View PDF Version - RePub - Erasmus Universiteit Rotterdam

More documents

Recommendations

Info

Chapter 3.2 166 References 1. Kuiper EM, Hansen BE, de Vries RA, den Ouden-Muller JW, van Ditzhuijsen TJ, Haagsma EB, Houben MH, Witteman BJ, van Erpecum KJ, van Buuren HR, Dutch PBCSG. Improved prognosis of patients with primary biliary cirrhosis that have a biochemical response to ursodeoxycholic acid. Gastroenterology 2009;136:1281-7. 2. Lyles RH, Xu J. Classifying individuals based on predictors of random effects. Multicenter AIDS Cohort Study. Stat Med 1999;18:35-52. 3. Tomasko L, Helms RW, Snapinn SM. A discriminant analysis extension to mixed models. Stat Med 1999;18:1249-60. 4. Marshall G, Baron AE. Linear discriminant models for unbalanced longitudinal data. Stat Med 2000;19:1969-81. 5. Brant LJ, Sheng SL, Morrell CH, Verbeke GN, Lesaffre E, Carter HB. Screening for prostate cancer by using random-effects models. Journal of the Royal Statistical Society Series A - Statistics in Society 2003;166(part 1):51-62. 6. Wernecke K-D, Kalb G, Schink T, Wegner B. A mixed model approach to discriminant analysis with longitudinale data. Biometrical Journal 2004;46:246-254. 7. Morrell CH, Brant LJ, Sheng S, Metter EJ. Using multivariate mixed-effects models to predict prostate cancer. Proceedings of the American Statistical Association, Biometrics Section Alexandria, American Statistical Association 2005:332-337. 8. Marshall G, De la Cruz-Mesia R, Quintana FA, Baron AE. Discriminant analysis for longitudinal data with multiple continuous responses and possibly missing data. Biometrics 2009;65:69- 80. 9. Fieuws S, Verbeke G, Maes B, Vanrenterghem Y. Predicting renal graft failure using multivariate longitudinal profi les. Biostatistics 2008;9:419-31. 10. Verbeke G, Lesaffre E. A linear mixed-effects model with heterogeneity in the random-effects population. Journal of American Statistical Association 1996;91:217-221. 11. Verbeke G, Molenberghs G. Linear Mixed Models for Longitudinale Data. Springer-Verlag, 2000. 12. Komárek A, Hansen BE, Janssen HL, Lesaffre E. Prediction of binary response using multivariate longitudinale profi les: Study on chronic hepatitis B patients. In: J.G.Booth I, ed. Proceedings of the 24th International Workshop on Statistical Modelling. Ithaca, NY, 2009, Cornell University, 2009:187-192. 13. R. Development Core Team R: A language and Enviroment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2009. 14. Komárek A. A new R package for Baysian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data. Computational Statistics and Data Analysis 2009;53:3932-3947.
Discriminant analysis using a MLMM with a normal mixture 167 15. Bates D, Meachler M. Ime4: Linear mixed-effects models using S4 classes. R package version 0.999375-31 ed, 2009. 16. McLachlan GJ, Basford KE. Mixture Models: Inference and Applications to Clustering. Marcel Dekker, Inc., 1988. 17. Fieuws S, Verbeke G. Pairwise fi tting of mixed models for the joint modeling of multivariate longitudinal profi les. Biometrics 2006;62:424-31. 18. Bates D, Maechler M. Matrix: Sparse and dense matrix classes and methods. R package version 0.999375-31 ed, 2009. 19. Tanner MA, Wong WH. The calculation of posterior distributions by data augmentation Journal of American Statistical Association 1987;82:528-550. 20. Diebolt J, Robert CP. Estimation of fi nite mixture distributions throught Bayesian sampling Journal of Royal Statistical Society, Series B 1994:363-375. 21. Roeder K, Wasserman L. Practical Baysian density estimation using mixtures of normals. Journal of American Statistical Association 1997;92:894-902. 22. Richardson S, Green PJ. On Bayesian analysis of mixtures with unknown number of components (with Discussion). Journal of the Royal Statistical Society, Series B 1997;59:731-792. 23. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. Chapman & Hall/CRC: Boca Raton, Second edition, 2004. 24. Morrell CH, Brant LJ, Sheng S. Comparing approaches for predicting prostate cancer from longitudinal data, In Proceedings of the American Statistical Association, Alexandria, American Statistica Association, 2007. 25. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fi t (with Discussion). Journal of the Royal Statistical Society, Series B 2002;64:583- 639. 26. Plummer M, Best N, Cowles K, Vines K. coda: Output analysis and diagnostics for MCMC. R package version 0.13-4 ed, 2009. 27. Plummer M. Penalized loss functions for Bayesian model comparison. Biostatistics 2008;9:523- 539. 28. Aitken M, Liu CC, Chadwick T. Bayesian model comparison and model averaging for smallarea estimation. Annals of Applied Statistics 2009;3:199-221. 29. Shah A, Laird N, Schoenfeld D. A random-effects model for multiple characteristics with possibly missing data. Journal of the American Statistical Association 1997;92:775-779. 30. Pinheiro JC, Liu C, Wu YN. Effcient algorithms for robust estimation in linear mixed-effects models using the multivariate t distribution. Journal of Computational and Graphical Statistics 2001;10. 31. Lin TI, Lee JC, Ni HF. Bayesian analysis of mixture modelling using the multivariate t distribution. Statistics and Computing 2004;14:114-130.
Page 1:
Statistical Models of Treatment Eff
Page 4 and 5:
ISBN: 978-90-8559-069-9 © Bettina
Page 6 and 7:
PROMOTIECOMMISSIE Promotor: Prof.dr
Page 9 and 10:
CONTENTS Chapter 1. Introduction 11
Page 13:
Cha pter 1 Introduction
Page 16 and 17:
Chapter 1 14 Epidemiology Worldwide
Page 18 and 19:
Chapter 1 16 Prediction models with
Page 20 and 21:
Chapter 1 18 standard method fails
Page 22 and 23:
Chapter 1 20 The extended non-linea
Page 24 and 25:
Chapter 1 22 References 1. Blumberg
Page 27:
Cha pter 2 Prediction of response t
Page 30 and 31:
Chapter 2.1 28 ABSTRACT The aim of
Page 32 and 33:
Chapter 2.1 30 been hepatitis B sur
Page 34 and 35:
Chapter 2.1 32 HBV DNA decline The
Page 36 and 37:
Chapter 2.1 34 Figure 2 continued.
Page 38 and 39:
Chapter 2.1 36 Figure 4. Estimated
Page 40 and 41:
Chapter 2.1 38 DNA was observed in
Page 42:
Chapter 2.1 40 15. Kao JH. Role of
Page 46 and 47:
Chapter 2.2 44 ABSTRACT Background:
Page 48 and 49:
Chapter 2.2 46 low baseline HBV DNA
Page 50 and 51:
Chapter 2.2 48 patient subgroups wa
Page 52 and 53:
Chapter 2.2 50 p=0.002). Previous I
Page 54 and 55:
Chapter 2.2 52 Application of the m
Page 56 and 57:
Chapter 2.2 54 PEG-IFN, the proport
Page 58 and 59:
Chapter 2.2 56 References 1. Lavanc
Page 60 and 61:
Chapter 2.2 58 26. Bonino F, Lau GK
Page 63 and 64:
Chap ter 2.3 Prediction of the resp
Page 65 and 66:
INTRODUCTION Dynamic prediction of
Page 67 and 68:
Statistical analysis Dynamic predic
Page 69 and 70:
Dynamic prediction of response to P
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
References Dynamic prediction of re
Page 81 and 82:
Cha pter 2.4 Dynamic prediction of
Page 83 and 84:
INTRODUCTION Dynamic prediction of
Page 85 and 86:
For the indirect approach we rewrit
Page 87 and 88:
logit pi,j = logit Pr(Ri =1|visit =
Page 89 and 90:
Dynamic prediction of response usin
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Table 1. Results of different stopp
Page 103 and 104:
Page 105:
Page 108 and 109:
Chapter 2.5 106 ABSTRACT Peginterfe
Page 110 and 111:
Chapter 2.5 108 and placebo daily.
Page 112 and 113:
Chapter 2.5 110 for patients with a
Page 114 and 115:
Chapter 2.5 112 Mean HBV DNA declin
Page 116 and 117:
Chapter 2.5 114 peginterferon and n
Page 118 and 119: Chapter 2.5 116 hepatocellular carc
Page 120 and 121: Chapter 2.5 118 13. Werle-Lapostoll
Page 122: Chapter 2.5 120 Greece - G Germanid
Page 127 and 128: Chap ter 3.1 Long-term clinical out
Page 129 and 130: INTRODUCTION Glycyrrhizin for IFN-n
Page 131 and 132: Statistics Glycyrrhizin for IFN-non
Page 133 and 134: Glycyrrhizin for IFN-non responders
Page 135 and 136: Table 2. Time dependent Cox regress
Page 143 and 144: Cha pter 3.2 Discriminant analysis
Page 145 and 146: Introduction Discriminant analysis
Page 147 and 148: Discriminant analysis using a MLMM
Page 149 and 150: Estimation Discriminant analysis us
Page 153 and 154: where wi,k(y i, θ) = (k =1,...,K).
Page 167: Discriminant analysis using a MLMM
Page 171 and 172: Appendix Discriminant analysis usin
Page 175: Cha pter 4 Statistical models of ea
Page 178 and 179: Chapter 4.1 176 ABSTRACT Nucleoside
Page 180 and 181: Chapter 4.1 178 at day 1 at t =0 an
Page 182 and 183: Chapter 4.1 180 Table 1. Baseline c
Page 184 and 185: Chapter 4.1 182 Figure 1.
Page 186 and 187: Chapter 4.1 184 Figure 1. Viral dec
Page 188 and 189: Chapter 4.1 186 suppression in seru
Page 190 and 191: Chapter 4.1 188 13. Seifer M, Hamat
Page 193 and 194: Cha pter 4.2 Viral dynamics during
Page 195 and 196: INTRODUCTION Viral dynamics during
Page 197 and 198: Viral dynamics during tenofovir the
Page 201 and 202: log HBV DNA (copies/mL) 10.0 9.0 8.
Page 211 and 212: Chap ter 4.3 Modelling of early vir
Page 213 and 214: INTRODUCTION HBV viral kinetics dur
Page 215 and 216: HBV viral kinetics during PEG-IFN 2
Page 217 and 218: HBV viral kinetics during PEG-IFN 2
Page 219 and 220:
HBV viral kinetics during PEG-IFN 2
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229:
Page 233 and 234:
SUMMARY AND CONCLUSION Summary and
Page 235 and 236:
Summary and conclusion 233 The mode
Page 237 and 238:
Summary and conclusion 235 The infe
Page 239:
Summary and conclusion 237 Early st
Page 243 and 244:
SAMENVATTING EN CONCLUSIE Samenvatt
Page 245 and 246:
Samenvatting en conclusie 243 Er we
Page 247 and 248:
Samenvatting en conclusie 245 Data
Page 249 and 250:
Samenvatting en conclusie 247 Behan
Page 253:
Dan kwoord
Page 256 and 257:
Dankwoord 254 Lieve Marion en Margr
Page 259 and 260:
BIBLI OGRAPHY Bibliography 257 1. K
Page 261 and 262:
Bibliography 259 Interferon-ribavir
Page 263 and 264:
Bibliography 261 Long term clinical
Page 265 and 266:
Bibliography 263 Flares in chronic
Page 267 and 268:
Bibliography 265 Genetic characteri
Page 269:
Bibliography 267 106. Reijnders JG,
show all

View PDF Version - RePub - Erasmus Universiteit Rotterdam

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?