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Cha pter 2.4 Dynamic prediction of response to HBVtreatment using multivariate longitudinal profi les Bettina E. Hansen 1,2 , Arnošt Komárek 3 , Erik H.C.J. Buster 2 , Ewout W. Steyerberg 4 , Harry L.A. Janssen 2 , Emmanuel Lesaffre 1,5 Departments of 1 Biostatistics, 2 Gastroenterology & Hepatology, Erasmus MC University Medical Center, Rotterdam, the Netherlands, 3Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic, 4Public Health, Erasmus MC Medical Center, Rotterdam, the Netherlands and 5Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, Leuven, Belgium and Universiteit Hasselt, Belgium Submitted
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Statistical Models of Treatment Eff
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ISBN: 978-90-8559-069-9 © Bettina
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PROMOTIECOMMISSIE Promotor: Prof.dr
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CONTENTS Chapter 1. Introduction 11
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Cha pter 1 Introduction
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Chapter 1 14 Epidemiology Worldwide
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Chapter 1 16 Prediction models with
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Chapter 1 18 standard method fails
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Chapter 1 20 The extended non-linea
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Chapter 1 22 References 1. Blumberg
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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 77 and 78: References Dynamic prediction of re
Page 82 and 83: Chapter 2.4 80 ABSTRACT Dynamic upd
Page 84 and 85: Chapter 2.4 82 prediction is necess
Page 86 and 87: Chapter 2.4 84 a. b. c. Figure 1.Th
Page 88 and 89: Chapter 2.4 86 suggested above a sm
Page 90 and 91: Chapter 2.4 88 Finally in the rando
Page 92 and 93: Chapter 2.4 90 more or less no chan
Page 94 and 95: Chapter 2.4 92 a. b. c. Figure 3. B
Page 96 and 97: Chapter 2.4 94 Figure 4. Dynamic pr
Page 98 and 99: Chapter 2.4 96 overall baseline pre
Page 100 and 101: Chapter 2.4 98 Finally some compari
Page 102 and 103: Chapter 2.4 100 mixed effect models
Page 104 and 105: Chapter 2.4 102 References 1. Janss
Page 107 and 108: Cha pter 2.5 Early on-treatment pre
Page 109 and 110: INTRODUCTION Early on-treatment pre
Page 111 and 112: Statistical analysis Early on-treat
Page 113 and 114: Early on-treatment prediction of re
Page 119 and 120: References Early on-treatment predi
Page 125: Cha pter 3 Statistical models of lo
Page 128 and 129: Chapter 3.1 126 ABSTRACT Background
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Chapter 3.1 128 METHODS Study desig
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Chapter 3.1 130 the presence of a c
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Chapter 3.1 132 ure 2 30 >3 x ULN 2
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Chapter 3.1 134 DISCUSSION The aim
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Chapter 3.1 136 References 1. Poyna
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Chapter 3.1 138 U i = Time to failu
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Chapter 3.2 142 Abstract We have de
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Chapter 3.2 144 multiple markers wa
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Chapter 3.2 146 The correlation bet
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Chapter 3.2 148 for our MLMM is fac
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Chapter 3.2 150 X and Z matrices fr
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Chapter 3.2 152 For each g =0,...,G
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Chapter 3.2 154 Focus of marginal,
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Chapter 3.2 156 Figure 1. Dutch PBC
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Chapter 3.2 158 Table 2. Dutch PBC
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Chapter 3.2 164 effects correct the
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Chapter 3.2 166 References 1. Kuipe
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Chapter 3.2 168 32. Tsiatis AA, Dav
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Chapter 3.2 170 Table 4. Structure
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Cha pter 4.1 Viral dynamics during
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INTRODUCTION Viral dynamics during
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V(t) = V 0 { A exp (-λ 1 t ) + (1-
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Table 2. Dynamic parameters during
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Figure 1. Viral dynamics during and
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Viral dynamics during and after ent
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References Viral dynamics during an
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Viral dynamics during and after ent
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Chapter 4.2 192 ABSTRACT Tenofovir,
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Chapter 4.2 194 PATIENTS AND METHOD
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Chapter 4.2 196 2 Slow: decline bet
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Chapter 4.2 198 Table 2. Parameter
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Chapter 4.2 200 9.0 8.0 7.0 6.0 5.0
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Chapter 4.2 202 release. Although w
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Chapter 4.2 204 References 1. Perri
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Chapter 4.2 206 29. Lewin SR, Ribei
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Chapter 4.3 210 ABSTRACT Treatment
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Chapter 4.3 212 HBV DNA quantifi ca
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Chapter 4.3 214 The resulting model
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Chapter 4.3 216 Figure 1. Median HB
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Chapter 4.3 218 Table 2: Pharmacoki
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Chapter 4.3 220 Figure 3. effects r
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Chapter 4.3 222 13-14 This model ta
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Chapter 4.3 224 References 1. Kane
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Chapter 4.3 226 25. Wolters LM, Han
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Cha pter 5 Summary and conclusion
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Summary and conclusion 232 Therapy
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Summary and conclusion 234 who deve
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Summary and conclusion 236 fi t mod
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Cha pter 6 Samenvatting en conclusi
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Samenvatting en conclusie 242 posit
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Samenvatting en conclusie 244 De ve
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Samenvatting en conclusie 246 en na
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Samenvatting en conclusie 248 In ee
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DANKWOORD Dankwoord 253 Met oplucht
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CUR RICULUM VITAE Curriculum Vitae
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Bibliography 258 Mechanical factors
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Bibliography 260 35. Wolters LM, Ha
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Bibliography 262 Volume Measurement
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Bibliography 264 Quantitative HBV D
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Bibliography 266 to Peginterferon-a
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ISBN: 978-90-8559-069-9
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