , 2005, The Lancet.
[10] Jay S. Kaufman,et al. Bounding Causal Effects Under Uncontrolled Confounding Using Counterfactuals , 2005, Epidemiology.
[11] S. Schneeweiss,et al. Association Between SSRI Use and Hip Fractures and the Effect of Residual Confounding Bias in Claims Database Studies , 2004, Journal of clinical psychopharmacology.
[12] Sean D Sullivan,et al. Methods to assess intended effects of drug treatment in observational studies are reviewed. , 2004, Journal of clinical epidemiology.
[13] J. Robins,et al. A Structural Approach to Selection Bias , 2004, Epidemiology.
[14] Sander Greenland,et al. Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. , 2004, American journal of epidemiology.
[15] S. Ebrahim,et al. Mendelian randomization: prospects, potentials, and limitations. , 2004, International journal of epidemiology.
[16] A. Hoes,et al. Confounding by indication in non-experimental evaluation of vaccine effectiveness: the example of prevention of influenza complications , 2002, Journal of epidemiology and community health.
[17] J. Robins,et al. Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.
[18] B. Psaty,et al. Assessment and Control for Confounding by Indication in Observational Studies , 1999, Journal of the American Geriatrics Society.
[19] R. Kronmal,et al. Assessing the sensitivity of regression results to unmeasured confounders in observational studies. , 1998, Biometrics.
[20] A. Hoes,et al. Confounding and indication for treatment in evaluation of drug treatment for hypertension , 1997, BMJ.
[21] Donald Rubin,et al. Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.
[22] S Greenland,et al. Basic methods for sensitivity analysis of biases. , 1996, International journal of epidemiology.
[23] B J McNeil,et al. Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. , 1994, JAMA.
[24] W. Flanders,et al. Indirect Assessment of Confounding: Graphic Description and Limits on Effect of Adjusting for Covariates , 1990, Epidemiology.
[25] D. Rubin,et al. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .