Biostatistics for Oncologists - Adam Sullivan

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Résumé :

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 (?)

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

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Biographie:

Kara-Lynne Leonard, MD, MS, Assistant Professor of Radiation Oncology, Alpert Medical School of Brown University, Providence, RI