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Chapter 2.3 68 Table 2. Logistic regression analysis of sustained response. Extension of the baseline model with ontreatment factors. cross validated Week OR 95%CI p-value c-statistics c-statistics Baseline 0 0.846 HBV DNA log10 4 1.04 (0.61 ; 1.78) 0.881 0.854 0.827 8 1.02 (0.65 ; 1.61) 0.927 0.852 0.828 12 1.07 (0.74 ; 1.56) 0.716 0.852 0.836 16 1.25 (0.92 ; 1.69) 0.147 0.856 0.842 20 1.39 (1.04 ; 1.85) 0.025 0.860 0.846 24 1.39 (1.06 ; 1.82) 0.017 0.857 0.848 28 1.43 (1.09 ; 1.89) 0.010 0.864 0.852 32 1.62 (1.19 ; 2.21) 0.002 0.895 0.888 Overall # 1.33 (1.05 ; 1.68) 0.016 decline 4 1.37 (0.58 ; 3.27) 0.477 0.857 0.839 HBV DNA log10 8 1.16 (0.60 ; 2.25) 0.656 0.854 0.834 12 1.18 (0.74 ; 1.89) 0.488 0.855 0.839 16 1.36 (0.97 ; 1.90) 0.076 0.857 0.845 20 1.49 (1.09 ; 2.03) 0.012 0.864 0.853 24 1.47 (1.10 ; 1.96) 0.010 0.866 0.854 28 1.56 (1.14 ; 2.13) 0.005 0.870 0.859 32 1.72 (1.23 ; 2.41) 0.001 0.898 0.891 Overall # 1.47 (1.13 ; 1.90) 0.004 # Estimated OR by dynamic logistic regression model for fi xed number of treatment weeks Dynamic logistic regression model of on-treatment factors Using the dynamic logistic regression model, signifi cant effects were found of crude HBV DNA levels over time (p=0.016, model A) and of decline in HBV DNA levels over time (p= 0.004, model B); the latter providing better fi t and discrimination (model A: QIC = 576.0, mean c-statistics = 0.860; model B: QIC=564.1, mean c-statistics=0.863). In both models duration of treatment (in weeks) was signifi cantly related to response (p=0.027 model A and p=0.011 model B). The effects of ALT levels were not signifi cant (ALT: p=0.406, model C and ln(ALT): p=0.558, model D). The dynamic logistic regression equation of prediction of SR at a specifi c week, PSR, week , is estimated by the crude observed HBV DNA, model A:
Dynamic prediction of response to PEG-IFN 69 logit(PSR, week ) = 2.041 - 0.026 * week - 0.286 * HBV DNAlog10 + PEG-IFN HBV Treatment Index, or by the log10 decline in HBV DNA compared to HBV DNA baseline, model B: logit (PSR, week ) = -0.529 - 0.034 * week - 0.382 * (HBV DNAlog10 - HBV DNAlog10 (baseline)) + PEG-IFN HBV Treatment Index. The overall estimated OR for HBV DNA levels and HBV DNA decline calculated by the dynamic logistic regression are presented in table 2, for weeks of therapy held fi xed. In table 3, the dynamic logistic regression models were used to estimate the effects of a 1 log10 decline of HBV DNA or 1xULN increase in ALT after 4 weeks of treatment for the individual patient. The effects of HBV DNA decline at 4, 12 and 24 weeks are plotted in fi gure 3. The application of non-linear functions (splines) of time, HBV DNA or ALT did not improve the fi t of the model nor did adding interaction terms with HBV-genotype or time. However, a trend was observed of an increasing effect of HBV DNA decline per week: p=0.018 for the interaction term week*HBV DNAlog10 , when added to model A Figure 3. Results of the dynamic logistic regression model. Estimated OR for sustained response according to HBV DNA decline after 4, 12 and 24 weeks of treatment compared to baseline.
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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
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 and 66: INTRODUCTION Dynamic prediction of
Page 67 and 68: Statistical analysis Dynamic predic
Page 69: Dynamic prediction of response to P
Page 73 and 74: Dynamic prediction of response to P
Page 75 and 76: 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
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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
Page 116 and 117: Chapter 2.5 114 peginterferon and n
Page 118 and 119: 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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