Detection of Unusual Increases in MRI Lesion Counts in Individual Multiple Sclerosis Patients

REFERENCES

• Aitkin, M. (1999), “A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models,” Biometrics, 55, 117–128.
   PubMed Web of Science ®Google Scholar
• Altman, R. M., Petkau, A.J., Vrecko, D., and Smith, A. (2012), “A Longitudinal Model for Magnetic Resonance Imaging Lesion Count Data in Multiple Sclerosis Patients,” Statistics in Medicine, 31, 449–469.
   PubMedGoogle Scholar
• Barkhof, F., Held, U., Simon, J.H., Daumer, M., Fazekas, F., Filippi, M., Frank, J.A., Kappos, L., Li, D., Menzler, S., Miller, D.H., Petkau, J., and Wolinsky, J. (2005), “Predicting Gadolinium Enhancement Status in MS Patients Eligible for Randomized Clinical Trials,” Neurology, 65, 1447–1454.
   PubMedGoogle Scholar
• Barkhof, F., Simon, J.H., Fazekas, F., Rovaris, M., Kappos, L., de Stefano, N., Polman, C.H., Petkau, J., Radue, E.W., Sormani, M.P., Li, D.K., O’Connor, P., Montalban, X., Miller, D.H., and Filippi, M. (2012), “MRI Monitoring of Immunomodulation in Relapse-Onset Multiple Sclerosis Trials,” Nature Reviews Neurology, 8, 13–21.
   Web of Science ®Google Scholar
• Booth, J.G., Casella, G., Friedl, H., and Hobert, J.P. (2003), “Negative Binomial Loglinear Mixed Models,” Statistical Modelling, 3, 179–191.
   Web of Science ®Google Scholar
• Burr, D., and Doss, H. (2005), “A Bayesian Semi-Parametric Model for Random Effects Meta-Analysis,” Journal of the American Statistical Association, 100, 242–251.
   Web of Science ®Google Scholar
• Carroll, R.J., and Hall, P. (2004), “Low Order Approximations in Deconvolution and Regression With Errors in Variables,” Journal of the Royal Statistical Society, Series B, 66, 31–46.
   Google Scholar
• Ellenberg, S.S., Fleming, T.R., and DeMets, D.L. (2002), Data Monitoring Committees in Clinical Trials: A Practical Perspective, West Sussex: Wiley.
   Google Scholar
• Ghidey, W., Lesaffre, E., and Eilers, P. (2004), “Smooth Random Effects Distribution in a Linear Mixed Model,” Biometrics, 60, 945–953.
   PubMed Web of Science ®Google Scholar
• Joe, H. (1997), Multivariate Models and Dependence Concepts, London: Chapman & Hall.
   Google Scholar
• Li, D.K., Li, M.J., Traboulsee, A., Riddehough, A., and Paty, D. (2006), “The Use of MRI as an Outcome Measure in Clinical Trials,” Advances in Neurology, 98, 203–226.
   PubMedGoogle Scholar
• Litiére, S., Alonso, A., and Molenberghs, G. (2008), “The Impact of a Misspecified Random-Effects Distribution on the Estimation and the Performance of Inferential Procedures in Generalized Linear Mixed Models,” Statistics in Medicine, 27, 3125–3144.
   PubMed Web of Science ®Google Scholar
• McCulloch, C.E., and Neuhaus, J.M. (2011), “Misspecifying the Shape of a Random Effects Distribution: Why Getting it Wrong may not Matter,” Statistical Science, 26, 388–402.
   Web of Science ®Google Scholar
• Molenberghs, G., Verbeke, G., Demétrio, C. G.B., and Afrânio, M.C. (2010), “Family of Generalized Linear Model for Repeated Measures With Normal Conjugate Random Effects,” Statistical Science, 25, 325–347.
   Web of Science ®Google Scholar
• Ohlssen, D., Sharples, L., and Spiegelhalter, D. (2007), “Flexible Random-Effects Models Using Bayesian Semi-Parametric Models: Applications to Institutional Comparisons,” Statistics in Medicine, 26, 2088–2112.
   PubMed Web of Science ®Google Scholar
• Riddell, C.A., Zhao, Y., Li, D. K.B., Petkau, A.J., Riddehough, A., Cutter, G.R., and Traboulsee, A. (2011), “Evaluation of Safety Monitoring Guidelines Based on MRI Lesion Activity in Multiple Sclerosis,” Neurology, 77, 2089–2096.
   PubMedGoogle Scholar
• Shen, W., and Louis, T. (1999), “Empirical Bayes Estimation Via the Smoothing by Roughening Approach,” Journal of Computational and Graphical Statistics, 8, 800–823.
   Web of Science ®Google Scholar
• Sormani, M.P., Bruzzi, P., Miller, D.H., Gasperini, C., Barkhof, F., and Filippi, M. (1999), “Modelling MRI Enhancing Lesion Counts in Multiple Sclerosis Using a Negative Binomial Model: Implications for Clinical Trials,” Journal of the Neurological Sciences, 163, 74–80.
   PubMedGoogle Scholar
• Staudenmayer, J., Ruppert, D., and Buonaccorsi, J.P. (2008), “Density Estimation in the Presence of Heteroscedastic Measurement Error,” Journal of the American Statistical Association, 103, 726–736.
   Web of Science ®Google Scholar
• Elskamp, I.J., Knol, D.L., Uitdehaag, B. M.J., and Barkhof, F. (2009), “The Distribution of New Enhancing Lesion Counts in Multiple Sclerosis: Further Explorations,” Multiple Sclerosis, 15, 42–49.
   PubMedGoogle Scholar
• Zhang, D., and Davidian, M. (2001), “Linear Mixed Models With Flexible Distributions of Random Effects for Longitudinal Data,” Biometrics, 57, 795–802.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., Petkau, A.J., Traboulsee, A., Riddehough, A., and Li, K. B.D. (2010), “Does MRI Lesion Activity Regress in Secondary-Progressive Multiple Sclerosis,” Multiple Sclerosis, 16, 434–442.
   PubMedGoogle Scholar
• Zhao, Y., Traboulsee, A., Petkau, A.J., and Li, D. (2008), “Regression of New Gadolinium Enhancing Lesion Activity in Relapsing-Remitting Multiple Sclerosis,” Neurology, 70, 1092–1097.
   PubMedGoogle Scholar