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Summary and conclusion 236 fi t model was 93%. There was only a small difference between the effi cacy parameter of the individual nonlinear fi tting and mixed-effect group fi tting on the biphasic expo- nential model. These data show that tenofovir has good effi cacy in blocking viral replication in HBV patients with lamivudine induced drug-resistant HBV mutants. Both models can be used to describe viral decay during tenofovir therapy. Treatment with PEG-IFN α-2b is effective for HBeAg-positive chronic hepatitis B although its mechanism of action remains unclear. In Chapter 4.3 early pharmaco- and viral kinetics in patients treated for 52 weeks with PEG-IFN α-2b with or without lamivudine were analysed. After 4 weeks of treatment, there was a median viral decline of 2.94 log10 copies/ml in those treated with PEG-IFN α-2b and lamivudine and only 0.45 log10 copies/ml in the PEG-IFN α-2b monotherapy group. Peak IFN levels were reached approximately one day after administration and subsequently declined exponentially consistent with a viral load rebound near to baseline levels at the end of the dosing period in most patients receiving PEG-IFN α-2b monotherapy. Modelling of pharmacoand viral kinetics data in this group revealed that viral load was minimal 3.6 days after PEG-IFN α-2b administration, the mean maximal and mean antiviral effectiveness was 70% and 48% with a mean infected cell loss rate of 0.07 per day, while no signifi cant biphasic decline was observed. We conclude that PEG-IFN α-2b induces a sustained response in a considerable number of patients despite limited direct antiviral activity during the fi rst weeks of antiviral therapy. CONCLUSION For the sustained response 6 months after end of peginterferon (PEG-IFN) treatment of HBeAg positive chronic hepatitis B patients a prediction model of baseline factors was designed. This model offers a practical tool to calculate the individual patient prediction to sustained response and can easily be used in clinical practice to select optimal candidates for PEG-IFN therapy. To update the individual response prediction during PEG-IFN therapy in HBeAG positive chronic hepatitis B patients with the repeated measurements of HBV DNA, assessed at each visit, dynamic prediction models were developed and different approaches were compared. The prediction of response to PEG-IFN obtained at baseline can be signifi - cantly improved with these new methods of dynamic prediction. These methods may be used to update the prognosis for the individual patients.
Summary and conclusion 237 Early stopping rules for treatment with PEG-IFN in HBeAg positive and negative chronic hepatitis B patients were designed to identify patients who do not benefi t from therapy. For the long-term effect of glycyrrhizin in chronic hepatitis C on the hepatocellular carcinoma-free period a Japanese cohort was studied. G-estimation was applied to offer a good solution to the problem of estimating the crude treatment effect when treatment is given on indication. For the early treatment response pharmacokinetic modelling of the viral load decline during the fi rst weeks of treatment with entecavir, tenofovir and PEG-IFN in chronic hepatitis B patients was studied. Available mathematical models were reformulated to fi t into the framework of non-linear mixed regression modelling of repeated measurement. The estimated parameters describe the pattern of viral decline the fi rst 4 weeks well. Both entecavir and tenofovir have a good effi cacy in blocking the viral replication. The viral kinetics for PEG-IFN was more complicated but despite the limited antiviral activity during the fi rst weeks PEG-IFN induces a sustained response in a considerable number of patients.
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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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Chapter 4.1 180 Table 1. Baseline c
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Chapter 4.1 182 Figure 1.
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Chapter 4.1 184 Figure 1. Viral dec
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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
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Page 201 and 202: log HBV DNA (copies/mL) 10.0 9.0 8.
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Page 213 and 214: INTRODUCTION HBV viral kinetics dur
Page 215 and 216: HBV viral kinetics during PEG-IFN 2
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Page 233 and 234: SUMMARY AND CONCLUSION Summary and
Page 235 and 236: Summary and conclusion 233 The mode
Page 237: Summary and conclusion 235 The infe
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,
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