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Chapter 4.1 178 at day 1 at t =0 and 8 h, at day 2 at t =24 and 32 h and at days 3, 4, 7, 10, 14, 21 and 28. Follow-up after withdrawal of therapy was documented with HBV DNA measurements at days 29, 30, 31, 32, 35, 38, 42, 49, 56 and months 3, 4, 5 and 6. Selection of patients Patients were screened for eligibility on two occasions which had to be at least 2 weeks apart. Eligible patients included men and woman older than 18 years with a chronic hepatitis B infection as documented by HBsAg positivity in the serum for over 24 weeks before the start of therapy and HBV DNA of .20 Meq/ml measured with the Chiron hybridization bDNA assay. Patients had to have a compensated liver disease as documented by laboratory and clinical evaluation. Both HBeAg positive and HBeAg negative patients could be included. Previous antiviral therapy with alpha interferon, other nucleoside analogues or immunosuppressive therapy was permitted but these drugs had to be withdrawn 6 months before the start of therapy in this trial. Patients were excluded if they were co-infected with the hepatitis C virus, the hepatitis D virus or the human immunodefi ciency virus (HIV), had another concomitant liver disease, had a history of pancreatitis, or had a history of any form of chronic headaches. Both male and female patients had to practice a reliable method of contraception. Assays HBV DNA was quantifi ed with a Digene Hybrid Capture tube liquid hybridization assay (calibrated on the EUROHEP standard19 ). If HBV DNA declined below 1.5 x 106 geq/ml (the limit of detection of this liquid hybridization assay) during therapy, it was reassessed with the quantitative PCR (Roche, Amplicor Diagnostics, Almere, The Netherlands, calibrated on the EUROHEP standard; lower limit of detection of 1000 geq/ml). HBV polymerase mutant analysis was performed with the INNO-LiPA strip (Innogenetics, Ghent, Belgium). 20 Modelling of viral decline A bi-phasic model previously applied for viral decline in chronic hepatitis C patients during alpha interferon therapy was used to describe viral decay, by means of viral dynamic parameters, during entecavir therapy. 21 In short, viral decline in this model is described by the following equation:
V(t) = V 0 { A exp (-λ 1 t ) + (1-A) exp(-λ 2 t)} Viral dynamics during and after entecavir therapy 179 where λ 1 is the slope of the fi rst phase of viral decline, λ 2 is the slope of the second phase of viral decline, A = (εc – λ 2 ) / (λ 1 -λ 2 ), λ 1/2 = ½ { (c + δ) ± [(c – δ) 2 + 4 (1 – ε) (1-η)cδ] ½ }, V0 is the initial viral load, t is time, d is the death rate of productively infected cells, c is the clearance rate of the free virus, ε is the effectiveness of entecavir in blocking virion production from infected cells, and η is the effectiveness of entecavir in blocking de novo infection of susceptible cells. The bi-phasic return of virus after withdrawal of therapy was described by application of a similar bi-phasic model as an inverse image of the bi phasic decline in viral load during antiviral therapy: V(t) = V 0 / { A exp (λ 1 t ) + (1-A) exp( - λ 2 t)} Statistics Patients were fi tted individually. Due to the small sample size, all patients on entecavir therapy were evaluated as one group. Non-linear modelling was used to fit both the bi-phasic model and the inverse bi-phasic model, executed in the PROC NLIN in SAS 6.12. The Mann–Whitney test was used to compare the difference between dose groups in rebound of viral replication after withdrawal of entecavir. The Kruskal–Wallis test was applied to calculate the difference in the dose of entecavir with regard to parameters of viral return. Signifi cant difference was achieved if p
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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
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 and 136: Table 2. Time dependent Cox regress
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
Page 182 and 183: Chapter 4.1 180 Table 1. Baseline c
Page 184 and 185: Chapter 4.1 182 Figure 1.
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
Page 201 and 202: log HBV DNA (copies/mL) 10.0 9.0 8.
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
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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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