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Medical Statistics for Cancer Studies [Hardback]

  • Formāts: Hardback, 334 pages, height x width: 234x156 mm, weight: 453 g, 71 Tables, black and white; 123 Line drawings, black and white; 123 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Biostatistics Series
  • Izdošanas datums: 23-Jun-2022
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367486156
  • ISBN-13: 9780367486150
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  • Cena: 119,73 €
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Medical Statistics for Cancer StudiesPalielināt
  • Formāts: Hardback, 334 pages, height x width: 234x156 mm, weight: 453 g, 71 Tables, black and white; 123 Line drawings, black and white; 123 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Biostatistics Series
  • Izdošanas datums: 23-Jun-2022
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367486156
  • ISBN-13: 9780367486150
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367486150.html
Keywords:
Cancer is a dreaded disease. One in two people will be diagnosed with cancer within their lifetime. Medical Statistics for Cancer Studies shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It gives some background in cancer biology and genetics, followed by detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics. It includes lots of examples using real data from the authors many years of experience working in a cancer clinical trials unit.

Features:











A broad and accessible overview of statistical methods in cancer research Necessary background in cancer biology and genetics Details of statistical methodology with minimal algebra Many examples using real data from cancer clinical trials Appendix giving statistics revision.
Preface xi
1 Introduction
1(6)
1.1 About cancer
2(1)
1.2 Cancer studies
3(2)
1.3 R code
5(2)
2 Cancer Biology and Genetics for Non-Biologists
7(16)
2.1 Cells
7(3)
2.2 DNA, genes, RNA and proteins
10(4)
2.2.1 DNA
10(2)
2.2.2 Genes
12(1)
2.2.2.1 RNA and proteins
12(2)
2.3 Cancer -- DNA gone wrong
14(2)
2.4 Cancer treatments
16(2)
2.5 Measuring cancer in the patient
18(5)
3 Survival Analysis
23(42)
3.1 The amazing survival equations
23(9)
3.1.1 The survival function
23(3)
3.1.2 The hazard function
26(1)
3.1.2.1 Shapes of Weibull distributions
27(2)
3.1.2.2 Proportional hazards and the hazard ratio
29(1)
3.1.3 The cumulative hazard function
29(2)
3.1.4 Design your own survival function
31(1)
3.2 Non-parametric estimation of survival curves
32(11)
3.2.1 The Kaplan-Meier survival curve
33(3)
3.2.2 Confidence intervals for KM curves
36(2)
3.2.3 Mean and median survival times
38(2)
3.2.4 The Nelson-Aalen cumulative hazard curve
40(1)
3.2.5 Estimating the hazard function
41(2)
3.3 Fitting parametric survival curves to data
43(4)
3.3.1 Parametric survival curves fitted to the lung cancer trial data
45(2)
3.4 Comparing two survival distributions
47(11)
3.4.1 The log-rank test
48(3)
3.4.1.1 Stratified log-rank test
51(1)
3.4.1.2 Log-rank tests for the lung cancer trial
52(1)
3.4.2 Other tests
53(1)
3.4.2.1 Checking for proportional hazards
54(1)
3.4.2.2 Weighted log-rank tests
55(1)
3.4.2.3 Log-rank test: three or more arms
56(2)
3.5 The ESPAC4-Trial
58(5)
3.6 Comparing two parametric survival curves
63(2)
4 Designing and Running a Clinical Trial
65(10)
4.1 Types of trials and studies
65(2)
4.2 Clinical trials
67(8)
4.2.1 Regulatory and other bodies
68(1)
4.2.2 Trial setup
69(3)
4.2.3 Running the trial
72(3)
5 Regression Analysis for Survival Data
75(48)
5.1 A Weibull parametric regression model
75(2)
5.2 Cox proportional hazards model
77(28)
5.2.1 Multiple imputation for missing data
82(5)
5.2.2 Assessing the fit of the Cox model
87(6)
5.2.3 Estimating the baseline hazard function
93(2)
5.2.4 Time dependent predictors
95(9)
5.2.5 Frailty models
104(1)
5.2.5.1 Shared frailty
105(1)
5.3 Accelerated failure time (AFT) models
105(4)
5.4 Proportional odds models
109(2)
5.5 Parametric survival distributions for PH and AFT models
111(2)
5.6 Flexible parametric models
113(10)
5.6.1 Parametric models fitted to some colon cancer data
115(8)
6 Clinical Trials: the Statistician's Role
123(28)
6.1 Sample size calculation
123(7)
6.1.1 Normal random variables
124(1)
6.1.2 Binomial random variables
125(1)
6.1.3 Survival random variables
126(2)
6.1.3.1 Number to recruit
128(2)
6.2 Examples of sample size calculations; Phases I to III
130(4)
6.2.1 Phase I trials: dose escalation studies
130(1)
6.2.2 Phase II trials
131(2)
6.2.3 Phase III trials
133(1)
6.3 Group sequential designs
134(8)
6.3.0.1 Stopping for futility
139(1)
6.3.0.2 Group sequential designs for survival endpoints
140(2)
6.4 More statistical tasks for clinical trials
142(9)
6.4.1 Randomisation and recruitment
142(1)
6.4.1.1 Simple randomisation
142(1)
6.4.1.2 Block randomisation
143(1)
6.4.1.3 Stratified randomisation
143(1)
6.4.1.4 Minimisation
143(1)
6.4.2 Statistical contribution to the protocol
144(2)
6.4.3 The Statistical Analysis Plan (SAP)
146(1)
6.4.4 Recruitment
146(1)
6.4.5 Statistical reports
147(1)
6.4.6 Post study analyses
148(3)
7 Cancer Epidemiology
151(42)
7.1 Measuring cancer
152(4)
7.1.1 Study designs for measuring cancer
154(2)
7.2 Cancer statistics for countries
156(8)
7.2.1 Cancer incidence
157(5)
7.2.2 Cancer mortality
162(2)
7.3 Cohort studies
164(12)
7.3.1 Poisson regression models
169(4)
7.3.2 Two more examples of cohort studies
173(3)
7.4 Case-control studies
176(10)
7.4.1 Unmatched case-control study
177(2)
7.4.2 Confounding in case-control studies
179(2)
7.4.3 Logistic regression for unmatched case-control studies
181(2)
7.4.4 Matched case-control studies
183(3)
7.5 Cross-sectional studies
186(4)
7.6 Spatial epidemiology
190(3)
8 Meta-Analysis
193(38)
8.1 How to carry out a systematic review
194(3)
8.1.1 An oesophageal cancer review
196(1)
8.2 Fixed effects model
197(4)
8.3 Random effects model
201(9)
8.3.1 Assessing model fit
205(3)
8.3.2 Publication bias
208(2)
8.4 Bayesian meta-analysis
210(6)
8.5 Network meta-analysis
216(13)
8.5.1 Fixed effects network meta-analysis
218(2)
8.5.2 Network meta-analysis for breast cancer
220(4)
8.5.3 Bayesian network meta-analysis for pancreatic cancer
224(5)
8.6 Individual patient data
229(2)
9 Cancer Biomarkers
231(24)
9.1 Diagnostic biomarkers
232(11)
9.1.1 Measuring prevalence
235(1)
9.1.2 Comparing two tests
236(2)
9.1.3 Receiver Operating Characteristic (ROC) curves
238(3)
9.1.3.1 ROC curve theory
241(1)
9.1.4 HCC revisited
242(1)
9.1.4.1 Validation
243(1)
9.2 Prognostic biomarkers
243(5)
9.2.1 Prognostic biomakers for HCC
244(4)
9.3 Predictive biomarkers for pancreatic cancer
248(5)
9.4 Biomarker trial design
253(2)
10 Cancer Informatics
255(30)
10.1 Producing genetic data
256(3)
10.1.1 Polymerase chain reaction (PCR)
257(1)
10.1.2 DNA microarrays
257(1)
10.1.3 Next generation sequencing (NGS)
258(1)
10.2 Analysis of microarray data
259(15)
10.2.1 Preprocessing microarray data, low-level analysis
260(2)
10.2.2 Preprocessing the melanoma microarray data
262(4)
10.2.3 High-level analysis of the melanoma data
266(8)
10.3 Pre-processing NGS data
274(1)
10.4 TCGA-KIRC: Renal clear cell carcinoma
275(10)
10.4.1 Differential expression
276(2)
10.4.2 Genes as biomarkers for survival
278(2)
10.4.3 Single nucleotide variation
280(5)
Appendices
285(22)
A Statistics Revision
287(1)
A.1 Some distribution theory
287(3)
A.1.1 The central limit theorem
287(1)
A.1.2 Transformations of random variables
288(2)
A.2 Statistical inference
290(3)
A.2.1 Parameter estimation
290(1)
A.2.2 Hypothesis testing
291(2)
A.3 Regression analysis
293(2)
A.3.0.1 Choosing a subset of predictor variables
294(1)
A.4 Bayesian methods
295(8)
A.4.1 MCMC: Markov Chain Monte Carlo
296(5)
A.4.1.1 Gibbs sampling
301(2)
A.5 Multivariate data analysis
303(4)
Bibliography 307(8)
Index 315
Trevor F. Cox is retired from Liverpool Cancer Trials Unit, University of Liverpool, UK