Multiple imputation for gamma outcome variable using generalized linear model

References

• Rubin DB. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987.
   Google Scholar
• Van Buuren S. Flexible imputation of missing data. Hoboken, NJ: CRC Press; 2012.
   Google Scholar
• Carpenter J, Kenward M. Multiple imputation and its application. Hoboken, NJ: John Wiley & Sons. 2012.
   Google Scholar
• Horton N, Lipsitz SR. Multiple imputation in practice: comparison of software packages for regression models with missing variables. Amer Statist. 2001;55(3):244–254. doi: 10.1198/000313001317098266
   Web of Science ®Google Scholar
• Harel O, Zhou XH. Multiple imputation: review of theory, implementation and software. Stat Med. 2007;26(16):3057–3077. doi: 10.1002/sim.2787
   PubMed Web of Science ®Google Scholar
• Reiter JP, Raghunathan TE. The multiple adaptations of multiple imputation. J Amer Statist Assoc. 2007;102(480):1462–1471. doi: 10.1198/016214507000000932
   Web of Science ®Google Scholar
• Ibrahim JG. Incomplete data in generalized linear models. J Amer Statist Assoc. 1990;85(411):765–769. doi: 10.1080/01621459.1990.10474938
   Web of Science ®Google Scholar
• Horton NJ, Laird NM. Maximum likelihood analysis of generalized linear models with missing covariates. Stat Methods Med Res. 1999;8(1):37–50. doi: 10.1177/096228029900800104
   PubMed Web of Science ®Google Scholar
• Lipsitz SR, Ibrahim JG. A conditional model for incomplete covariates in parametric regression models. Biometrika. 1996;83(4):916–922. doi: 10.1093/biomet/83.4.916
   Web of Science ®Google Scholar
• Wang CY, Wang S, Gutierrez RG, et al. Local linear regression for generalized linear models with missing data. Ann Statist. 1998;26(3):1028–1050. doi: 10.1214/aos/1024691087
   Web of Science ®Google Scholar
• Chen YH, Chen H. Incomplete covariates data in generalized linear models. J Statist Plann Inference. 1999;79(2):247–258. doi: 10.1016/S0378-3758(98)00255-9
   Web of Science ®Google Scholar
• Ibrahim JG, Chen MH, Lipsitz SR, et al. Missing-data methods for generalized linear models: a comparative review. J Amer Statist Assoc. 2005;100(469):332–346. doi: 10.1198/016214504000001844
   Web of Science ®Google Scholar
• Nielsen SF. Proper and improper multiple imputation. Int Stat Rev. 2003;71(3):593–607. doi: 10.1111/j.1751-5823.2003.tb00214.x
   Web of Science ®Google Scholar
• Wood AM, White IR, Thompson SG. Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clin Trials. 2004;1(4):368–376. doi: 10.1191/1740774504cn032oa
   PubMedGoogle Scholar
• White IR, Horton NJ, Carpenter J, et al. Strategy for intention to treat analysis in randomised trials with missing outcome data. BMJ. 2011;342:d40. doi:doi: 10.1136/bmj.d40
   PubMed Web of Science ®Google Scholar
• Shao J, Zhong B. Last observation carry-forward and last observation analysis. Stat Med. 2003;22(15):2429–2441. doi: 10.1002/sim.1519
   PubMed Web of Science ®Google Scholar
• Kenward MG, Goetghebeur EJ, Molenberghs G. Sensitivity analysis for incomplete categorical data. Stat Model. 2001;1(1):31–48. doi: 10.1191/147108201128078
   Web of Science ®Google Scholar
• Little RJ. Regression with missing X’s: a review. J Amer Statist Assoc. 1992;87(420):1227–1237.
   Web of Science ®Google Scholar
• Rubin DB. Multiple imputation after 18+ years. J Amer Statist Assoc. 1996;91(434):473–489. doi: 10.1080/01621459.1996.10476908
   Web of Science ®Google Scholar
• von Hippel PT. Regression with missing Ys: an improved strategy for analyzing multiply imputed data. Sociol Methodol. 2007;37(1):83–117. doi: 10.1111/j.1467-9531.2007.00180.x
   Web of Science ®Google Scholar
• Sullivan TR, Salter AB, Ryan P, et al. Bias and precision of the ‘multiple imputation, then deletion’ method for dealing with missing outcome data. Am J Epidemiol. 2015;182(6):528–534. doi: 10.1093/aje/kwv100
   PubMed Web of Science ®Google Scholar
• Van Burren S. Cross validated [Internet]. Multiple imputation for outcome variables. Stef van Burren; January 3, 2013. [cited 2016 April 23]. Available from: http://stats.stackexchange.com/questions/46226/multiple-imputation-for-outcome-variables
   Google Scholar
• Su YS, Gelman A, Hill J, et al. Multiple imputation with diagnostics (mi) in R: opening windows into the black box. J Stat Softw. 2011;45(2):1–31. doi: 10.18637/jss.v045.i02
   Web of Science ®Google Scholar
• Rowe P. Essential statistics for the pharmaceutical sciences. Chichester: John Wiley & Sons; 2007.
   Google Scholar
• Demirtas H, Freels SA, Yucel RM. Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: a simulation assessment. J Stat Comput Simul. 2008;78(1):69–84. doi: 10.1080/10629360600903866
   Web of Science ®Google Scholar
• von Hippel PT. Should a normal imputation model be modified to impute skewed variables? Sociol Methods Res. 2013;42(1):105–138. doi: 10.1177/0049124112464866
   Web of Science ®Google Scholar
• Grover G, Gadpayle AK, Makhija N. On the estimation of survival time of cardio-vascular disease patients with random number of myocardial infarctions using parametric and semi-parametric methods. Electron J Appl Stat Anal. 2009;3(1):1–17.
   Google Scholar
• Anderson TW, Darling DA. A test of goodness of fit. J Amer Statist Assoc. 1954;49(268):765–769. doi: 10.1080/01621459.1954.10501232
   Web of Science ®Google Scholar
• Nelder JA, Wedderburn RWM. Geleralized linear models. J R Stat Soc Ser A. 1972;135(3):370–384. doi: 10.2307/2344614
   Web of Science ®Google Scholar
• McCullagh P, Nelder JA. Generalized linear models. London: CRC Press; 1989.
   Google Scholar
• Hardin JW, Hilbe JM. Generalized linear model and extensions. 2nd ed. College station, TX: Stata Press; 2007.
   Google Scholar
• Cordeiro GM, Simas AB. The distribution of Pearson residuals in generalized linear models. Comput Statist Data Anal. 2009;53(9):3397–3411. doi: 10.1016/j.csda.2009.02.025
   Web of Science ®Google Scholar
• R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015. Available from: http://www.R-project.org/
   Google Scholar
• Chorozoglou M, Smith E, Koerting J, et al. Preschool hyperactivity is associated with long-term economic burden: evidence from a longitudinal health economic analysis of costs incurred across childhood, adolescence and young adulthood. J Child Psychol Psychiatry. 2015;56(9):966–975. doi: 10.1111/jcpp.12437
   PubMed Web of Science ®Google Scholar
• Burton A, Altman DG, Royston P, et al. The design of simulation studies in medical statistics. Stat Med. 2006;25:4279–4292. doi: 10.1002/sim.2673
   PubMed Web of Science ®Google Scholar
• Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147–177. doi: 10.1037/1082-989X.7.2.147
   PubMed Web of Science ®Google Scholar