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Chapter 3.1 134 DISCUSSION The aim of this study was to determine the long-term clinical outcome of chronic hepatitis C patients who did not respond to interferon monotherapy and to evaluate the effect of glycyrrhizin treatment on the incidence of HCC in this group of patients. During follow-up 107 patients developed HCC. This is well in accordance with data published by Yoshida et al., who presented the rates of development of HCC by age, sex and fi brosis stage in their population of non-sustained responders. Applying these rates to our dataset would lead to an expected number of 117 HCCs (95% CI 99-139) during 6.1 years of follow-up. 10 In our cohort the overall yearly incidence of HCC was 1.6%. Previous large cohort studies found a yearly incidence of 0.3 to 2.7% per year in Japanese non sustained responders to interferon treatment. 10-12 In the literature, lower rates of HCC development are described in patients who relapsed after an initial response and in patients with persistently low ALT levels. 13 Similarly, in our cohort, patients with lower baseline ALT levels had a smaller probability of developing HCC. As chronic hepatitis C only progresses slowly, it is hard to evaluate the effi cacy of treatment on clinical outcomes like mortality and development of HCC in randomized controlled trials. Therefore, “best” information should be derived from cohort studies. However, cohort studies are only reliable if the drop-out rate is low compared to the events. In retrospective cohort studies the risk of introducing bias is even larger. Incomplete capture of early clinical events, confounding bias and compliance bias have been described as possible confounders in retrospective studies. 14 In large randomized trials this problem is usually avoided, as unmeasured confounders are likely to be equally divided over the groups by randomization. We executed this retrospective cohort analysis with great care to avoid these biases. Incomplete capture of clinical events could not play a role in our analysis as the development of HCC was monitored during the whole follow-up period. Secondly, confounding bias may have played a role as raised ALT-levels increased both the chance of receiving glycyrrhizin treatment as the risk of developing HCC. Sophisticated statistical analyses were used to correct for this confounder. 9,15,16 Finally, compliance bias may have played a role in this study, as patients who are willing to attend the hospital several times a week for intravenous injections of glycyrrhizin are possibly also more likely to adhere to other protective types of behavior. However, the fact that the follow-up of patients who did not receive glycyrrhizin was similar to those who did, suggests that they were equally compliant in their hospital visits. A previous study on the effect of glycyrrhizin on clinical outcome did show a signifi cant protective effect on development of HCC. 17 In our study we refi ned the methodology by using an intention-to-treat approach. All patients who received glycyrrhizin were
Glycyrrhizin for IFN-non responders 135 included, even those who were treated for a short time. In this way we tried to avoid excluding patients who stopped their glycyrrhizin early because they died of HCC. In the present study, we fi rst used multiple regression analysis to assess the effect of glycyrrhizin. Overall, there was no signifi cant effect, but in patients with fi brosis stage 3 and 4 there was a trend to a protective effect on development of HCC. An intention to treat analysis of all patients treated with glycyrrhizin showed that patients responding by decreased ALT levels had a signifi cantly lower probability of developing HCC. A G-estimation was performed to address the problem of confounding by ALT levels. The latter analysis failed to show an overall benefi cial effect of glycyrrhizin, but in patients with fi brosis stage 3 or 4 at the start of follow-up there was a trend towards a protective effect. In conclusion, this study provides some evidence that interferon non-responder patients with chronic hepatitis C and fi brosis stage 3 or 4 may have a reduced incidence of HCC if glycyrrhizin therapy leads to normalization of ALT levels.
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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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Page 108 and 109: Chapter 2.5 106 ABSTRACT Peginterfe
Page 110 and 111: Chapter 2.5 108 and placebo daily.
Page 112 and 113: Chapter 2.5 110 for patients with a
Page 114 and 115: Chapter 2.5 112 Mean HBV DNA declin
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Page 118 and 119: Chapter 2.5 116 hepatocellular carc
Page 120 and 121: Chapter 2.5 118 13. Werle-Lapostoll
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Page 127 and 128: Chap ter 3.1 Long-term clinical out
Page 129 and 130: INTRODUCTION Glycyrrhizin for IFN-n
Page 131 and 132: Statistics Glycyrrhizin for IFN-non
Page 133 and 134: Glycyrrhizin for IFN-non responders
Page 135: Table 2. Time dependent Cox regress
Page 139 and 140: Glycyrrhizin for IFN-non responders
Page 143 and 144: Cha pter 3.2 Discriminant analysis
Page 145 and 146: Introduction Discriminant analysis
Page 147 and 148: Discriminant analysis using a MLMM
Page 149 and 150: Estimation Discriminant analysis us
Page 153 and 154: where wi,k(y i, θ) = (k =1,...,K).
Page 171 and 172: Appendix Discriminant analysis usin
Page 175: Cha pter 4 Statistical models of ea
Page 178 and 179: Chapter 4.1 176 ABSTRACT Nucleoside
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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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