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Chapter 1 20 The extended non-linear PD-models describing HBV DNA during and after therapy can be fi tted with available statistical software in two ways: (1) separately for each patient with non-linear regression and (2) combined for all patients with mixed non-linear methods adding random effects to obtain individual patient curves. Pegylated interferon behaves different than NA’s. The drug is standard injected once a week for 48 weeks and the injection of one dose of pegylated interferon α-2b (PEG-IFN) results in a decrease of hepatitis B viral load, followed by a slow increase as the drug concentration in the blood declines (see fi gure). Until now it has been assumed that the effectiveness of the drug stayed constant between injections, but with the observed increase of viral load at the end of the week, new models are necessary to interpret and describe viral kinetics. When treated with PEG-IFN the biphasic model is not adequate to describe the pattern of the HBV DNA and more complex models are required. First the drug concentration is assessed with a one-compartment model and the results incorporated in the model describing the viral load during the fi rst week (fi gure 4). As a result, the PEG-IFN concentration, the viral load and also the effectiveness during one week after one injection can be fi tted. Assuming that the drug concentration and the viral load pattern after the fi rst injection repeats itself after each injection, the viral and drug kinetics are fi tted with a periodical continuation during the fi rst month. The fi tted concentration and viral decline allows for comparison of biologically relevant patient characteristics, such as body weight and HBV genotype, which may be important for future treatment protocols. Similar to the analysis of the pharmacokinetic of the NA either non-linear regression analysis per subject or non-linear mixed regression analysis on all subjects with random effects on the parameters can be applied. PEG-IFN concentration viral load 0 1 2 3 4 5 6 7 days Figure 4. The patterns of interferon concentration and viral load after on injection of PEG-IFN.
AIMS Introduction 21 In this thesis we aim to develop a prediction model of sustained response to PEG- IFN treatment in patients with HBeAg positive chronic hepatitis B. We will introduce new dynamic model methods and improve existing ones to update the prediction of response to treatment when longitudinal markers are available. These methods are applied to chronic hepatitis B patients receiving PEG-IFN therapy. The methods are used as a guide to identify patients, who do not benefi t from PEG-IFN, and whom may be advised to discontinue therapy as early as possible in the treatment schedule. Furthermore, we aim to investigate the long-term effect of glycerrhicin treatment on the development of HCC in chronic hepatitis C patients, who were all non-responders to interferon treatment. A statistical model that corrects for bias when treatment is given depending on the state of the disease is applied. Finally we aim to describe the early treatment effects of nucleoside/nucleuotide analogous and PEG-IFN in HBeAg positive chronic hepatitis B patients with pharmcokinetic and pharmcodynamic models.
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 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: Dynamic prediction of response to P
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Dynamic prediction of response to P
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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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Chapter 4.1 186 suppression in seru
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Chapter 4.1 188 13. Seifer M, Hamat
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Cha pter 4.2 Viral dynamics during
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INTRODUCTION Viral dynamics during
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Viral dynamics during tenofovir the
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log HBV DNA (copies/mL) 10.0 9.0 8.
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Chap ter 4.3 Modelling of early vir
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INTRODUCTION HBV viral kinetics dur
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HBV viral kinetics during PEG-IFN 2
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SUMMARY AND CONCLUSION Summary and
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Summary and conclusion 233 The mode
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Summary and conclusion 235 The infe
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Summary and conclusion 237 Early st
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SAMENVATTING EN CONCLUSIE Samenvatt
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Samenvatting en conclusie 243 Er we
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Samenvatting en conclusie 245 Data
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Samenvatting en conclusie 247 Behan
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Dan kwoord
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Dankwoord 254 Lieve Marion en Margr
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BIBLI OGRAPHY Bibliography 257 1. K
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Bibliography 259 Interferon-ribavir
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Bibliography 261 Long term clinical
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Bibliography 263 Flares in chronic
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Bibliography 265 Genetic characteri
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Bibliography 267 106. Reijnders JG,
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