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Chapter 2.3 64 only provides the best prediction and discrimination, but also is easy to apply in clinical practice. METHODS Patients and study design The data origin from the PEG-IFN monotherapy arm of the HBV 99-01 study, 4 136 patients received PEG-IFN α-2b 100μg per week for 32 weeks, followed by 20 weeks of PEG-IFN 50μg per week up to a total of 52 weeks. Subjects were subsequently followed up for 26 weeks. Patients randomized to PEG-IFN and lamivudine combination therapy were not included in the current study, because HBV-DNA kinetics during combination therapy showed a different pattern in comparison with PEG-IFN monotherapy; generally a steep decline during therapy followed by a post-treatment relapse. 4 Patients were eligible to participate in the HBV99-01 study if they had been HBsAg positive for at least 6 months, were HBeAg positive, anti-HBe negative, had elevated serum ALT levels 2-10 times the upper limit of normal (ULN), had serum HBV DNA >1.0 x 105 copies/ml and had abnormalities on liver biopsy consistent with the presence of chronic hepatitis B. Exclusion criteria included decompensated liver disease, antiviral therapy within 6 months prior to randomization, viral co-infections (hepatitis C virus, hepatitis delta virus or human immunodefi ciency virus), or pre-existent neutropenia or thrombocytopenia. For the current study sustained response (SR) was defi ned as the combination of clearance of HBeAg from serum and HBV DNA
Statistical analysis Dynamic prediction of response to PEG-IFN 65 In a previous study baseline factors infl uencing the SR-rate were described in detail and a prediction model (PEG-IFN HBV Treatment Index) was developed which provides a subject specifi c prediction of SR. 9 Baseline factors associated with SR were: HBVgenotype, age, gender, baseline HBV DNA (copies/ml, log10 ), ALT (loge ) and previous treatment with IFN. In the current study the PEG-IFN HBV Treatment Index was fi rst calculated for each subject and was used throughout the analysis as an offset, i.e. a subject specifi c starting value with the corresponding regression coeffi cient set to 1. The baseline prediction of SR, Pbaseline , was hereafter updated with data on HBV DNA and ALT during therapy applying logistic regression analysis techniques. For comparison purposes two model approaches were considered: the fi rst was updating the model at each visit with the new information, resulting in 8 models; one for each visit (week 4, 8, 12, 16, 20, 24, 28 and 32), the second an overall generalized estimating equations model11 using information of all visits and adding the visit time as a continuous factor. The latter model approach allows repeated measurement data and hence reduces the 8 models of the fi rst approach into one overall model. Technically, each visit per patient is treated as an observation and the model then corrects for the fact that each patient contributes with 8 visits. This model is referred to as the dynamic logistic regression model and is sometimes called a pooled logistic regression model. 12-13 The treatment duration (visit time) was added as a linear variable to the model, a restricted cubic spline was used to check the linearity assumption. The effect of the crude HBV-DNA (log10 ) as well as the effect of HBV DNA log10-decline compared to baseline (= HBV DNAlog10 at time t – HBV DNAlog10 at baseline) was studied. ALT was entered in the models as measured and also transformed logarithmically. Interactions between HBV DNA and ALT with treatment duration (visit time) and with HBV-genotype were considered. Changes in HBV DNA and ALT measurements in prior visits were also studied. Discrimination between the different models was quantifi ed by the c-statistics, which is the area under the receiver operating curve. The best model-fi t was assessed comparing these (the higher the better) and the Akaike’s Information criteria (AIC) or the quasi-likelihood information criteria (QIC) for the generalized estimating equations method (the lower the better). Cross validation, leaving one out, was performed to establish overall performances of the models. Finally a cut-off value of HBV DNA during treatment was sought to fi nd a clinical useful guiding rule for (dis)continuation of therapy. The optimal cut-off point was established in a multivariable setting14 including the baseline PEG-IFN HBV Treatment Index: explanatory plots were evaluated, followed by the maximum chi-square approach. Subsequently the cut-off point search was repeated in 500 bootstrap samples for validation of the
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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
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 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: INTRODUCTION Dynamic prediction of
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 =
Page 89 and 90: Dynamic prediction of response usin
Page 101 and 102: Table 1. Results of different stopp
Page 105: Dynamic prediction of response usin
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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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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