The speaker series is sponsored by UC Radiation Oncology.
Kara Leonard, MD
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:
- Royalties from Elsevier for Biostatistics for Oncologists
- ACRO chart review compensation.
| 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):
Vinay Prasad, MD, MPH
Thinking Better About Cancer Drugs
Using examples, Dr. Prasad discusses clinical trial design and common pitfalls.
Vinay Prasad, MD, MPH
Clifton Fuller, MD, PhD
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
David Sher, MD
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
Søren Bentzen, PhD, DMSc
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
Peter Thall, PhD
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 |
John Kang, MD, PhD
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 2: Machine Learning: Understandable for beginners.
Benjamin Movsas, MD
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 |