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Summary and conclusion 232 Therapy with PEG-IFN results in sustained response in a minority of chronic HBV infected patients and has considerable side-effects. We combined data from individual patients (n=721) from the 2 largest global trials of HBeAg positive chronic hepatitis B to determine which patients are most likely to respond to PEG-IFN therapy. A sustained response was defi ned as HBeAg loss and HBV DNA
Summary and conclusion 233 The model strongly supports individual decision making on treatment discontinuation in patients with HBeAg positive chronic hepatitis B. It is recommended to stop PEG-IFN treatment by 24 weeks if HBV DNA declined less than 2log10. In Chapter 2.4 statistical methods are presented that enable dynamic updates of the prediction of a signifi cant clinical event. If biomarkers change during follow-up, the clinical prognosis changes along. Our aim was to incorporate longitudinal profi les of these markers in a dynamic model to repeatedly update the individual prediction of the event. The general concept is presented specifi cally in the setup when the clinical event has a bivariate outcome. First a direct approach is proposed, extending the usual logistic regression of baseline variables with the observed repeated measurements of the markers. The model is designed to update the prognosis of the outcome each time new information becomes available. Instead of entering the observed marker values the behaviour of the markers can also be used. Proceeding this way fi rst linear mixed modelling is applied to fi t the subject specifi c patterns of the markers and afterwards entering the random effects in the logistic regression while adjusting for the estimation error of the random effects. Secondly an indirect prediction method using multivariate mixed effects models is applied. The patterns of the markers are allowed to vary depending on the outcome variable. Thereafter, the empirical Bayes estimates are used to obtain posterior probabilities that are subsequently used to update the probability of the outcome variable each time new information becomes available. The different methods were applied to data on treatment of chronic hepatitis B patients. We conclude that the prediction of response obtained at baseline can be signifi cantly improved with all of the above mentioned methods and may be useful tools to update the prognosis for the individual patients. PEG-IFN alfa-2a results in a sustained response in a minority of HBeAg-negative chronic hepatitis B patients. In Chapter 2.5 the role of on-treatment quantitative HBsAg and HBV DNA levels in the prediction of sustained response in HBeAg-negative patients receiving PEG-IFN alfa-2a is assessed. HBV DNA and HBsAg were quantifi ed at baseline, during treatment and follow-up in the sera from 107 patients who participated in an international multicenter trial. Overall, 22% of patients achieved sustained response (serum HBV DNA
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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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Page 186 and 187: Chapter 4.1 184 Figure 1. Viral dec
Page 188 and 189: Chapter 4.1 186 suppression in seru
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
Page 197 and 198: Viral dynamics during tenofovir the
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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 233: SUMMARY AND CONCLUSION Summary and
Page 237 and 238: Summary and conclusion 235 The infe
Page 239: Summary and conclusion 237 Early st
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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