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. -
版本 : First edition.
ISBN : 9781003041931
ISBN : 1003041930
ISBN : 9781000601107
ISBN : 1000601102
ISBN : 9781000601152
ISBN : 1000601153
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100 1 $aCox, Trevor F.,$eauthor.$3100217
245 10$aMedical statistics for cancer studies /$cauthored by Trevor F. Cox.
250 $aFirst edition.
263 $a2206
264 1$aBoca Raton :$bChapman & Hall, CRC Press,$c2022.
300 $a1 online resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
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.$324189
650 0$aMedical statistics.$335910
710 2 $aEBSCOhost$387894
776 08$iPrint version:$aCox, Trevor F.$tMedical statistics for cancer studies$bFirst edition.$dBoca Raton : Chapman & Hall, CRC Press, 2022$z9780367486150$w(DLC) 2021061413
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)
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.