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Chapter 3.1 136 References 1. Poynard T, Bedossa P, Opolon P. Natural history of liver fi brosis progression in patients with chronic hepatitis C. The OBSVIRC, METAVIR, CLINIVIR, and DOSVIRC groups. Lancet 1997;349(9055):825-32 2. Manns MP, McHutchison JG, Gordon SC, et al. Peginterferon alfa-2b plus ribavirin compared with interferon alfa-2b plus ribavirin for initial treatment of chronic hepatitis C: a randomised trial Lancet 2001;358(9286):958-65. 3. Hadziyannis SJ, Sette H, Jr., Morgan TR, et al. Peginterferon-alpha2a and ribavirin combination therapy in chronic hepatitis C: a randomized study of treatment duration and ribavirin dose Ann Intern Med 2004;140(5):346-55. 4. Veldt BJ, Saracco G, Boyer N, et al. Long term clinical outcome of chronic hepatitis C patients with sustained virological response to interferon monotherapy. Gut 2004;53(10):1504-8. 5. Shiffman ML, Hofmann CM, Contos MJ, et al. A randomized, controlled trial of maintenance interferon therapy for patients with chronic hepatitis C virus and persistent viremia Gastroenterology 1999;117(5):1164-72. 6. Hoofnagle JH, Ghany MG, Kleiner DE, et al. Maintenance therapy with ribavirin in patients with chronic hepatitis C who fail to respond to combination therapy with interferon alfa and ribavirin Hepatology 2003;38(1):66-74. 7. van Rossum TG, Vulto AG, Hop WC, et al. Glycyrrhizin-induced reduction of ALT in European patients with chronic hepatitis C Am J Gastroenterol 2001;96(8):2432-7. 8. Fattovich G, Giustina G, Degos F, et al. Effectiveness of interferon alfa on incidence of hepatocellular carcinoma and decompensation in cirrhosis type C. European Concerted Action on Viral Hepatitis (EUROHEP) J Hepatol 1997;27(1):201-5. 9. Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias Epidemiology 2004;15(5):615-25. 10. Yoshida H, Tateishi R, Arakawa Y, et al. Benefi t of interferon therapy in hepatocellular carcinoma prevention for individual patients with chronic hepatitis C. Gut 2004;53(3):425-30. 11. Okanoue T, Itoh Y, Kirishima T, et al. Transient biochemical response in interferon therapy decreases the development of hepatocellular carcinoma for fi ve years and improves the long-term survival of chronic hepatitis C patients. Hepatol Res. 2002;23(1):62-77. 12. Ikeda K, Saitoh S, Arase Y, et al. Effect of interferon therapy on hepatocellular carcinogenesis in patients with chronic hepatitis type C: A long-term observation study of 1,643 patients using statistical bias correction with proportional hazard analysis Hepatology 1999;29(4):1124-30. 13. Moriyama M, Matsumura H, Aoki H, et al. Decreased risk of hepatocellular carcinoma in patients with chronic hepatitis C whose serum alanine aminotransferase levels became less than twice the upper limit of normal following interferon therapy. Liver Int. 2005;25(1):85-90. 14. Grodstein F, Clarkson TB, Manson JE. Understanding the divergent data on postmenopausal hormone therapy N Engl J Med 2003;348(7):645-50.
Glycyrrhizin for IFN-non responders 137 15. Witteman JC, D’Agostino RB, Stijnen T, et al. G-estimation of causal effects: isolated systolic hypertension and cardiovascular death in the Framingham Heart Study Am J Epidemiol 1998;148(4):390-401. 16. Robins JM, Blevins D, Ritter G, et al. G-estimation of the effect of prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of AIDS patients Epidemiology 1992;3(4):319-36. 17. Arase Y, Ikeda K, Murashima N, et al. The long term effi cacy of glycyrrhizin in chronic hepatitis C patients Cancer 1997;79(8):1494-500. APPENDIX The method of G-estimation by J.M.Robins offers a solution to estimate the causal effect of the time dependent glycyrrhizin-treatment on the development of HCC, in the presence of a time-dependent covariate ALT that is both a confounder and an intermediate variable. G-estimation of the parameter of a nested structural model estimates the expansion or contraction parameter ψ of the time to event (HCC) due to the exposure to glycyrrhizin treatment. If, for instance the exponent of -ψ (referred to as E in the text) = 1.20 the time to HCC is expanded by 20%, corresponding with a benefi cial effect. Fundamental for this approach is the assumption of no unmeasured confounders. This means that all covariates infl uencing both the decision to use glycyrrhizin and the HCCfree survival time should be measured. That means that given the covariates, the decision to start treatment is independent of the patient’s (possibly counterfactual) HCC-free survival time under any treatment regime. A pooled logistic regression analysis over all visits was applied, with glycyrrhizin therapy at visit k as outcome. This means that each subject contributed with multiple observations, one for each visit, until development of HCC or censoring. Covariates considered for inclusion in the model are baseline factors (age, sex, fi brosis stage, ALT and gamma glutamyltransferase at the start of the study) and the covariate history before visit k (ALT, glycyrrhizin treatment and concomitant medication at the two visits prior to visit k). Furthermore the number of weeks since the prior visit and the number of weeks since the start of the study were included in the model. The parameter ψ is g-estimated by extending the logistic model with sets of imaginary (counterfactual) HCC-free survival times, had glycyrrhizin-treatment never been given. Weights have been calculated to adjust for patients who are lost to follow-up or who are censored at a second interferon-based treatment. Data description and annotation: Ti = Observed failure time for subject i.
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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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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
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 and 136: Table 2. Time dependent Cox regress
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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 180 and 181: Chapter 4.1 178 at day 1 at t =0 an
Page 182 and 183: Chapter 4.1 180 Table 1. Baseline c
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Page 186 and 187: 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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