Using multiple imputation to classify potential outcomes subgroups
暂无分享,去创建一个
Michael R. Elliott | Yun Li | Irina Bondarenko | Timothy P. Hofer | Jeremy M.G. Taylor | M. Elliott | T. Hofer | Jeremy M. G. Taylor | Yun Li | I. Bondarenko
[1] D. Rubin,et al. Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse , 1986 .
[2] John Van Hoewyk,et al. A multivariate technique for multiply imputing missing values using a sequence of regression models , 2001 .
[3] A. Kurian,et al. Chemotherapy decisions and patient experience with the recurrence score assay for early‐stage breast cancer , 2017, Cancer.
[4] R. Schilsky,et al. American Society of Clinical Oncology 2013 top five list in oncology. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[5] C. Gross,et al. Trends and clinical implications of preoperative breast MRI in Medicare beneficiaries with breast cancer , 2013, Breast Cancer Research and Treatment.
[6] Xihong Lin,et al. Estimating causal effects in trials involving multitreatment arms subject to non‐compliance: a Bayesian framework , 2010, Journal of the Royal Statistical Society. Series C, Applied statistics.
[7] T. Raghunathan,et al. Multiple Imputation for Causal Inference , 2010 .
[8] D. Rubin,et al. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction , 2016 .
[9] Susan Halabi,et al. American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine , 2016, CA: a cancer journal for clinicians.
[10] Virginia G Kaklamani,et al. Adjuvant Chemotherapy Guided by a 21‐Gene Expression Assay in Breast Cancer , 2018, The New England journal of medicine.
[11] Jack Cuzick,et al. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[12] Donald B. Rubin,et al. Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Death” , 2003 .
[13] R. Little,et al. Penalized Spline of Propensity Methods for Treatment Comparison , 2019, Journal of the American Statistical Association.
[14] Michael R Elliott,et al. A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials , 2010, Biometrics.
[15] R. Jagsi,et al. The influence of 21-gene recurrence score assay on chemotherapy use in a population-based sample of breast cancer patients , 2017, Breast Cancer Research and Treatment.
[16] Zhiwei Zhang,et al. Assessing the heterogeneity of treatment effects via potential outcomes of individual patients. , 2013, Journal of the Royal Statistical Society. Series C, Applied statistics.
[17] D. Horvitz,et al. A Generalization of Sampling Without Replacement from a Finite Universe , 1952 .
[18] Timothy L Lash,et al. A most stubborn bias: no adjustment method fully resolves confounding by indication in observational studies. , 2010, Journal of clinical epidemiology.
[19] T. VanderWeele. Controlled Direct and Mediated Effects: Definition, Identification and Bounds , 2010, Scandinavian journal of statistics, theory and applications.
[20] J. Robins. The control of confounding by intermediate variables. , 1989, Statistics in medicine.
[21] M R Elliott,et al. Surrogacy assessment using principal stratification and a Gaussian copula model , 2017, Statistical methods in medical research.
[22] Joseph W Hogan,et al. Bayesian Inference for the Causal Effect of Mediation , 2012, Biometrics.
[23] P. Holland. Statistics and Causal Inference , 1985 .
[24] Donald B Rubin,et al. Principal Stratification for Causal Inference With Extended Partial Compliance , 2008 .
[25] K. Goddard,et al. Utilization of HER2 genetic testing in a multi-institutional observational study. , 2012, The American journal of managed care.
[26] Fan Li,et al. Causal Inference: A Missing Data Perspective , 2017, 1712.06170.
[27] P. Royston,et al. Patrick Royston model with a binary outcome A comparison of imputation techniques for handling missing predictor values in a risk , 2007 .
[28] Jeremy MG Taylor,et al. Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials. , 2011, Biostatistics.
[29] B. Stricker,et al. Confounding by indication: an example of variation in the use of epidemiologic terminology. , 1999, American journal of epidemiology.
[30] Donald B. Rubin,et al. Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .
[31] D. Rubin. INFERENCE AND MISSING DATA , 1975 .
[32] S. van Buuren. Multiple imputation of discrete and continuous data by fully conditional specification , 2007, Statistical methods in medical research.
[33] T. Eberlein. A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer , 2006 .
[34] X H Zhou,et al. Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials , 2009, Biometrics.
[35] Arthur B. Kennickell,et al. Imputation of the 1989 Survey of Consumer Finances: Stochastic Relaxation and Multiple Imputation , 1997 .
[36] Julian Wolfson,et al. Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials , 2010, Biometrics.
[37] H. Goldstein,et al. Variance partitioning in multilevel logistic models that exhibit overdispersion , 2005 .
[38] Stef van Buuren,et al. Multiple imputation of discrete and continuous data by fully conditional specification , 2007 .
[39] D. Allison,et al. Treatment Heterogeneity and Individual Qualitative Interaction , 2012, The American statistician.
[40] Michael R Elliott,et al. Joint modeling compliance and outcome for causal analysis in longitudinal studies. , 2014, Statistics in medicine.
[41] Michael R Elliott,et al. Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes. , 2010, Biostatistics.
[42] B. Efron,et al. Compliance as an Explanatory Variable in Clinical Trials , 1991 .
[43] J. Pearl,et al. Bounds on Treatment Effects from Studies with Imperfect Compliance , 1997 .
[44] Nicole A. Lazar,et al. Statistical Analysis With Missing Data , 2003, Technometrics.
[45] J. Wolfson,et al. Design and Estimation for Evaluating Principal Surrogate Markers in Vaccine Trials , 2013, Biometrics.
[46] Pier Luigi Conti,et al. How far from identifiability? A systematic overview of the statistical matching problem in a non parametric framework , 2017 .
[47] Robert Gray,et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[48] Jeremy MG Taylor,et al. Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal. , 2014, Biostatistics.
[49] Geert Molenberghs,et al. On the relationship between the causal‐inference and meta‐analytic paradigms for the validation of surrogate endpoints , 2015, Biometrics.
[50] Sebastian Vollmer,et al. Testing for heterogeneous treatment effects in experimental data: false discovery risks and correction procedures , 2014 .
[51] Xiaoming Li,et al. A Multiple Imputation Approach for the Evaluation of Surrogate Markers in the Principal Stratification Causal Inference Framework , 2013 .
[52] Roee Gutman,et al. Estimation of causal effects of binary treatments in unconfounded studies , 2015, Statistics in medicine.
[53] D. Rubin. Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment , 1980 .
[54] Stephen R Cole,et al. Imputation approaches for potential outcomes in causal inference. , 2015, International journal of epidemiology.
[55] R. Uzzo,et al. Latent Class Survival Models Linked by Principal Stratification to Investigate Heterogenous Survival Subgroups Among Individuals With Early-Stage Kidney Cancer , 2017, Journal of the American Statistical Association.
[56] Dylan S. Small,et al. Bounds on causal effects in three‐arm trials with non‐compliance , 2006 .
[57] R. Bast,et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[58] Trivellore Raghunathan,et al. Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models , 2016, Statistics in medicine.
[59] S. Shak,et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients , 2006, Breast Cancer Research.
[60] Robert B Livingston,et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. , 2010, The Lancet. Oncology.
[61] K. McGlynn,et al. Chemotherapy Use and Survival Among Young and Middle-Aged Patients With Colon Cancer , 2017, JAMA surgery.
[62] Joseph W Hogan,et al. Principal stratification with predictors of compliance for randomized trials with 2 active treatments. , 2008, Biostatistics.