Basic Biostatistics Concepts for Radiation Oncologists

This lecture serves as a continuation of the basic biostatistics lecture. Dr. Leonard’s discussion of survival analysis is supplemented with real-life examples.

Disclosures:

0:00	Introduction
2:25	Sensitivity and Specificity, Positive/Negative Predictive Value
21:10	Positive and Negative Likelihood Ratios
28:00	Receiver/Operator Curves
29:33	Pre- and Post-Test Probability
39:35	Survival Analysis
41:50	Kaplan-Meier Curves
1:00:19	Log-Rank Test
1:05:14	Wilcoxon Rank Sum
1:05:32	Cox Proportional Hazard Model

Survival Analysis Worksheet (link to Word doc):

https://mailuc-my.sharepoint.com/:w:/g/personal/ramoska_ucmail_uc_edu/EVJhlxKkT5BCn3SvEAIL9TQBkHLMOBu8IAPQSCLAA3M7zQ?e=WMAnZL

Survival Analysis (link to Excel file):

https://mailuc-my.sharepoint.com/:x:/g/personal/ramoska_ucmail_uc_edu/EWc4cvuHvClLtufvrbRNxTkBdqqlIfJI0lCmOAzlnn4EHw?e=PHFA1t

Thinking Better About Cancer Drugs

Using examples, Dr. Prasad discusses clinical trial design and common pitfalls.

Vinay Prasad, MD, MPH

Basic Biostatistics for Radiation Oncology Trainees

In this lecture, Dr. Fuller discusses probability distributions and significance testing, among other core biostatistics concepts. 

Dr. Fuller's supplementary data set available for download is available at:   https://doi.org/10.6084/m9.figshare.13365191.v1

0:00	Introduction
5:10	Types of Measurement Scales
6:24	Summarizing and Displaying Data
9:29	Probability Distributions
14:07	Measures of Central Tendency, Measures of Spread
19:00	Graphical Representation
27:44	Sampling Variability, Standard Error
29:00	Confidence Intervals
29:53	Sample Size and Variability of Mean
30:45	Significance Testing
48:45	Statistical Error
51:00	t-Tests
53:45	Means of More than Two Samples
55:50	Multiple Comparisons and p-values
56:30	Mann-Whitney Test
58:05	Kruskal-Wallis H-Test

Clifton Fuller, MD, PhD

Non-Experimental Studies and Observational Data

Dr. Sher reviews methods of critically evaluating observational data, including limitations.

0:00	Introduction
6:12	Bias
8:15	Confounding
11:18	Effect Modification
12:09	Fundamental Comparisons
14:38	Univariable Calculations
15:54	Fisher Exact Test
20:01	Chi-squared Test
26:32	Pearson Correlation
28:30	Spearman Correlations
30:33	Linear Regression
34:58	Odds Ratio, Risk Ratio
36:39	Logistic Regression
40:58	Building a Model
45:59	Propensity Score Analysis
47:59	Instrumental Analysis
50:50	Case-Control Studies
53:10	Cross-Sectional Study
55:11	Cohort Study

David Sher, MD

Radiobiologic Principles in Radiation Oncology

Clinical Research Design

Dr. Bentzen discusses clinical trial design and what makes radiation oncology trials special.

0:00	Introduction
2:21	Discussion of historic RO trial (Copenhagen Breast Cancer Trial)
4:06	Summary of UK post-op RT trials for early breast cancer
6:08	Classical hypothesis testing
12:04	Phases of clinical trials
17:19	Cochrane ladder of evidence
19:19	What’s special about RO trials?
19:19	Double Blinding
19:19	Phase I/II or Feasibility Trials
19:19	Drug-Radiation Combinations
29:28	Toxicity monitoring
32:29	Dose-response relationship and trial design
43:44	Choice of endpoints

Søren Bentzen, PhD, DMSc

Bayesian Statistics in Clinical Trials

 In this seminar, Dr. Thall provides an introduction to Bayesian statistics.

0:00	Introduction
2:20	Elements of Bayesian Statistics
4:35	Beta Distributions
5:40	Simple Example
10:17	Estimating Probabilities of Rare Events
11:19	Simple Example, continued
14:38	Example: Kaplan Meier Estimates of PFS Time Distribution for Each Dose from a Phase I-II Trial of Targeted Agent in Advanced Renal Cancer
18:15	Quantifying Strength of Evidence
28:28	p-values
31:50	Looking at Efficacy and Toxicity- A Simple Example
34:49	Utility Based Clinical Trial Design
37:47	Example: A Radiation Dose-Finding Trial in Pediatric Brain Tumors
42:42	Example: Randomized Subgroup-Specific Comparison of NuPrehab vs Standard of Care Effects on Post-op Morbidity after Esophageal Resection
47:47	Example: PK-Guided vs. Fixed IV Busulfan Dose in Allogeneic Stem Cell Transplantation
52:26	Example: Randomized Pilot Study of T-reg Cell Therapy for ARDS in Intubated COVID-19 Patients

Machine Learning for Radiation Oncologists

0:00	Introduction
1:48	Machine learning and statistics
3:35	Modeling Nature
8:20	Examples of real-life models
12:41	Current state of AI in Rad Onc
14:51	Deep learning for diagnosis and treatment guidance
17:07	Definitions
20:11	Importance of using the correct model
24:37	Clinical models
26:38	Tips for creating a machine learning nomogram
27:57	Random Forest Model
33:15	Improving on Kattan-type models
36:07	Case study
47:22	ROC curves

Additional Resources:

Coursera 1: AI for Medicine: Goes through statistical models and machine learning models. Participants should have some coding experience.

Coursera 2: Machine Learning: Understandable for beginners.

Analyzing and Interpreting Patient Reported Outcomes

May 26, 2021 from 8:00-9:00 a.m.

0:00	Introduction
2:13	Definition of PRO
4:08	“Overcoming the Cons”
6:23	Secondary Analysis of RTOG 0617
21:30	RTOG 9801 QOL analysis
22:43	“Disadvantage of Men Living Alone Participating in RTOG Head and Neck Trials”
24:10	Quality of Life
28:25	PROs and PCI (RTOG 0214)
29:55	Suggested References
32:38	Lung SBRT and PROs
34:22	QOL Instruments
36:50	PRO-CTCAE
38:22	Collecting QOL data
41:15	Future of PROs/QOL
45:30	PROceeding with PROs