358
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

Longitudinal conditional models with intermittent missingness: SAS code and applications

&
Pages 753-780 | Received 14 Feb 2011, Accepted 27 Aug 2012, Published online: 25 Sep 2012

References

  • D. R., Cox, The analysis of multivariate binary data. Appl. Stat., 21 (1972), 113–120. doi: 10.2307/2346482
  • G., Molenberghs, G., Verbeke (2005). Models for Discrete Longitudinal Data, New York: Springer-Verlag.
  • D. B., Rubin, Inference and missing data. Biometrika, 63 (1976), 581–592. doi: 10.1093/biomet/63.3.581
  • G., Verbeke, G., Molenberghs, Linear Mixed Models for Longitudinal Data, New York: Springer-Verlag 2000.
  • G., Molenberghs, M. G., Kenward, Missing Data in Clinical Studies, Chichester: John Wiley & Sons 2007.
  • R. J.A., Little, D. B., Rubin, Statistical Analysis with Missing Data, New York: John Wiley & Sons 1987.
  • Chair of Statistics, University of Würzburg, A First Course on Time Series Analysis. Examples with SAS, Available at http://www.statistik-mathematik.uni-wuerzburg.de/fileadmin/10040800/user_upload/time_series/the_book/2006-September-01-times.pdf. 2006
  • P., Diggle, M. G., Kenward, Informative drop-out in longitudinal data analysis. Appl. Stat., 43(1) (1994), 49–93. doi: 10.2307/2986113
  • Cuban Registry of Clinical Trials, Havana, Cuba, National Center of Clinical Trials, 2007. Available at http://registroclinico.sld.cu.
  • R., Uranga, Transition models with intermittent missing data: Implementation and application to a Cuban clinical trial. VacciMonitor, 20(2) (2011), 11–16.
  • M. A., Marrero Two natural products in migraine prevention. Clinical trials phase III, 3rd Cuba: International symposium on pharmacology of natural products, Topes de Collantes, Sancti Spiritus. 6–10 June 2012. Available at www.fapronaturacuba.com.
  • I., Jansen, C., Beunckens, G., Molenberghs, G., Verbeke, C., Mallinckrodt (2006). Analyzing incomplete discrete longitudinal clinical trial data. Statist. Sci., 21(1), 52–69. doi: 10.1214/088342305000000322
  • J. G., Ibrahim, G., Molenberghs (2009). Missing data methods in longitudinal studies: A review. Test, 18, 1–43. Available at http://www.ncbi.nlm.nih.gov/pubmed/21218187. doi: 10.1007/s11749-009-0138-x
  • H., White (1982). Maximum likelihood estimation of misspecified models. Econometrica, 50(1), 1–25. doi: 10.2307/1912526
  • B., Zhang (2001). An information matrix test for logistic regression models based on case-control data. Biometrika, 88(4), 921–932. doi: 10.1093/biomet/88.4.921
  • S., Litiére, A., Alonso, G., Molenberghs (2007). Type I and type II error under random-effects misspecification in generalized linear mixed models. Biometrics, 63, 1038–1044. doi: 10.1111/j.1541-0420.2007.00782.x
  • A. A., Abad, S., Litiere, G., Molenberghs (2010). Testing for misspecification in generalized linear mixed models. Biostatistics, 11(4), 771–786. doi: 10.1093/biostatistics/kxq019
  • J. M., Neuhaus, C. E., McCulloch, R., Boylan (2011). A note on type II error under random effects misspecification in generalized linear mixed models. Biometrics, 67, 654–660. doi: 10.1111/j.1541-0420.2010.01474_1.x
  • SAS Institute Inc (2004). SAS OnlineDoc\textregistered 9.1.3, Cary, NC: SAS Institute Inc.
  • P. J., Diggle, K.-Y., Liang, S. L., Zeger (1994). Analysis of Longitudinal Data, Clarendon Press, Oxford: Oxford Science.
  • A. P., Dempster, N. M., Laird, D. B., Rubin (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. Ser. B, 39, 1–38.
  • G., Verbeke, G., Molenberghs, H., Thijs, E., Lesaffre, M. G., Kenward (2001). Sensitivity analysis for non-random dropout: A local influence approach. Biometrics, 57, 7–14. doi: 10.1111/j.0006-341X.2001.00007.x
  • H., Thijs, G., Molenberghs, G., Verbeke (2000). The milk protein trial: Influence analysis of the dropout process. Biom. J., 42, 617–646. doi: 10.1002/1521-4036(200009)42:5<617::AID-BIMJ617>3.0.CO;2-N
  • G., Molenberghs, G., Verbeke, H., Thijs, E., Lesaffre, M. G., Kenward (2001). Mastitis in dairy cattle: Influence analysis to assess sensitivity of the dropout process. Comput. Stat. Data Anal., 37, 93–113. doi: 10.1016/S0167-9473(00)00065-7
  • R. D., Cook (1986). Assessment of local influence. J. R. Statist. Soc. Ser. B, 48, 133–169.
  • H., Thijs, G., Molenberghs, B., Michiels, G., Verbeke, D., Curran (2002). Strategies to fit pattern-mixture models. Biostatistics, 3, 245–265. doi: 10.1093/biostatistics/3.2.245
  • B., Michiels, G., Molenberghs, L., Bijnens, T., Vangeneugden (2002). Selection models and pattern-mixture models to analyze longitudinal quality of life data subject to dropout. Stat. Med., 21, 1023–1041. doi: 10.1002/sim.1064
  • M. G., Kenward, E. J.T., Goetghebeur, G., Molenberghs (2001). Sensitivity analysis of incomplete categorical data. Statist. Model., 1, 31–48. doi: 10.1191/147108201128078
  • V. V., Morariu, L., Buimaga-Iarinca Auto-regressive modeling of coding sequence lengths in bacterial genome, arXiv Preprint archive (2009). Available at http://arxiv.org/ftp/arxiv/papers/0907/0907.1159.pdf.
  • G. E., Ding-Fei, H., Bei-Ping, X., Xin-Jian (2007). Study of feature extraction based on auto-regressive modeling in ECG automatic diagnosis. Acta Automat. Sinica, 33(5), 462–466.
  • S. L., Simpson, L. J., Edwards, K. E., Muller, M. A., Styner Kronecker product linear exponent AR(1) correlation structures for multivariate repeated measures, arXiv Preprint archive (2010). Available at http://arxiv.org/ftp/arxiv/papers/1010/1010.4471.pdf.
  • R., Holst, B., Jorgensen Efficient and robust estimation for a class of generalized linear longitudinal mixed models, arXiv Preprint archive (2010). Available at http://arxiv.org/PS_cache/arxiv/pdf/1008/1008.2870v1.pdf.
  • A. C., Titman (2011). Flexible nonhomogeneous Markov models for panel observed data. Biometrics, 67, 780–787. doi: 10.1111/j.1541-0420.2010.01550.x
  • X., Jiang, D., Gold, E. D., Kolaczyk (2011). Network-based auto-probit modeling for protein function prediction. Biometrics, 67, 958–966. doi: 10.1111/j.1541-0420.2010.01519.x
  • C. E., McCulloch, J. M., Neuhaus (2011). Prediction of random effects in linear and generalized linear models under model misspecification. Biometrics, 67, 270–279. doi: 10.1111/j.1541-0420.2010.01435.x
  • N., Neeti, J. R., Eastman (2011). A contextual Mann-Kendall approach for the assessment of trend significance in image time series. Trans. GIS, 15(5), 599–611. doi: 10.1111/j.1467-9671.2011.01280.x
  • W. L., Wang, T. H., Fan (2011). Estimation in multivariate t linear mixed models for multiple longitudinal data. Statist. Sinica, 21(4), 1857–1880. doi: 10.5705/ss.2009.306
  • N. H., Holford, K. E., Peace (1992). Methodologic aspects of a population pharmacodynamic model for cognitive effects in Alzheimer patients treated with tacrine. Proc. Natl Acad. Sci. USA, 89(23), 11466–70. doi: 10.1073/pnas.89.23.11466
  • C. R., Rao (1973). Linear Statistical Inference and Its Applications, 2nd ed., New York: John Wiley & Sons.
  • A. H., Wright (1985). Finding all solutions to a system of polynomial equations. Math. Comput., 44(169), 125–133. doi: 10.1090/S0025-5718-1985-0771035-4

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.