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E-raamat: Big Data Analytics in Oncology with R

(University of Leicester, UK)
  • Formaat: 270 pages
  • Ilmumisaeg: 29-Dec-2022
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781000823691
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Big Data Analytics in Oncology with RSuurem pilt
  • Formaat: 270 pages
  • Ilmumisaeg: 29-Dec-2022
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781000823691
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Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area.

Features:





Covers gene expression data analysis using R and survival analysis using R Includes bayesian in survival-gene expression analysis Discusses competing-gene expression analysis using R Covers Bayesian on survival with omics data

This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.
Preface xiii
Author xv
1 Survival Analysis
1(24)
1.1 Introduction
1(2)
1.2 Hazard Function
3(2)
1.3 Censoring
5(1)
1.4 Study Design and Survival Analysis
6(2)
1.5 Survival Analysis Objective
8(1)
1.6 Non-Parametric Approach for Survival Analysis
9(1)
1.7 Log-Rank Test
9(1)
1.8 Median Follow-Up Time Calculation
10(1)
1.9 Survival Data
10(4)
1.9.1 Multiple event-time data
11(1)
1.9.2 Multivariate survival data
11(1)
1.9.3 Univariate survival models
12(1)
1.9.4 Multivariate survival models
12(1)
1.9.5 Doubly interval-censored survival data
13(1)
1.9.6 Frequentist approach
13(1)
1.10 Bayesian Prior Assumptions for Survival Analysis
14(1)
1.10.1 Prior in survival analysis
15(1)
1.10.2 Dirichlet process prior
15(1)
1.11 Illustration Using R
15(10)
2 Cox Proportional Survival Analysis
25(14)
2.1 Introduction
25(1)
2.2 Cox Proportional Hazard
25(2)
2.2.1 Hazard ratio
26(1)
2.2.2 Partial likelihood function
26(1)
2.2.3 Wald score and Likelihood ratio tests
27(1)
2.3 Cox Proportional Diagnostics Test
27(2)
2.3.1 Cox-snell residual
28(1)
2.3.2 Martingale residual
29(1)
2.4 Mean and Median Survival Time
29(1)
2.5 Stratified Cox Proportional Hazard Test
30(1)
2.6 Schoenfeld Residuals
30(1)
2.7 Extended Cox Regression Model
31(1)
2.8 Illustration Using R rr-r
32(7)
2.8.1 Univariate Cox proportional hazard in high dimensional data
32(5)
2.8.2 Expectation-maximization algorithm
37(2)
3 Parametric Survival Analysis
39(10)
3.1 Introduction
39(1)
3.2 Regularized Survival Analysis
40(1)
3.3 Gaussian Prior and Ridge Regression
41(1)
3.4 Laplacian Prior and Lasso Regression
42(1)
3.5 Parameteric Survival Analysis
42(1)
3.6 Different Distribution
43(2)
3.6.1 Exponential distribution
43(1)
3.6.2 Weibull model
43(1)
3.6.3 Gamma distribution
44(1)
3.7 Maximum Likelihood Estimation
45(1)
3.8 Illustration Using R
45(4)
4 Competing Risk Modeling in High Dimensional Data
49(24)
4.1 Introduction
49(3)
4.2 Survival and Competing Risk Model
52(2)
4.3 The Competing Risk Models
54(4)
4.4 Aalen's Additive Hazards Model
58(1)
4.5 Bayesian Formulation
59(2)
4.6 The Lasso Method
61(1)
4.7 Metropolis Algorithm
62(1)
4.8 Deviance Information Criterion and Akaike Information Criteria
63(1)
4.9 Illustration with Example Data
64(4)
4.10 Bayesian for Competing Risk Analysis Illustration Using R
68(5)
5 Biomarker Thresholding in High Dimensional Data
73(28)
5.1 Introduction
73(1)
5.2 Statistical Methodology for Biomarker Thresholding
74(1)
5.3 Thresholding for Repeatedly Measured Biomarker
75(2)
5.4 Statistical Model
77(3)
5.5 Repeteadly Measured Biomarker Thresholding
80(2)
5.6 Biomarkar Thresholding Determination
82(5)
5.7 Illustration Using R
87(5)
5.8 Data Illustration
92(3)
5.9 Classification and Regression Tree Analysis in Biomarker Thresholding
95(6)
6 High Dimensional Survival Data Analysis
101(20)
6.1 Introduction
101(1)
6.2 Challenges in High Dimensional Data
102(1)
6.3 Variable Selection in High Dimensional Data
103(3)
6.3.1 Lasso selection
103(1)
6.3.2 Elastic net
104(1)
6.3.3 Cox regression
105(1)
6.4 Survival and High Dimensional Data
106(1)
6.5 Covariance Structure in High Dimensional Data
107(1)
6.6 Variable Selection
108(2)
6.6.1 Bayesian information criterion
108(1)
6.6.2 Deviance information criterion
109(1)
6.6.3 Predictive criteria
109(1)
6.7 Illustration Using R
110(11)
6.7.1 Data flietration with batches
113(8)
7 Frailty Models
121(16)
7.1 Introduction
121(3)
7.2 Proportional Hazard Model
124(1)
7.2.1 Single event frailty model
124(1)
7.2.2 Clustered wise frailty
125(1)
7.2.3 Recurrent events
125(1)
7.3 Frailty Model
125(3)
7.3.1 Frailty distribution
126(1)
7.3.2 Univariate frailty model
127(1)
7.3.3 Correlated frailty model
127(1)
7.3.4 Clustered survival data
127(1)
7.3.5 Covariates
128(1)
7.4 Illustration
128(2)
7.4.1 Diabetic retinopathy study
128(1)
7.4.2 Canadian health and aging study
129(1)
7.5 Frailty Model in Packages
130(1)
7.6 Frailty and Biomarker
131(1)
7.7 Illustration Using R
132(5)
8 Time-Course Gene Expression Data Analysis
137(24)
8.1 Introduction
137(2)
8.2 Microarray Data
139(1)
8.2.1 Source of microarray data
139(1)
8.2.2 Gene expression and microarray data
139(1)
8.3 Model for Microarray Data
140(1)
8.3.1 Bayesian state space modeling
140(1)
8.4 Different Covariance Structure
141(1)
8.4.1 Variance Components (VC) covariance structure
141(1)
8.4.2 First order Auto Regressive AR(1)
141(1)
8.4.3 Unstructured (US)
142(1)
8.5 Model Development
142(14)
8.5.1 Gene selection procedure
144(1)
8.5.2 Model fitting and prediction
145(1)
8.5.3 Parameter estimation
145(3)
8.5.4 Prediction of gene expression
148(3)
8.5.5 Study design
151(1)
8.5.6 Longitudinal over cross sectional gene expression
152(1)
8.5.7 Short time course experiment
153(1)
8.5.8 Replication
154(1)
8.5.9 Identifying the genes of interest
155(1)
8.5.10 ANOVA and F-statistic
155(1)
8.5.11 Moderation
155(1)
8.5.12 Gene-specific moderation
156(1)
8.6 Likelihood-Based Approach
156(1)
8.7 Empirical Bayes Approach
157(1)
8.8 Illustration Using R
158(3)
9 Survival Analysis and Time-course Data Analysis
161(24)
9.1 Introduction
161(12)
9.1.1 Cox proportional hazard model and filtration
163(5)
9.1.2 Multivariate joint model
168(1)
9.1.2.1 The mixed model
168(1)
9.1.2.2 The Cox model
169(1)
9.1.2.3 The Joint model
169(1)
9.1.3 Bayesian approach in joint longitudinal and survival modeling
170(1)
9.1.4 Description of data
171(2)
9.2 Model Fitting
173(2)
9.3 Results
175(6)
9.3.1 The linear mixed effect model
175(2)
9.3.2 The Cox model
177(2)
9.3.3 The joint longitudinal and survival model
179(1)
9.3.4 Model validation
180(1)
9.4 Discussion
181(4)
10 Features Selection in High Dimensional Time to Event Data
185(40)
10.1 Introduction
185(1)
10.2 Different Methods in Feature Selection
185(3)
10.2.1 Filter method
186(1)
10.2.2 Wrapper method
186(1)
10.2.3 Embedded method
187(1)
10.2.4 Other methods
187(1)
10.2.5 Limitations of existing methods
187(1)
10.2.6 Re-sampling algorithm
188(1)
10.3 Distribution of Weight in Feature Selection
188(5)
10.3.1 Re-sampling feature selection steps
191(2)
10.4 Data Methodology
193(5)
10.5 Weight Function and The Re-sampling Algorithm
198(2)
10.6 High Dimensional Time to event
200(4)
10.6.1 Time to event data
201(1)
10.6.2 Gene expression data
201(1)
10.6.3 Machine learning algorithms
202(1)
10.6.4 Machine learning codes with high dimensional data
203(1)
10.7 Methodological Framework
204(8)
10.7.1 Feature selection
204(4)
10.7.2 Frailty analysis
208(1)
10.7.3 Classification using CPH model in time-course data
208(2)
10.7.4 Sequential threshold selection
210(2)
10.8 Ilustration Using R
212(9)
10.8.1 Implementation details
213(1)
10.8.1.1 Feature selection using CPH learner model
213(1)
10.8.1.2 Feature selection using kaplan method learner model
214(1)
10.8.1.3 Fraity analysis with high dimensional data
215(1)
10.8.1.4 Sequential thresholding of correlated biomarkers
216(2)
10.8.1.5 Gene classification using longitudinal gene expressions
218(3)
10.8.1.6 MlclassKap
221(1)
10.9 Discussion
221(4)
Bibliography 225(28)
Index 253
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