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Volumn 81, Issue 11, 2011, Pages 1653-1675

A comparison of various software tools for dealing with missing data via imputation

Author keywords

Missing at random; Missing data; Missing not at random; Multiple imputation; Random forest

Indexed keywords


EID: 84855746403     PISSN: 00949655     EISSN: 15635163     Source Type: Journal    
DOI: 10.1080/00949655.2010.498788     Document Type: Article
Times cited : (6)

References (42)
  • 1
    • 0017133178 scopus 로고
    • Inference and missing data
    • Rubin, Inference and missing data, Biometrika 63 (1976), pp. 581-592.
    • (1976) Biometrika , vol.63 , pp. 581-592
  • 5
    • 0004093524 scopus 로고    scopus 로고
    • SAGE University Papers Series on Quantitative Applications in the Social Sciences, Sage Publications, Thousand Oaks, CA
    • P.D. Allison, Missing Data, SAGE University Papers Series on Quantitative Applications in the Social Sciences, Vol. 136, Sage Publications, Thousand Oaks, CA, 2002.
    • (2002) Missing Data , vol.136
    • Allison, P.D.1
  • 6
    • 84950455641 scopus 로고
    • Regression with missing X's: A review
    • R.J.A. Little, Regression with missing X's: A review, J. Am. Statist. Assoc. 87 (1992), pp. 1227-1237.
    • (1992) J. Am. Statist. Assoc. , vol.87 , pp. 1227-1237
    • Little, R.J.A.1
  • 7
    • 84950431939 scopus 로고
    • Incomplete data in generalized linear models
    • J.G. Ibrahim, Incomplete data in generalized linear models, J. Am. Statist. Assoc. 85 (1990), pp. 765-769.
    • (1990) J. Am. Statist. Assoc. , vol.85 , pp. 765-769
    • Ibrahim, J.G.1
  • 8
    • 0442293662 scopus 로고    scopus 로고
    • Missing data: Dial M for???
    • X.-L. Meng, Missing data: Dial M for???, J. Am. Statist. Assoc. 95 (2000), pp. 1325-1330.
    • (2000) J. Am. Statist. Assoc. , vol.95 , pp. 1325-1330
    • Meng, X.-L.1
  • 9
    • 2142647296 scopus 로고    scopus 로고
    • What do we do with missing data? Some options for analysis of incomplete data
    • T.E. Raghunathan, What do we do with missing data? Some options for analysis of incomplete data, Annu. Rev. Public Health 25 (2004), pp. 99-117.
    • (2004) Annu. Rev. Public Health , vol.25 , pp. 99-117
    • Raghunathan, T.E.1
  • 10
    • 14944360441 scopus 로고    scopus 로고
    • Missing-data methods for generalized linear models: A comparative review
    • J.G. Ibrahim, M.-H. Chen, S.R. Lipsitz, and A.H. Herring, Missing-data methods for generalized linear models: A comparative review, J. Am. Statist. Assoc. 100 (2005), pp. 332-346.
    • (2005) J. Am. Statist. Assoc. , vol.100 , pp. 332-346
    • Ibrahim, J.G.1    Chen, M.-H.2    Lipsitz, S.R.3    Herring, A.H.4
  • 11
    • 33846873244 scopus 로고    scopus 로고
    • Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models
    • N.J. Horton and K.P. Kleinman, Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models, Am. Stat. 61 (2007), pp. 79-90.
    • (2007) Am. Stat. , vol.61 , pp. 79-90
    • Horton, N.J.1    Kleinman, K.P.2
  • 12
    • 23044525261 scopus 로고    scopus 로고
    • Multiple imputation in practice: Comparison of software packages for regression models with missing variables
    • N.J. Horton and S.R. Lipsitz, Multiple imputation in practice: Comparison of software packages for regression models with missing variables, Am. Stat. 55 (2001), pp. 244-254.
    • (2001) Am. Stat. , vol.55 , pp. 244-254
    • Horton, N.J.1    Lipsitz, S.R.2
  • 13
    • 63149102671 scopus 로고    scopus 로고
    • MIDAS: A SAS macro for multiple imputation using distance-aided selection of donors [accessed May 25, 2009]
    • J. Siddique and O. Harel, MIDAS: A SAS macro for multiple imputation using distance-aided selection of donors [accessed May 25, 2009], J. Statist. Softw. 29 (2009), pp. 1-18.
    • (2009) J. Statist. Softw. , vol.29 , pp. 1-18
    • Siddique, J.1    Harel, O.2
  • 14
    • 4243828610 scopus 로고
    • Informative dropout in longitudinal data analysis (with discussion)
    • P.J. Diggle and M.G. Kenward, Informative dropout in longitudinal data analysis (with discussion), Appl. Stat. 43 (1994), pp. 49-93.
    • (1994) Appl. Stat. , vol.43 , pp. 49-93
    • Diggle, P.J.1    Kenward, M.G.2
  • 15
    • 3343019343 scopus 로고    scopus 로고
    • Missing covariate data within cancer prognostic studies: A review of current reporting and proposed guidelines
    • A. Burton and D.G. Altman, Missing covariate data within cancer prognostic studies: A review of current reporting and proposed guidelines, British J. Cancer 91 (2004), pp. 4-8.
    • (2004) British J. Cancer , vol.91 , pp. 4-8
    • Burton, A.1    Altman, D.G.2
  • 16
    • 27644579038 scopus 로고    scopus 로고
    • Statistical methods in the journal (research letter)
    • N.J. Horton and S.S. Switzer, Statistical methods in the journal (research letter), N. Engl. J. Med. 353 (2005), pp. 1977-1979.
    • (2005) N. Engl. J. Med. , vol.353 , pp. 1977-1979
    • Horton, N.J.1    Switzer, S.S.2
  • 17
    • 75149129982 scopus 로고    scopus 로고
    • Last observation carried forward: A crystal ball?
    • M.G. Kenward and G. Molenberghs, Last observation carried forward: A crystal ball? J. Biopharm. Statist. 19 (2009), pp. 872-888.
    • (2009) J. Biopharm. Statist. , vol.19 , pp. 872-888
    • Kenward, M.G.1    Molenberghs, G.2
  • 18
    • 33644667710 scopus 로고
    • Multiple imputations in sample surveys - a phenomenological Bayesian approach to nonresponse
    • Tech. Rep., U.S. Department of Commerce,Washington, DC
    • D.B. Rubin, Multiple imputations in sample surveys - a phenomenological Bayesian approach to nonresponse, in Imputation and Editing of Faulty or Missing Survey Data, Tech. Rep., U.S. Department of Commerce,Washington, DC, 1978, pp. 1-23.
    • (1978) Imputation and Editing of Faulty Or Missing Survey Data , pp. 1-23
    • Rubin, D.B.1
  • 20
    • 0001986642 scopus 로고    scopus 로고
    • The analysis of longitudinal ordinal data with non-random dropout
    • G. Molenberghs, M.G. Kenward, and E. Lesaffre, The analysis of longitudinal ordinal data with non-random dropout, Biometrika 84 (1997), pp. 33-44.
    • (1997) Biometrika , vol.84 , pp. 33-44
    • Molenberghs, G.1    Kenward, M.G.2    Lesaffre, E.3
  • 22
    • 0035285349 scopus 로고    scopus 로고
    • Analyzing incomplete political science data: An alternative algorithm for multiple imputation
    • G. King, J. Honaker, A. Joseph, and K. Scheve, Analyzing incomplete political science data: An alternative algorithm for multiple imputation, Am. Polit. Sci. Rev. 95 (2001), pp. 49-69.
    • (2001) Am. Polit. Sci. Rev. , vol.95 , pp. 49-69
    • King, G.1    Honaker, J.2    Joseph, A.3    Scheve, K.4
  • 23
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm (with discussion)
    • A.P. Dempster, N.M. Laird, and D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm (with discussion), J. R. Statist. Soc. B 39 (1977), pp. 1-38.
    • (1977) J. R. Statist. Soc. B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 24
    • 84868944008 scopus 로고    scopus 로고
    • accessed November 20, 2008, Available at
    • F.E. Harrell, The Hmisc Package [accessed November 20, 2008], 2009. Available at http://cran.r-project.org/web/packages/Hmisc/Hmisc.pdf.
    • (2009) The Hmisc Package
    • Harrell, F.E.1
  • 26
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman, Random forests, Mach. Learn 45 (2001), pp. 5-32.
    • (2001) Mach. Learn , vol.45 , pp. 5-32
    • Breiman, L.1
  • 28
    • 70049115890 scopus 로고    scopus 로고
    • SAS Institute Inc, [accessed November 20, 2008]. SAS OnlineDoc 9.1.2. SAS Institute Inc., Cary, NC, Available at
    • SAS Institute Inc., The MI Procedure [accessed November 20, 2008]. SAS OnlineDoc 9.1.2. SAS Institute Inc., Cary, NC, 2004. Available at http://support.sas.com/onlinedoc/912/getDoc/statug.hlp/mi_index.htm
    • (2004) The MI Procedure
  • 29
    • 77951622706 scopus 로고
    • The central role of the propensity score in observational studies for causal effects
    • P. Rosenbaum and D. Rubin, The central role of the propensity score in observational studies for causal effects, Biometrika 70 (1983), pp. 41-55.
    • (1983) Biometrika , vol.70 , pp. 41-55
    • Rosenbaum, P.1    Rubin, D.2
  • 30
    • 0002241694 scopus 로고
    • The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem
    • G. Celeux and J. Diebolt, The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem, Comput. Statist. Q. 2 (1985), pp. 73-82.
    • (1985) Comput. Statist. Q , vol.2 , pp. 73-82
    • Celeux, G.1    Diebolt, J.2
  • 31
    • 0002241603 scopus 로고    scopus 로고
    • Stochastic EM: Method and application
    • W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, eds., Chapman & Hall, London
    • J. Diebolt and E.H.S. Ip, Stochastic EM: Method and application, in Markov Chain Monte Carlo in Practice, W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, eds., Chapman & Hall, London, 1996.
    • (1996) Markov Chain Monte Carlo in Practice
    • Diebolt, J.1    Ip, E.H.S.2
  • 33
    • 33646076443 scopus 로고    scopus 로고
    • Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm
    • A.M. Gad and A.S. Ahmed, Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm, Comput. Stat. Data Anal. 50 (2006), pp. 2702-2714.
    • (2006) Comput. Stat. Data Anal. , vol.50 , pp. 2702-2714
    • Gad, A.M.1    Ahmed, A.S.2
  • 34
    • 77958076540 scopus 로고    scopus 로고
    • MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness
    • C. Sotto, C. Beunckens, G. Molenberghs, and M.G. Kenward, MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness, Comput. Statist. Data Anal. 55 (2011), pp. 301-311.
    • (2011) Comput. Statist. Data Anal. , vol.55 , pp. 301-311
    • Sotto, C.1    Beunckens, C.2    Molenberghs, G.3    Kenward, M.G.4
  • 35
    • 0001608056 scopus 로고    scopus 로고
    • An R-squared measure of goodness of fit for some common nonlinear regression models
    • A.C. Cameron and F.A.G. Windmeijer, An R-squared measure of goodness of fit for some common nonlinear regression models, J. Econom. 77 (1997), pp. 329-342.
    • (1997) J. Econom. , vol.77 , pp. 329-342
    • Cameron, A.C.1    Windmeijer, F.A.G.2
  • 37
    • 0002373852 scopus 로고
    • Discussion to Diggle
    • P.J. and Kenward, M.G.: Informative dropout in longitudinal data analysis
    • N.M. Laird, Discussion to Diggle, P.J. and Kenward, M.G.: Informative dropout in longitudinal data analysis, J. R. Statist. Soc. C 43 (1994), p. 84.
    • (1994) J. R. Statist. Soc. C , vol.43 , pp. 84
    • Laird, N.M.1
  • 38
    • 38949124382 scopus 로고    scopus 로고
    • Every missingness not at random model has a missingness at random counterpart with equal fit
    • G. Molenberghs, C. Beunckens, C. Sotto, and M. Kenward, Every missingness not at random model has a missingness at random counterpart with equal fit, J. R. Statist. Soc. B 70 (2008), pp. 371-388.
    • (2008) J. R. Statist. Soc. B , vol.70 , pp. 371-388
    • Molenberghs, G.1    Beunckens, C.2    Sotto, C.3    Kenward, M.4
  • 39
    • 8544270082 scopus 로고    scopus 로고
    • Interferon α-IIA is ineffective for patients with choroidal neovascularization secondary to age-related macular degeneration. Results of a prospective randomized placebo-controlled clinical trial
    • Pharmacological Therapy for Macular Degeneration Study Group
    • Pharmacological Therapy for Macular Degeneration Study Group, Interferon α-IIA is ineffective for patients with choroidal neovascularization secondary to age-related macular degeneration. Results of a prospective randomized placebo-controlled clinical trial, Arch. Ophthalmol. 115 (1997), pp. 865-872.
    • (1997) Arch. Ophthalmol. , vol.115 , pp. 865-872
  • 40
    • 0031708453 scopus 로고    scopus 로고
    • The validation of surrogate endpoints in randomized experiments
    • M. Buyse and G. Molenberghs, The validation of surrogate endpoints in randomized experiments, Biometrics 54 (1998), pp. 1014-1029.
    • (1998) Biometrics , vol.54 , pp. 1014-1029
    • Buyse, M.1    Molenberghs, G.2
  • 41


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