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Chapter 2.4 100 mixed effect models are used to calculate posterior probabilities of treatment response. The methods result in a dynamic individualized update of the prediction of response. The approaches were applied to data on the peginterferon treatment of chronic hepatitis B and their predictive performance were compared. The modelling approaches are very flexible in that they allow inclusion of more than one biomarker. The biomarkers may be described by complex mixed modelling if necessary, however one has to be aware not to overfit. For the indirect approach splines were used to describe the decline of viral load and ALT of a hepatitis B patient. Instead of linear mixed models, non-linear mixed models might be considered or ordinal distributed biomarkers might be modelled. Fieuws et al. 15 studied multivariate longitudinal profiles allowing a joint distribution of the random effects. They further16 used non-linear mixed of longitudinal profiles designing a set of classification rules. Komárek et al. 17 relaxed the normal assumption of the random effects biomarkers studying a heteroscedastic multivariate normal mixture for the random effects. The direct approach with the observed biomarkers enables as the term say direct inclusion of any marker, ordinal or non-linear as well as inclusion of interaction over time. The indirect method and the direct method, which uses the parameters describing the patterns of the biomarkers over time, have one small drawback being the computational programming, which is elaborate and time consuming and new estimates need to be established for a future subject before predictions can be calculated. In contrast, standard statistical procedures are available for the direct method of the observed markers and a prediction model can be expressed7 which directly can be applied to a future subject. This model though, has limited memory of the patterns of the biomarkers (depending on inclusion of the markers at previous visits), which can be an advantage in the situation were the last observed biomarkers predicts the outcome well, and a disadvantage if the prediction of the outcome is better associated with the total pattern of the markers. In the situation of response of peginterferon treatment the last observed load predicts just as well as the estimated decline. In our situation we studied a binary outcome. Brant and Morrell used the indirect method in several clinical studies. 9,10,18 Especially they focused on longitudinal measurements of the prostate specific antigen to predict prostate cancer. They observe more than two outcome categories (ex. no cancer, low risk, high risk) and constructs an elegant stopping rule depending on posterior probabilities over time. Our clinical situation is simpler, but therefore also allows us to easily study different stopping rules. In case of more than two outcome categories a generalized logit
Dynamic prediction of response using longitudinal profi les 101 approach to apply the direct approach would be necessary. Maruyama8 used the direct approach with an application to data on smoking cessation and the relation with longitudinal measurements of carving. They first estimated the decline of carving over time and then included the estimated individual slope in the logistic regression of smoking cessation. Their situation is very similar to ours. We adapted their method but reformulated it to a dynamic updating process. In summary several dynamic prediction methods to update the prognosis for the individual patient are available and a significant improvement of the baseline prediction can be obtained. The direct approach had the best predictive performance in our application, while the indirect approaches behaved more like classifiers. Furthermore, the direct method is easy for practical application while standard statistical software is not yet available for the other approaches. Finally the methods are flexible and offer a new generation of prediction models.
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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
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 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: 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
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 151 and 152: Discriminant analysis using a MLMM
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where wi,k(y i, θ) = (k =1,...,K).
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Discriminant analysis using a MLMM
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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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