Statistical topic 1 for radiation oncology: A Brief Introduction to Bayesian Data Analysis Presentation (Word)

In this topic, we present some basic concepts in Bayesian data analysis and provide an example to show how this popular method can give very similar results to those from traditional statistical methods. 

Statistical topic 2 for radiation oncology: Statistical index variables and applications presentation (Word)

In this topic, we are introducing some popular and influential statistical index variables which are a comprehensive score of the underlying phenomena. At first sight, they are minor, yet they are powerful in the applications to wide range of clinical practice and research. 

Statistical topic 3 for radiation oncology – Data transformations in statistical analysis presentation (Word)

In this topic, we present some SAS and R codes introducing fundamental methods in data transformation, either from wide (horizontal) data to long (vertical) formatted data or vice versa; and why we need these procedures in statistical analysis.

Statistical topic 4 for radiation oncology at KU Medical Center – Cross validations in statistical applications. Presentation (Word)

In this topic, we are introducing the concepts of cross validation (CV) – how it is defined, types of cross validation, why we need it in statistical analysis (key ideas behind CV), a brief historical note and some examples in R and SAS. 

Statistical topic 5 for radiation oncology at KU Medical Center – Some common mistakes in statistical analysis. Presentation (Word)

In this topic, we are talking about some commonly seen mistakes in research or study. Some are obvious, some are quite hidden – 2 Kaplan-Meier curves could overlap or cross over but still significantly different; a model looks great but may be very badly overfitted; correlations are not causations; we could visually exaggerate some minor group differences in the data; and more. R and SAS codes are provided to replicate the examples. 

Statistical topic 6 for radiation oncology at KU Medical Center – Kaplan-Meier or CIF curves in survival data analysis (Word)

In this topic, we are introducing Kaplan-Meier curves in data analysis. We talk about its basic concepts, issues and how to plot publication-quality curves. SAS codes are provided to replicate the examples.

Statistical topic 7 for radiation oncology at KU Medical Center – Medical statistics for cancer research (Word)

In this topic, we are introducing common statistical methods (KM curves, Bayesian design) in cancer research or oncological clinical trials. We talk about the basic statistical concepts or ideas behind those popular methods, their advantages or disadvantages. Some online tools for sample size calculations are also included. 

Statistical topic 8 for radiation oncology at KUMC – Adaptive statistics, some concepts (Word)

In this topic, we are introducing some fundamental ideas behind the so-called adaptive statistics (we compared several AI definitions) and provide an example on clinical trial adaptive design and how it is realized in a SAS code.

Statistical topic 9 for radiation oncology at KUMC – The Most Influential Statistical Papers in History (Word) 

In this topic, we are briefing some (arguably) of the most read or cited papers in the history of statistics and their applications. We provided links where readers can find the original writings that greatly shaped (unbelievably) the world around us.

Statistical topic 10 for radiation oncology at KUMC – Random matrix and its applications in statistics (Word) 

In this topic, we are introducing a very important concept in statistical applications – the random matrix. We provided some historical briefs on the development of the topic and its applications (examples) to statistical analysis and other areas, mostly conceptual. 

Statistical topic 11 for radiation oncology at KUMC – The Paradoxes in Statistics or Probability (Word)

In this topic, some of the most popular statistical paradoxes are revisited, and we tried to provide some reasonable interpretations of why they existed and how we can avoid entering the trap.

Statistical topic 12 for radiation oncology at KUMC – How to Improve Statistical Analysis for Likert Type of Measurements (Word)

In this topic, we are introducing some widely used methods for Likert type variables, their background, applications, and their connection to a variety of areas and studies.

Statistical topic 1 for radiation oncology  - A glimpse of medicine – a poem (Word doc)

The poems tell stories about how data and statistical analysis affect the daily work of the data analyst at the Department of Radiation Oncology, KU Medical Center.

Statistical topic 2 for radiation oncology at KU Medical Center - Non-inferiority and superiority tests in clinical trials (Word doc)

In this topic, we are introducing some of the fundamental concepts that are a key to understanding clinical trials where we would apply the non-inferiority or superiority statistical test to evaluate a new treatment or new drug and so on.

Statistical topic 3 for radiation oncology at KU Medical Center -  Why is logistic model so popular?(Word doc)

In this topic, we’ll introduce the basic concepts for performing logistic models and how they are realized in many different areas such as clinical data, banking service, NFL, and more. 

Statistical topic 4 for radiation oncology at KU Medical Center -  Uncertainty in statistical applications (Word doc)

In this topic, we present some types of uncertainties that appear in statistical analysis such as SD, bias, asymptotic theory, adaptive statistics in clinical trials, fuzz statistics, adjustments in statistics and quasi-statistics. 

Statistical topic 5 for radiation oncology at KU Medical Center – Geometric curves and their equations in statistical applications (Word doc)

In this topic, we present some interesting statistical curves we see every day and their corresponding algebraic equations (a magic duality). Examples include normal distribution curve, S shaped curves, KM curves, logistic curves, and more.

Statistical topic 6 for radiation oncology at KU Medical Center – Entropy, information theory and statistical applications (Word doc)

In this topic, we tried to link the concept of entropy in physics (thermodynamics and statistical mechanics...) to information theory (computer science, communications...) and statistical applications. It remains mysterious how all these quite different branches of science are connected via a low-profile creative probabilistic idea. 

Statistical topic 7 for radiation oncology at KU Medical Center – Mistakes or misunderstanding in statistical applications? (Word doc)

Statistical applications are a part of the integrated solution of clinical practice, especially in writing a paper, a grant application, or a presentation at a professional meeting. Can we avoid any mistakes when we do statistics? Not likely. 

Statistical topic 8 for radiation oncology at KU Medical Center - How to select good variables for a multivariable model? (Word doc)

This is a common situation that occurs again and again when we were trying to model a clinical data or observational cohort. We have a larger number of variables in hand but how should we select some “good” variables (also nicknamed as “predictors”) so that we can use them for a multivariate predictive model such as logistic model or Cox proportional hazard model?

Statistical topic 9 for radiation oncology at KU Medical Center - Nomogram and its applications (Word doc)

A nomogram is an alternative to a mathematical formula in presenting a complex result of a data, for example a multivariable logistic regression or Cox model in survival analysis. We should try to include it in our data analysis for logistic model or Cox proportional model in survival data analysis. It would help us to visualize the data and the biological or clinical mechanism that may be hidden in the database. 

Statistical topic 10 for radiation oncology at KU Medical Center - Randomness, hovering like a Himalaya eagle in the world of data. At first sight, it is seemingly related to chaos and order of beautiful nature, but we are far away from grasping its grandness, versatile applications, and universality.  Randomness, hovering like a Himalaya eagle in the world of data (Word doc)

Statistical topic 11 for radiation oncology at KU Medical Center - Additive models in statistical analysis. Why do we use additive models? It’s not new but other than a general linear model, additive models are more flexible to deal with non-linear data, better in prediction than a linear model, more appropriate to handle missing data issues, and easier to interpret on the model so why not!  Additive models in statistical analysis (Word doc)

Topic 13 - How to Make A Descriptive Statistics Table in SAS Presentation (Word)

Topic 14 - Evolution of P Values (Word)

Topic 15 - What is ChatGPT (Word)

Topic 16 - How to Make a Multivariable (Word)

Topic 17 - Optimization (Word)

Topic 18 - An Introductory to Biostatistics at KU Medical Center - Presentation

Topic 19 - How Was The History of Statistics Made in The Books (Word)

Topic 20 - Prediction- A Lure or Dream in Statistics (Word)

Topic 21 - Statistical Columns in a Medical Journal (Word)

Topic 22 - Statistical Question For a Resident in Radiation Oncology (Word)

Topic 1 - A brief history of modern statistical applications - Presentation.

Topic 2 - Radiation Oncology and Statistics Presentation (Word) 

Topic 3 - Do it yourself statistics Presentation (Word)

Topic 4 - Covid 19 Numbers and Statistics - Presentation 

Topic 5- The magic normal distributions- Presentation

Topic 6- Data Transformation "A Prince of Statistics- Presentation

Topic 7-The Yin and Yang of Small Data- Presentation

Topic 8 - Correlation- It's Dual Characteristics In Statistics - Presentation 

Topic 9-Similar or different: before and after some transformation - Presentation

Topic 10 - The Embarrassed Moments in Statistics- Presentation

Topic 11 - A World of Statistics- Presentation

Topic 12 - A Conversation With The Statistics- Presentation