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Chapter 2.1 36 Figure 4. Estimated mean decline (with 95% CI) of HBV DNA at the consecutive visit depending on the WHO grade of ALT elevation. P-values represent the differences between PEG-IFN (black) and placebo (grey). A fl are (ALT >5 x ULN, grade ≥3) was observed in 164 (10%) of 1588 available visits in the PEG-IFN group and in 230 (13%) of 1755 visits in the placebo group (p=0.01). In the PEG-IFN group, the estimated consecutive decline of HBV DNA was signifi cantly larger for visits accompanied by a fl are compared to those without (mean HBV DNA decline 0.54 log (95% CI [0.42; 0.66]) versus 0.08 log (95% CI [0.04; 0.12]), respectively; p
PEG-IFN versus placebo for HBeAg-positive CHB 37 the immunomodulatory agent PEG-IFN induced a larger HBV DNA decline compared with placebo. Furthermore, HBV DNA kinetics was infl uenced by the HBV genotype. Interestingly, the decline of HBV DNA was faster and stronger in patients with PEG-IFN then placebo-induced HBeAg clearance. Moreover, our data indicate that ALT fl ares during PEG-IFN therapy were associated with a stronger degree of HBV DNA decline compared with placebo. IFN-alfa has been used for the treatment of CHB for more than two decades and mainly has an immunomodulatory mode of action, although this cytokine also has a modest direct antiviral effect on HBV replication. 7 The attachment of a polyethyleneglycol (PEG) molecule to IFN improved its pharmacokinetic and pharmacodynamic properties and resulted in stronger suppression of HBV DNA compared with conventional IFN. 20 In our study, one year of PEG-IFN therapy resulted in 2.3 log decline of HBV DNA, which was signifi cantly higher than the spontaneous HBV DNA drop of 1.0 log in placebo-treated patients. Nevertheless, the observed effi cacy of PEG-IFN in suppression of HBV replication after 1 year of therapy was markedly inferior compared with third generation NA (6.2-6.9 log). 5-6 Eight genotypes have been identifi ed (A-H), of whom A-D are most prevalent. 21 Since the four major HBV genotypes (A-D) were represented in both groups, we had the opportunity to show that patterns of HBV DNA decline varied according to HBV genotype. PEG-IFN therapy did not result in a signifi cantly larger HBV DNA decline compared with placebo until week 20 for genotype B and week 16 for genotype A patients. In contrast, PEG-IFN showed a more profound HBV DNA decline already at week 4 and 12 for genotypes C and D, respectively. This fi nding may appear to be contradictious with the high chance of genotype A patients to respond to PEG-IFN therapy. 13 However, we have previously demonstrated that patients with a delayed HBV DNA decline during PEG-IFN therapy (almost half being genotype A patients) between week 4 and 32 had the highest chance of achieving HBeAg loss and HBsAg loss. 9 Given their distinct modes of action, different treatment endpoints are applied for the two treatment modalities. 3 While suppression of HBV DNA levels to undetectable levels is the only goal during NA therapy, loss of HBeAg is more frequently used as primary treatment endpoint for patients treated with PEG-IFN. HBeAg loss is a surrogate marker for immunological control over HBV and is associated with increased survival and a reduced risk of developing HCC. 22-23 Spontaneous loss of HBeAg occurred in 11% of placebo-treated patients after 1 year, which is similar to the rate of HBeAg loss reported in natural history studies. 11 The proportion of patients with HBeAg loss was almost three times higher in patients who received PEG-IFN, refl ecting its immunomodulatory properties. An important fi nding of this study was that a markedly larger drop of HBV
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 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 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 =
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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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