Biostatistics for Oncologists is the first practical guide providing the essential biostatistical concepts, oncology-specific examples, and applicable problem sets for medical oncologists, radiation oncologists, and surgical oncologists. The book also serves as a review for medical oncology and radiation oncology residents or fellows preparing for in-service and board exams. All examples are relevant to oncology and demonstrate how to apply core conceptual knowledge and applicable methods related to hypothesis testing, correlation and regression, categorical data analysis and survival analysis to the field of oncology. The book also provides guidance on the fundamentals of study design and analysis. Written for oncologists by oncologists, this practical text demystifies challenging statistical concepts and provides concise direction on how to interpret, analyze, and critique data in oncology publications, as well as how to apply statistical knowledge to understanding, designing, and analyzing clinical trials. With practical problem sets and twenty-five multiple choice practice questions with answers, the book is an indispensable review for anyone preparing for in-service exams, boards, MOC, or looking to hone a lifelong skill. Key Features: Practically explains biostatistics concepts important for passing the hematology, medical oncology, and radiation oncology boards and MOC exams. Provides guidance on how to read, understand, and critique data in oncology publications. Gives relevant examples that are important for analyzing data in oncology, including the design and analysis of clinical trials. Tests your comprehension of key biostatistical concepts with problem sets at the end of each section and a final section devoted to board-style multiple choice questions and answers Includes digital access to the eBook
I. General Statistical Concepts 1. Why study biostatistics? 1.1 What is biostatistics? 1.2 How is biostatistics useful for oncologists 2. Summarizing and Graphing Data 2.1 Types of data 2.1.1 Quantitative data 2.1.1.1 Discrete data 2.1.1.2 Continuous data 2.1.2 Qualitative data 2.1.2.1 Nominal data 2.1.2.2 Ordinal categorical data 2.2 Data summaries 2.2.1 Measures of Central Tendency 2.2.1.1 Mean 2.2.1.2 Median 2.2.1.3 Mode 2.2.2 Measures of Dispersion 2.2.2.1 Standard deviation 2.2.2.2 Interquartile range 2.3 Statistical Graphs 2.3.1 Histogram 2.3.2 Box Plot 2.3.3 Scatter plot 3. Sampling 3.1 Populations and Sample 3.2 Simple Random Sample 3.3 Other Sampling Methods 4. Statistical Estimation 4.1 Some basic distributions 4.1.1 Normal distribution 4.1.1.1 Central limit theorem 4.1.1.2 Student's T-distribution 4.1.1.3 Standard error of the mean 4.1.2 Binomial distribution 4.1.3 Poisson distribution 4.2 Estimations 4.2.1 Point estimates 4.2.2 Confidence intervals II. Important Statistical Concept for Oncologists 5. Hypothesis testing 5.1 Type I & Type II Errors 5.1.1 Type I Error 5.1.2 Type II Error 5.1.3 Alpha ( ) 5.1.4 Beta (ss) 5.2 p-values 5.3 T-Tests 5.3.1 One-Tailed versus Two-Tailed 5.3.2 Independent Samples 5.3.3 Paired Data 5.4 Wilcoxon Tests 5.4.1 Wilcoxon Rank Sum Test 5.4.2 Wilcoxon Signed-Rank Test 5.5 Analysis of Variance (ANOVA) 5.6 Testing Binomial Proportions 5.7 Confidence Intervals and Hypothesis Tests: How are they related? 5.8 Sensitivity and Specificity 5.8.1 Negative Predictive Value 5.8.2 Positive Predictive Value 5.8.3 Positive Likelihood Ratio 5.8.4 Negative Likelihood Ratio 6. Correlation and Regression 6.1 Correlation 6.1.1 Pearson's Correlation Coefficient 6.1.2 Spearman Rank Correlation 6.2 Regression 6.2.1 Simple Linear Regression 6.2.2 Multiple Linear Regression 6.2.3 Logistic Regression 7. Categorical Data Analysis 7.1 Contingency Tables 7.1.1 2 x 2 Tables 7.1.2 RxC Tables 7.1.3 Fisher's Exact Test 7.1.4 Chi-Square Test 7.1.5 Chi-Square Test versus Logistic Regression 7.2 Effect Size Estimators 7.2.1 Relative Risk 7.2.2 Odds Ratio 7.2.3 Relative Risk versus Odds Ratio 7.3 McNemar's Test 7.4 Mantel-Haenszel Method 7.4.1 Homogeneity Test 7.4.2 Summary Odds Ratio 8. Survival Analysis Methods 8.1 Time-to-event Data 8.2 Kaplan-Meier Curves 8.3 Log-Rank Test 8.4 Wilcoxon Rank Sum Test 8.5 Cox Proportional Hazards Model 9. Guide to choosing the appropriate statistical test 10. Non-inferiority Analysis III. Basics of Epidemiology 11. Study Designs 11.1 Experimental Studies 11.1.1 Clinical Trials 11.1.1.1 Common Outcomes for Clinical Trials in Oncology 11.1.1.2 Phase I Clinical Trials 11.1.1.3 Phase II Clinical Trials 11.1.1.4 Phase III Clinical Trials 11.1.1.5 Phase IV Clinical Trials 11.1.1.6 Meta-analysis 11.1.2 Field Trials 11.1.3 Community Intervention Trials 11.2 Non-experimental Studies 11.2.1 Cohort Studies 11.2.2 Case-Control Studies 11.2.3 Cohort Studies versus Case-Control Studies 11.2.4 Cross-Sectional Studies 11.2.5 Matched Studies 11.3 Analysis of Studies 11.3.1 Crude Analysis 11.3.2 Bias 11.3.2.1 Selection Bias 11.3.2.2 Measurement Bias 11.3.3 Confounding 11.3.4 Stratified Analysis 11.3.5 Effect Modification 11.4 Connections to Regression 11.5 Sample Size
