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SUMMARY AND CONCLUSION Summary and conclusion 231 This research focuses on development and application of statistical models to analyse the treatment effects in chronic hepatitis B and C. Treatment options for both hepatitis B and C are expanding and there is thus a growing demand for individual fi rst-line treatment recommendation. This can only be achieved by studying the effects of treatment in detail. Which patients benefi t from treatment, in which should treatment be stopped and what are the early and long-term effects of treatment? With advanced statistical models the results of clinical studies of treatment of chronic hepatitis B and C were analysed, achieving more insight into how the individual patient reacts to treatment. PREDICTION OF RESPONSE TO TREATMENT The decline of HBV DNA during peginterferon (PEG-IFN) therapy and the spontaneous fl uctuations in viral load in placebo-treated patients with HBeAg-positive chronic hepatitis B were compared in Chapter 2.1. A total of 136 HBeAg-positive patients who participated in a randomized trial were treated with PEG-IFN alfa-2b for 52 weeks. This group was compared with 167 HBeAgpositive patients who received placebo for 48 weeks using linear mixed regression analysis. Response was defi ned as negative HBeAg at end of treatment (EOT). Overall, decline of HBV DNA at EOT was larger in the PEG-IFN group compared with placebo and varied according to HBV genotype. Viral suppression was stronger in the PEG-IFN group compared with placebo starting at week 4 and throughout the entire treatment period. Among responders, HBV DNA decline was larger for PEG-IFN than placebo. ALT levels were signifi cantly related to HBV DNA decline at the next visit and ALT fl ares (≥5 times the upper limit) during PEG-IFN therapy were associated with a stronger HBV DNA decline compared with placebo. In conclusion PEG-IFN therapy resulted in a larger HBV DNA decline compared with placebo. Furthermore, the decline of HBV DNA was stronger in HBeAg-positive patients who lost HBeAg or who exhibited a fl are during PEG-IFN therapy compared with spontaneous HBeAg loss or fl ares occurring during placebo therapy. Baseline prediction model In Chapter 2.2 the baseline prediction of response of HBeAg positive chronic hepatitis B to PEG-IFN is studied.
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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
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Chapter 2.1 28 ABSTRACT The aim of
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Chapter 2.1 30 been hepatitis B sur
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Chapter 2.1 32 HBV DNA decline The
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Chapter 2.1 34 Figure 2 continued.
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Chapter 2.1 36 Figure 4. Estimated
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Chapter 2.1 38 DNA was observed in
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Chapter 2.1 40 15. Kao JH. Role of
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Chapter 2.2 44 ABSTRACT Background:
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Chapter 2.2 46 low baseline HBV DNA
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Chapter 2.2 48 patient subgroups wa
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Chapter 2.2 50 p=0.002). Previous I
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Chapter 2.2 52 Application of the m
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Chapter 2.2 54 PEG-IFN, the proport
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Chapter 2.2 56 References 1. Lavanc
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Chapter 2.2 58 26. Bonino F, Lau GK
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Chap ter 2.3 Prediction of the resp
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INTRODUCTION Dynamic prediction of
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Statistical analysis Dynamic predic
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Dynamic prediction of response to P
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References Dynamic prediction of re
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Cha pter 2.4 Dynamic prediction of
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INTRODUCTION Dynamic prediction of
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For the indirect approach we rewrit
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logit pi,j = logit Pr(Ri =1|visit =
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Dynamic prediction of response usin
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Table 1. Results of different stopp
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Chapter 2.5 106 ABSTRACT Peginterfe
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Chapter 2.5 108 and placebo daily.
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Chapter 2.5 110 for patients with a
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Chapter 2.5 112 Mean HBV DNA declin
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Chapter 2.5 114 peginterferon and n
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Chapter 2.5 116 hepatocellular carc
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Chapter 2.5 118 13. Werle-Lapostoll
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Chapter 2.5 120 Greece - G Germanid
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Chap ter 3.1 Long-term clinical out
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INTRODUCTION Glycyrrhizin for IFN-n
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Statistics Glycyrrhizin for IFN-non
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Glycyrrhizin for IFN-non responders
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Table 2. Time dependent Cox regress
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Cha pter 3.2 Discriminant analysis
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Introduction Discriminant analysis
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Discriminant analysis using a MLMM
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Estimation Discriminant analysis us
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where wi,k(y i, θ) = (k =1,...,K).
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Appendix Discriminant analysis usin
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Cha pter 4 Statistical models of ea
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Chapter 4.1 176 ABSTRACT Nucleoside
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Chapter 4.1 178 at day 1 at t =0 an
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Page 186 and 187: Chapter 4.1 184 Figure 1. Viral dec
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Page 190 and 191: Chapter 4.1 188 13. Seifer M, Hamat
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Page 195 and 196: INTRODUCTION Viral dynamics during
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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 229: HBV viral kinetics during PEG-IFN 2
Page 234 and 235: Summary and conclusion 232 Therapy
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Page 241: Cha pter 6 Samenvatting en conclusi
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Page 246 and 247: Samenvatting en conclusie 244 De ve
Page 248 and 249: Samenvatting en conclusie 246 en na
Page 250: Samenvatting en conclusie 248 In ee
Page 255 and 256: DANKWOORD Dankwoord 253 Met oplucht
Page 257: CUR RICULUM VITAE Curriculum Vitae
Page 260 and 261: Bibliography 258 Mechanical factors
Page 262 and 263: Bibliography 260 35. Wolters LM, Ha
Page 264 and 265: Bibliography 262 Volume Measurement
Page 266 and 267: Bibliography 264 Quantitative HBV D
Page 268 and 269: Bibliography 266 to Peginterferon-a
Page 272: ISBN: 978-90-8559-069-9
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