Daniels M.J., Hogan J.W. (2008): Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis. CRC Press.10.1201/9781420011180
Heckman J.J. (1976): The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. In Annals of Economic and Social Measurement volume 5 pages 475–492. NBER.
Kaciroti N.A., Raghunathan T. (2014): Bayesian sensitivity analysis of incomplete data: bridging pattern-mixture and selection models. Statistics in medicine 33(27): 4841–4857.10.1002/sim.630225256610
Kaciroti N.A., Raghunathan T.E., Anthony Schork M., Clark N.M. (2008): A bayesian model for longitudinal count data with non-ignorable dropout. Journal of the Royal Statistical Society: Series C (Applied Statistics) 57(5): 521–534.
Kaciroti N.A., Raghunathan T.E., Taylor J.M., Julius S. (2012): A bayesian model for time-to-event data with informative censoring. Biostatistics 13(2): 341–354.10.1093/biostatistics/kxr048329782722223746
Kaciroti N.A., Schork M.A., Raghunathan T., Julius S. (2009): A bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts. Statistics in medicine 28(4): 572–585.10.1002/sim.349419072769
Kim J.K., Yu C.L. (2012): A semiparametric estimation of mean functionals with nonignorable missing data. Journal of the American Statistical Association.10.1198/jasa.2011.tm10104
Nath D.C., Bhattacharje A. (2012): Pattern mixture modeling: An application in anti diabetes drug therapy on serum creatinine in type 2 diabetes patients. Asian Journal of Mathematics & Statistics 5(3): 71.
Satty A., Mwambi H. (2013): Selection and pattern mixture models for modelling longitudinal data with dropout: An application study. SORT-Statistics and Operations Research Transactions 1(2): 131–152.