参考文献
以下是我的 R 進程信息：
sessionInfo()## R version 3.4.4 (2018-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
## [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid splines stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 PredictABEL_1.2-2 abind_1.4-5 HLMdiag_0.3.1
## [5] LogisticDx_0.2 PBSmodelling_2.68.6 ROCR_1.0-7 gplots_3.0.1
## [9] Hmisc_4.1-1 Formula_1.2-3 eha_2.5.1 stargazer_5.2.2
## [13] tableone_0.9.3 lmerTest_3.0-1 ATE_0.2.0 dagitty_0.2-2
## [17] exact2x2_1.6.2 exactci_1.3-3 ssanv_1.1 FSA_0.8.20
## [21] lmtest_0.9-36 zoo_1.8-2 BSDA_1.2.0 lattice_0.20-35
## [25] ggthemr_1.1.0 binomTools_1.0-1 limma_3.34.9 DescTools_0.99.24
## [29] ggsci_2.9 ggthemes_3.5.0 car_3.0-0 carData_3.0-1
## [33] scatterplot3d_0.3-41 mvtnorm_1.0-8 kableExtra_0.9.0 sandwich_2.4-0
## [37] nlme_3.1-131 lme4_1.1-17 Matrix_1.2-12 psych_1.8.4
## [41] margins_0.3.23 epiDisplay_3.5.0.1 nnet_7.3-12 MASS_7.3-49
## [45] foreign_0.8-69 rgl_0.99.16 epiR_0.9-96 shiny_1.1.0
## [49] epitools_0.5-10 flexsurv_1.1 mstate_0.2.11 cmprsk_2.2-7
## [53] gnm_1.1-0 KMsurv_0.1-5 Epi_2.30 gridExtra_2.3
## [57] plotly_4.7.1 haven_1.1.2 survminer_0.4.2 ggpubr_0.1.7
## [61] magrittr_1.5 ggfortify_0.4.5 survival_2.41-3 forcats_0.3.0
## [65] stringr_1.3.1 dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
## [69] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0 tidyverse_1.2.1
## [73] plyr_1.8.4 kfigr_1.2 knitr_1.20
##
## loaded via a namespace (and not attached):
## [1] utf8_1.1.4 rms_5.1-2 tidyselect_0.2.4
## [4] htmlwidgets_1.2 pROC_1.12.1 munsell_0.5.0
## [7] codetools_0.2-15 statmod_1.4.30 miniUI_0.1.1.1
## [10] withr_2.1.2 colorspace_1.3-2 muhaz_1.2.6
## [13] highr_0.7 rstudioapi_0.7.0-9000 labeling_0.3
## [16] RLRsim_3.1-3 mnormt_1.5-5 rprojroot_1.3-2
## [19] TH.data_1.0-8 xfun_0.3 R6_2.2.2
## [22] manipulateWidget_0.10.0 bitops_1.0-6 assertthat_0.2.0
## [25] promises_1.0.1 scales_0.5.0 multcomp_1.4-8
## [28] gtable_0.2.0 MatrixModels_0.4-1 rlang_0.2.1
## [31] lazyeval_0.2.1 acepack_1.4.1 broom_0.4.5
## [34] checkmate_1.8.5 yaml_2.1.19 reshape2_1.4.3
## [37] prediction_0.3.6 modelr_0.1.2 crosstalk_1.0.0
## [40] backports_1.1.2 httpuv_1.4.4.2 tools_3.4.4
## [43] tcltk_3.4.4 bookdown_0.7 RColorBrewer_1.1-2
## [46] Rcpp_0.12.17 base64enc_0.1-3 BiasedUrn_1.07
## [49] rpart_4.1-13 deSolve_1.21 cluster_2.0.6
## [52] survey_3.33-2 data.table_1.11.4 openxlsx_4.1.0
## [55] SparseM_1.77 manipulate_1.0.1 hms_0.4.2
## [58] mime_0.5 evaluate_0.10.1 xtable_1.8-2
## [61] XML_3.98-1.11 rio_0.5.10 etm_1.0.3
## [64] readxl_1.1.0 compiler_3.4.4 KernSmooth_2.23-15
## [67] V8_1.5 crayon_1.3.4 minqa_1.2.4
## [70] htmltools_0.3.6 mgcv_1.8-23 later_0.7.3
## [73] speedglm_0.3-2 expm_0.999-2 lubridate_1.7.4
## [76] boot_1.3-20 relimp_1.0-5 cli_1.0.0
## [79] quadprog_1.5-5 gdata_2.18.0 parallel_3.4.4
## [82] bindr_0.1.1 pkgconfig_2.0.1 km.ci_0.5-2
## [85] numDeriv_2016.8-1 xml2_1.2.0 webshot_0.5.0
## [88] rvest_0.3.2 digest_0.6.15 rmarkdown_1.10
## [91] cellranger_1.1.0 survMisc_0.5.5 htmlTable_1.12
## [94] curl_3.2 quantreg_5.36 gtools_3.8.1
## [97] nloptr_1.0.4 jsonlite_1.5 aod_1.3
## [100] qvcalc_0.9-1 viridisLite_0.3.0 pillar_1.2.3
## [103] httr_1.3.1 glue_1.2.0 zip_1.0.0
## [106] class_7.3-14 stringi_1.2.3 polspline_1.1.13
## [109] latticeExtra_0.6-28 caTools_1.17.1 e1071_1.6-8Abadie, Alberto, and Guido W Imbens. 2016. “Matching on the Estimated Propensity Score.” Econometrica 84 (2). Wiley Online Library: 781–807.
Armitage, Peter, Geoffrey Berry, and John Nigel Scott Matthews. 2008. Statistical Methods in Medical Research. John Wiley & Sons.
Austin, Peter C. 2011. “An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.” Multivariate Behavioral Research 46 (3). Taylor & Francis: 399–424.
Belle, G. van, L.D. Fisher, P.J. Heagerty, and T. Lumley. 2004. Biostatistics: A Methodology for the Health Sciences. Wiley Series in Probability and Statistics. Wiley. https://books.google.co.uk/books?id=KSh8IOrLPzwC.
Bland, J Martin, and DouglasG Altman. 1986. “Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement.” The Lancet 327 (8476). Elsevier: 307–10.
Cattaneo, Matias D. 2010. “Efficient Semiparametric Estimation of Multi-Valued Treatment Effects Under Ignorability.” Journal of Econometrics 155 (2). Elsevier: 138–54.
Cox, D.R. 2006. Principles of Statistical Inference. Cambridge University Press. https://books.google.co.jp/books?id=nRgtGZXi2KkC.
Cox, David R. 1972. “Models and Life-Tables Regression.” JR Stat. Soc. Ser. B 34: 187–220.
Cox, R. T. 1946. “Probability, Frequency and Reasonable Expectation.” American Journal of Physics 14 (1): 1–13. doi:10.1119/1.1990764.
Dawid, A Philip. 2000. “Causal Inference Without Counterfactuals.” Journal of the American Statistical Association 95 (450). Taylor & Francis: 407–24.
De Finetti, Bruno. 1974. Theory of Probability: A Critical Introductory Treatment. Transl. by Antonio Machi and Adrian Smith. J. Wiley.
Fisher, R. A. 1922. “On the Mathematical Foundations of Theoretical Statistics.” Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 222. The Royal Society: 309–68. http://www.jstor.org/stable/91208.
Gelman, A., J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, and D.B. Rubin. 2013. Bayesian Data Analysis, Third Edition. Chapman & Hall/Crc Texts in Statistical Science. Taylor & Francis. https://books.google.co.uk/books?id=ZXL6AQAAQBAJ.
Good, P.I. 2006. Permutation, Parametric, and Bootstrap Tests of Hypotheses. Springer Series in Statistics. Springer New York. https://books.google.co.uk/books?id=tQtedCBEgeAC.
Laan, Mark J van der, and Sherri Rose. 2017. “Targeted Learning in Data Science.” New York, NY: Springer International Publishing. Doi 10. Springer: 978–3.
Lesaffre, E., and A.B. Lawson. 2012. Bayesian Biostatistics. Statistics in Practice. Wiley. https://books.google.co.uk/books?id=WV7KVjEQnJMC.
Mann, Vera, Bianca L De Stavola, and David A Leon. 2004. “Separating Within and Between Effects in Family Studies: An Application to the Study of Blood Pressure in Children.” Statistics in Medicine 23 (17). Wiley Online Library: 2745–56.
Neyman, JS. 1923. “On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.(Tlanslated and Edited by Dm Dabrowska and Tp Speed, Statistical Science (1990), 5, 465-480).” Annals of Agricultural Sciences 10: 1–51.
Pearl, Judea. 2011. “Invited Commentary: Understanding Bias Amplification.” American Journal of Epidemiology 174 (11). Oxford University Press: 1223–7.
Pepe, Margaret S, Ziding Feng, Ying Huang, Gary Longton, Ross Prentice, Ian M Thompson, and Yingye Zheng. 2007. “Integrating the Predictiveness of a Marker with Its Performance as a Classifier.” American Journal of Epidemiology 167 (3). Oxford University Press: 362–68.
Raudenbush, Stephen W, and Anthony S Bryk. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods. Vol. 1. Sage.
Robinson, Laurence D, and Nicholas P Jewell. 1991. “Some Surprising Results About Covariate Adjustment in Logistic Regression Models.” International Statistical Review. JSTOR, 227–40.
Robinson, W. S. 1950. “Ecological Correlations and the Behavior of Individuals.” American Sociological Review 15 (3). [American Sociological Association, Sage Publications, Inc.]: 351–57. http://www.jstor.org/stable/2087176.
Rosenbaum, Paul R, and Donald B Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1). Oxford University Press: 41–55.
Royston, Patrick, and Mahesh KB Parmar. 2002. “Flexible Parametric Proportional-Hazards and Proportional-Odds Models for Censored Survival Data, with Application to Prognostic Modelling and Estimation of Treatment Effects.” Statistics in Medicine 21 (15). Wiley Online Library: 2175–97.
Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66 (5). American Psychological Association: 688.
Smith, Adrian. 1995. “A Conversation with Dennis Lindley.” Statistical Science 10 (3). JSTOR: 305–19.
Snijders, Tom, and Roel Bosker. 1999. “Multilevel Analysis: An Introduction to Basic and Applied Multilevel Analysis.” London: Sage.
Verbeke, Geert. 1997. Linear Mixed Models for Longitudinal Data. Springer.
Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. http://yihui.name/knitr/.
———. 2018. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.