Medical Statistics for Cancer Studies: Chapman & Hall/CRC Biostatistics Series
Autor Trevor F. Coxen Limba Engleză Paperback – 26 aug 2024
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.
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 292.24 lei 6-8 săpt. | |
CRC Press – 26 aug 2024 | 292.24 lei 6-8 săpt. | |
Hardback (1) | 540.00 lei 6-8 săpt. | |
CRC Press – 23 iun 2022 | 540.00 lei 6-8 săpt. |
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Specificații
ISBN-13: 9781032285870
ISBN-10: 1032285877
Pagini: 334
Ilustrații: 246
Dimensiuni: 156 x 234 mm
Greutate: 0.62 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Biostatistics Series
Locul publicării:Boca Raton, United States
ISBN-10: 1032285877
Pagini: 334
Ilustrații: 246
Dimensiuni: 156 x 234 mm
Greutate: 0.62 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Biostatistics Series
Locul publicării:Boca Raton, United States
Cuprins
1 Introduction. 1.1. About Cancer. 1.2. Cancer studies. 1.3. R Code. 2. Cancer Biology and Genetics for Non-Biologists. 2.1. Cells. 2.2. DNA, Genes, RNA and Proteins. 2.3. Cancer – DNA Gone Wrong. 2.4. Cancer Treatments. 2.5. Measuring Cancer in the Patient. 3. Survival Analysis. 3.1. The Amazing Survival Equations. 3.2. Non-parametric Estimation of Survival Curves. 3.3. Fitting Parametric Survival Curves to Data. 3.4. Comparing Two Survival Distributions. 3.5. The ESPAC4-Trial. 3.6. Comparing Two Parametric Survival Curves. 4. Designing and Running a Clinical Trial. 4.1. Types of Trials and Studies. 4.2. Clinical Trials. 5. Regression Analysis for Survival Data. 5.1. A Weibull Parametric Regression Model. 5.2. Cox Proportional Hazards Model. 5.3. Accelerated Failure Time (AFT) Models. 5.4. Proportional Odds Models. 5.5. Parametric Survival Distributions for PH and AFT Models. 5.6. Flexible Parametric Models. 6. Clinical Trials: The Statistician’s Role. 6.1. Sample Size Calculation. 6.2. Examples of Sample Size Calculations; Phases I to III. 6.3. Group Sequential Designs. 6.4. More Statistical Tasks for Clinical Trials. 7. Cancer Epidemiology. 7.1. Measuring Cancer. 7.2. Cancer Statistics for Countries. 7.3. Cohort Studies. 7.4. Case-control Studies. 7.5. Cross-sectional Studies. 7.6. Spatial Epidemiology. 8. Meta-Analysis. 8.1. How to Carry Out a Systematic Review. 8.2. Fixed Effects Model. 8.3. Random Effects Model. 8.4. Bayesian Meta-analysis. 8.5. Network Meta-analysis. 8.6. Individual Patient Data. 9. Cancer Biomarkers. 9.1. Diagnostic Biomarkers. 9.2. Prognostic Biomarkers. 9.3. Predictive Biomarkers for Pancreatic Cancer. 9.4. Biomarker Trial Design. 10. Cancer Informatics. 10.1. Producing Genetic Data. 10.2. Analysis of Microarray Data. 10.3. Pre-processing NGS Data. 10.4. TCGA-KIRC: Renal Clear Cell Carcinoma.
Notă biografică
Trevor F. Cox is retired from Liverpool Cancer Trials Unit, University of Liverpool, UK
Descriere
This textbook 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 provides detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics.