Introduction to Medical Statistics

Published

April 16, 2026

Keywords

Medical Statistics, OUCRU, Biostatistics, R Programming, Clinical Research

Welcome!

Course Objectives

  • Concepts: Hypothesis tests, p-values, & confidence intervals.
  • Methods: t-tests, chi-square, linear & logistic regression.
  • Skills: Perform and interpret basic statistical analyses.

Welcome to the OUCRU Introduction to Medical Statistics 2026. This course is designed to provide clinicians and researchers with the fundamental statistical tools required for medical research.

Welcome note

Note📅 Logistics & Schedule
  • Time: 09:15 – 16:45 (UTC +7) daily
  • Lunch Break: 12:30 – 13:30 (UTC +7)
  • Full Schedule: Timetable
  • Note: Lunch is provided for in-person attendees.

Contributors

  • Prof. Ronald Geskus
  • Dr. Duc Du Hong
  • Dr. Hoang Van Thuan
  • Dr. Nguyen Lam Vuong
  • Dr. Tran Thai Hung

1 Pre-course Setup

Tip🛠️ Choose Your Environment

You have two ways to follow the course exercises:

  1. Zero Setup: Use the Web-R versions directly in your browser.
  2. Local Setup: If you prefer using RStudio, please complete the Pre-course Setup Handout before joining.

2 Course Topics

Course handout: Course handout; Dictionary

Day 1: Descriptive and Exploratory Data Analysis

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Contents

  1. Data characteristics & Variable types
  2. Numerical & Graphical summaries
  3. Intro to R and RStudio

Materials

Contents

  1. Baseline tables
  2. Graphs for categorical variables
  3. Numeric variables by group
  4. 2-variable numeric plots

Materials

Day 2: Statistical analysis principles, Distributions, Confidence Intervals

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Contents

  1. Types of study questions
  2. Sampling variation
  3. Binomial distribution
  4. Testing hypotheses

Materials

Contents

  1. The normal distribution
  2. Confidence intervals
  3. Hypothesis tests and p-values

Materials

Day 3: Comparing continuous variables and Simple linear regression

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Contents

  1. Student’s t-test vs. Wilcoxon rank-sum test
  2. Paired t-test vs. Wilcoxon signed-rank test

Materials

Contents

  1. Visualizing Relationships
  2. The Linear Regression Model
  3. Interpreting Results
  4. Regression Diagnostics

Materials

Day 4: Multivariable linear and logistic regression

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Contents

  1. Model formulation
  2. Estimation
  3. Testing

Materials

Contents

  1. Risk/odds, risk ratio (RR)/odds ratio (OR)
  2. Logistic regression model
  3. Interpreting Univariable Logistic Regression
  4. Interpreting Multivariable Logistic Regression

Materials

Day 5: Study design, sample size calculation & Steps in quantitative research

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Contents

  1. Study design
  2. Sample size calculation

Materials

Contents

  1. Quantitative research

Materials