Testing the proportional odds assumption in multiply imputed ordinal longitudinal data

References

• N.K. Aaronson, S. Ahmedzai, B. Bergman, M. Bullinger, A. Cull, N.J. Duez, A. Filiberti, H. Flechtner, D.B. Fleishman, J.C.J.M. De Haes, S. Kaasa, M. Klee, D. Osoba, D. Razavi, P. Rofe, S. Schraub, K. Sneeuw, M. Sullivan, and F. Takeda, The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology, J. Natl. Cancer Inst. 85 (1993), pp. 365–376. doi: 10.1093/jnci/85.5.365
   PubMed Web of Science ®Google Scholar
• A. Albert and J.A. Anderson, On the existence of maximum likelihood estimates in logistic regression models, Biometrika 71 (1984), pp. 1–10. doi: 10.1093/biomet/71.1.1
   Web of Science ®Google Scholar
• R. Bender and U. Grouwen, Using binary logistic regression models for ordinal data with non-proportional odds, J. Clin. Epidemiol. 51(10) (1998), pp. 809–816. doi: 10.1016/S0895-4356(98)00066-3
   PubMed Web of Science ®Google Scholar
• S. van Buuren, Multiple imputation of discrete and continuous data by full conditional specification, Stat. Methods Med. Res. 16 (2007), pp. 219–242. doi: 10.1177/0962280206074463
   PubMed Web of Science ®Google Scholar
• A.F. Donneau, M. Mauer, P. Lambert, G. Molenberghs, and A. Albert, Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings, J. Biopharm. Stat. (in press), doi:10.1080/10543406.2014.920864.
   PubMed Web of Science ®Google Scholar
• A.F. Donneau, M. Mauer, G. Molenberghs, and A. Albert, A simulation study comparing multiple imputation methods for incomplete longitudinal ordinal data, Commun. Stat. Simul. Comput. (in press), 10.1080/03610918.2013.818690
   Web of Science ®Google Scholar
• M. Gameroff, Using the proportional odds model for health-related outcomes: Why, When, and How with various SAS procedures, Paper presented at SAS Users Group International, thirty annual conference, Philadelphia, PA, 2005.
   Google Scholar
• J.H. Goodnight, A tutorial on the SWEEP operator, Am. Stat. 33 (1979), pp. 149–158.
   Web of Science ®Google Scholar
• J.W. Graham, A.E. Olchowski, and T.D. Gilreath, How many imputations are really needed? Some practical clarifications of multiple imputation theory, Prevent. Sci. 8 (2007), pp. 206–213. doi: 10.1007/s11121-007-0070-9
   PubMed Web of Science ®Google Scholar
• N.A. Ibrahim and S. Suliadi, Generating correlated discrete ordinal data using R and SAS IML, Comput. Methods Prog. Biomed. 104(3) (2011), pp. 122–132. doi: 10.1016/j.cmpb.2011.06.003
   Web of Science ®Google Scholar
• A.J. Lee, Some simple methods for generating correlated categorical variates, Comput. Stat. Data Anal. 26 (1997), pp. 133–148. doi: 10.1016/S0167-9473(97)00030-3
   Web of Science ®Google Scholar
• K.-H. Li, X.-L. Meng, T. Raghunathan, and D.B. Rubin, Significance levels from repeated p-values with multiply-imputed data, Stat. Sin. 1 (1991), pp. 65–92.
   Web of Science ®Google Scholar
• K.-Y. Liang and S.L. Zeger, Longitudinal data analysis using generalized linear models, Biometrika 73 (1986), pp. 13–22. doi: 10.1093/biomet/73.1.13
   Web of Science ®Google Scholar
• S.R. Lipsitz, K. Kim, and L. Zhao, Analysis of repeated categorical data using generalized estimating equations, Stat. Med. 13(11) (1994), pp. 1149–1163. doi: 10.1002/sim.4780131106
   PubMed Web of Science ®Google Scholar
• R.J.A. Little, Modelling the drop-out mechanism in repeated measures studies, J. Am. Stat. Assoc. 90 (1995), pp. 1112–1121. doi: 10.1080/01621459.1995.10476615
   Web of Science ®Google Scholar
• R.J.A. Little and D.B. Rubin, Statistical Analysis with Missing Data, Wiley, New York, 1987.
   Google Scholar
• P. McCullagh, Regression models for ordinal data (with discussion), J. R. Stat. Soc. Ser. B 42 (1980), pp. 109–142.
   Google Scholar
• B. Peterson and F. Harrell, Partial proportional odds models for ordinal response variables, J. R. Stat. Soc. Ser. C 39(2) (1990), pp. 205–217.
   Web of Science ®Google Scholar
• J.M. Robins and A. Rotnitzky, Semiparametric efficiency in multivariate regression models with missing data, J. Am. Stat. Assoc. 90 (1995), pp. 122–129. doi: 10.1080/01621459.1995.10476494
   Web of Science ®Google Scholar
• J.M. Robins, A. Rotnitzky, and L.P. Zhao, Analysis of semiparametric regression models with missing data, J. Am. Stat. Assoc. 90 (1995), pp. 106–121. doi: 10.1080/01621459.1995.10476493
   Web of Science ®Google Scholar
• A. Rotnitzky and N.P. Jewell, Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data, Biometrika 77 (1990), pp. 485–497. doi: 10.1093/biomet/77.3.485
   Web of Science ®Google Scholar
• D.B. Rubin, Inference and missing data, Biometrika 63 (1976), pp. 581–592. doi: 10.1093/biomet/63.3.581
   Web of Science ®Google Scholar
• D.B. Rubin, Multiple Imputations for Nonresponse in Survey, Wiley, New York, 1987.
   Google Scholar
• J.L. Schafer, Analysis of Incomplete Multivariate Data, Chapman & Hall, London, 1997.
   Google Scholar
• T. Stiger, H. Barnhart, and J. Williamson, Testing proportionality in the proportional odds model fitted with GEE, Stat. Med. 18 (1999), pp. 1419–1433. doi: 10.1002/(SICI)1097-0258(19990615)18:11<1419::AID-SIM127>3.0.CO;2-Q
   PubMed Web of Science ®Google Scholar
• M. Stokes, C. Davis, and G. Koch, Categorical Data Analysis Using the SAS System, 2nd ed., SAS Institute Inc., Cary, NC, 2000.
   Google Scholar
• R. Stupp, W.P. Mason, M.J. van den Bent, M. Weller, B. Fisher, M.J.B. Taphoorn, K. Belanger, A.A. Brandes, C. Marosi, U. Bogdahn, J. Curschmann, R.C. Janzer, S.K. Ludwin, T. Gorlia, A. Allgeier, D. Lacombe, J.G. Cairncross, E. Eisenhauer, and R.O. Mirimanoff, Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma, N. Engl. J. Med. 352(10) (2005), pp. 987–996. doi: 10.1056/NEJMoa043330
   PubMed Web of Science ®Google Scholar
• M.A. Tanner and W.H. Wong, The calculation of posterior distribution by data augmentation, J. Am. Stat. Assoc. 82 (1987), pp. 528–550. doi: 10.1080/01621459.1987.10478458
   Web of Science ®Google Scholar
• M.J. Taphoorn, R. Stupp, C. Coens, D. Osoba, R. Kortmann, M.J. van den Bent, W. Mason, R.O. Mirimanoff, B.G. Baumert, E. Eisenhauer, P. Forsyth, and A. Bottomley, Health-related quality of life in patients with glioblastoma: A randomized controlled trial, Lancet Oncol. 6(12) (2005), pp. 937–944. doi: 10.1016/S1470-2045(05)70432-0
   PubMed Web of Science ®Google Scholar
• S.H. Walker and D.B. Duncan, Estimation of the probability of an event as a function of several independent variables, Biometrika 54 (1967), pp. 167–178. doi: 10.1093/biomet/54.1-2.167
   PubMed Web of Science ®Google Scholar
• I.R. White, P. Royston, and A.M. Wood, Multiple imputation using chained equations: Issues and guidance for practice, Stat. Med. 30(4) (2011), pp. 377–399. doi: 10.1002/sim.4067
   PubMed Web of Science ®Google Scholar
• S.L. Zeger and K.-Y. Liang, Longitudinal data analysis for discrete and continuous outcomes, Biometrics 42 (1986), pp. 121–130. doi: 10.2307/2531248
   PubMed Web of Science ®Google Scholar