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Medical statistics for cancer studies /
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Medical statistics for cancer studies /

紀錄類型 : 書目-語言資料,印刷品: 單行本

作者 : Cox, Trevor F.,

出版項 : Boca Raton :Chapman & Hall, CRC Press,2022.

面頁冊數 : 1 online resource.

內容註 : <P><STRONG>1 Introduction.</STRONG> 1.1. About Cancer. 1.2. Cancer studies. 1.3. R Code. <STRONG>2. Cancer Biology and Genetics for Non-Biologists. </STRONG>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. <STRONG>3. Survival Analysis.</STRONG> 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.<STRONG> 4. Designing and Running a Clinical Trial.</STRONG> 4.1. Types of Trials and Studies. 4.2. Clinical Trials. <STRONG>5. Regression Analysis for Survival Data. </STRONG>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. <STRONG>6. Clinical Trials: The Statistician's Role.</STRONG> 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. <STRONG>7. Cancer Epidemiology.</STRONG> 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. <STRONG>8. Meta-Analysis.</STRONG> 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.<STRONG> 9. Cancer Biomarkers.</STRONG> 9.1. Diagnostic Biomarkers. 9.2. Prognostic Biomarkers. 9.3. Predictive Biomarkers for Pancreatic Cancer. 9.4. Biomarker Trial Design.<STRONG> 10. Cancer Informatics.</STRONG> 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.</P>

標題 : Cancer - Research - Statistical methods. -

標題 : Medical statistics. -

版本 : First edition.

ISBN : 9781003041931

ISBN : 1003041930

ISBN : 9781000601107

ISBN : 1000601102

ISBN : 9781000601152

ISBN : 1000601153

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245 10$aMedical statistics for cancer studies /$cauthored by Trevor F. Cox.

250 $aFirst edition.

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264 1$aBoca Raton :$bChapman & Hall, CRC Press,$c2022.

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490 0 $aChapman & hall/crc biostatistics series

504 $aIncludes bibliographical references and index.

505 0 $a<P><STRONG>1 Introduction.</STRONG> 1.1. About Cancer. 1.2. Cancer studies. 1.3. R Code. <STRONG>2. Cancer Biology and Genetics for Non-Biologists. </STRONG>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. <STRONG>3. Survival Analysis.</STRONG> 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.<STRONG> 4. Designing and Running a Clinical Trial.</STRONG> 4.1. Types of Trials and Studies. 4.2. Clinical Trials. <STRONG>5. Regression Analysis for Survival Data. </STRONG>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. <STRONG>6. Clinical Trials: The Statistician's Role.</STRONG> 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. <STRONG>7. Cancer Epidemiology.</STRONG> 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. <STRONG>8. Meta-Analysis.</STRONG> 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.<STRONG> 9. Cancer Biomarkers.</STRONG> 9.1. Diagnostic Biomarkers. 9.2. Prognostic Biomarkers. 9.3. Predictive Biomarkers for Pancreatic Cancer. 9.4. Biomarker Trial Design.<STRONG> 10. Cancer Informatics.</STRONG> 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.</P>

533 $aElectronic reproduction.$bIpswich, MA$nAvailable via World Wide Web.

545 0 $aTrevor F. Cox is retired from Liverpool Cancer Trials Unit, University of Liverpool, UK

588 $aDescription based on print version record and CIP data provided by publisher; resource not viewed.

650 0$aCancer$xResearch$xStatistical methods.$324189

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856 40$uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3275753$zClick to View (限總院院內)

Cox, Trevor F.,

Medical statistics for cancer studies /authored by Trevor F. Cox. - First edition. - 1 online resource. - Chapman & hall/crc biostatistics series.

Includes bibliographical references and index.

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.




Electronic reproduction.
Ipswich, MA





Available via World Wide Web.

Trevor F. Cox is retired from Liverpool Cancer Trials Unit, University of Liverpool, UK


ISBN: 9781003041931

Standard No.: 10.1201/9781003041931doi

Source: 9781003041931Taylor & Francis

LCCN: 2021061414Subjects--Topical Terms:

24189
Cancer
--Research

LC Class. No.: RC267

Dewey Class. No.: 616.99/400727
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