Albert, J.H., Chib, S.: Bayesian analysis of binary and polychotomous response data. J. Am. Stat. Assoc. 88(422), 669–679 (1993)
Angrist, J.D., Imbens, G.W., Rubin, D.B.: Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91(434), 444–455 (1996)
Basse, G.W., Volfovsky, A., Airoldi, E.M.: Observational studies with unknown time of treatment. arXiv preprint arXiv:1601.04083 (2016)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L., Cutler, A.: Manual for Setting Up, Using, and Understanding Random Forests. (2003) https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf
Byar, D.P.: Why data bases should not replace randomized clinical trials. Biometrics 36(2), 337–342 (1980)
Carlin, B.P., Polson, N.G.: Monte Carlo Bayesian methods for discrete regression models and categorical time series. Bayesian Stat. 4, 577–586 (1992)
Danaei, G., Rodríguez, L.A.G., Cantero, O.F., Logan, R., Hernán, M.A.: Observational data for comparative effectiveness research: an emulation of randomised trials of statins and primary prevention of coronary heart disease. Stat. Methods Med. Res. 22(1), 70–96 (2013)
Denison, D.G., Holmes, C.C., Mallick, B.K., Smith, A.F.: Bayesian Methods for Nonlinear Classification and Regression, vol. 386. Wiley, New York (2002)
Freiman, M.R., Rose, A.J., Powell, R.W., Miller, D.R., Wiener, R.S.: Patterns of potentially inappropriate prescribing of phosphodiesterase inhibitors in pulmonary hypertension in the va. In: C13. Accounting for costs and resource utilization in respiratory health, pp. A3889–A3889. American Thoracic Society (2015)
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian Data Analysis, vol. 2. Chapman & Hall/CRC, Boca Raton (2014)
Gelman, A., Rubin, D.B.: Inference from iterative simulation using multiple sequences. Stat. Sci. 7(4), 457–472 (1992)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)
Geweke, J.: Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments. Bayesian Stat. 4, 641–649 (1992)
Hernán, M.A., Alonso, A., Logan, R., Grodstein, F., Michels, K.B., Stampfer, M.J., Willett, W.C., Manson, J.E., Robins, J.M.: Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology 19(6), 766 (2008)
Holland, P.W.: Statistics and causal inference. J. Am. Stat. Assoc. 81(396), 945–960 (1986)
Kim, D., Lee, K.M., Freiman, M.R., Powell, W.R., Klings, E.S., Rinne, S.T., Miller, D.R., Rose, A.J., Wiener, R.S.: Phosphodiesterase-5 inhibitor therapy for pulmonary hypertension in the united states. Actual versus recommended use. Ann. Am. Thorac. Soc. 15(6), 693–701 (2018)
Levine, M.N., Julian, J.A.: Registries that show efficacy: good, but not good enough. J. Clin. Oncol. 26(33), 5316–5319 (2008)
Li, Y.P., Propert, K.J., Rosenbaum, P.R.: Balanced risk set matching. J. Am. Stat. Assoc. 96(455), 870–882 (2001)
Mamdani, M., Rochon, P.A., Juurlink, D.N., Kopp, A., Anderson, G.M., Naglie, G., Austin, P.C., Laupacis, A.: Observational study of upper gastrointestinal haemorrhage in elderly patients given selective cyclo-oxygenase-2 inhibitors or conventional non-steroidal anti-inflammatory drugs. BMJ 325(7365), 624 (2002)
McGettigan, P., Henry, D.: Cardiovascular risk and inhibition of cyclooxygenase a systematic review of the observational studies of selective and nonselective inhibitors of cyclooxygenase 2. JAMA 296(13), 1633–1644 (2006)
Plummer, M., et al.: Jags: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing, vol. 124, pp. 1–10. Vienna, Austria (2003)
Poses, R.M., Smith, W.R., McClish, D.K., Anthony, M.: Controlling for confounding by indication for treatment: are administrative data equivalent to clinical data? Medical Care, pp. AS36–AS46 (1995)
Robins, J.M., Hernán, M.A., Brumback, B.: Marginal structural models and causal inference in epidemiology. Epidemiology 11(5), 551 (2000)
Rubin, D.B.: Matching to remove bias in observational studies. Biometrics 29, 159–183 (1973)
Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581–592 (1976)
Rubin, D.B.: Randomization analysis of experimental data: the fisher randomization test comment. J. Am. Stat. Assoc. 75(371), 591–593 (1980)
Rubin, D.B.: William G. Cochran’s contributions to the design, analysis, and evaluation of observational studies. In: Rao, S., Sedransk, J. (eds.) Research Work of William G. Cochran, pp. 37–69. Wiley, New York (1984)
Rubin, D.B.: The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat. Med. 26(1), 20–36 (2007)
Rubin, D.B.: On the limitations of comparative effectiveness research. Stat. Med. 29(19), 1991–1995 (2010)
Slaughter, J.L., Reagan, P.B., Newman, T.B., Klebanoff, M.A.: Comparative effectiveness of nonsteroidal anti-inflammatory drug treatment vs no treatment for patent ductus arteriosus in preterm infants. JAMA Pediatr. 171(3), e164354–e164354 (2017)
Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Van Der Linde, A.: Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 64(4), 583–639 (2002)
van Houwelingen, H., Putter, H.: Dynamic Prediction in Clinical Survival Analysis. CRC Press, Boca Raton (2011)
Watson, D., Spaulding, A.B., Dreyfus, J.: Risk-set matching to assess the impact of hospital-acquired bloodstream infections. Am. J. Epidemiol. 188(2), 461–466 (2019)
Yoshida, K., Solomon, D.H., Kim, S.C.: Active-comparator design and new-user design in observational studies. Nat. Rev. Rheumatol. 11(7), 437–441 (2015)
Zhou, H., Hanson, T.: A unified framework for fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially referenced data. J. Am. Stat. Assoc. 113(522), 571–581 (2018)
Zhou, H., Hanson, T., Zhang, J.: spbayessurv: Fitting Bayesian spatial survival models using r. J. Stat. Softw. 92(1), 1–33 (2020)
Zhou, Z., Rahme, E., Abrahamowicz, M., Pilote, L.: Survival bias associated with time-to-treatment initiation in drug effectiveness evaluation: a comparison of methods. Am. J. Epidemiol. 162(10), 1016–1023 (2005)
Comments (0)