Introduction

Biostatistics plays a crucial role in healthcare by providing the tools needed to analyze and interpret data from clinical trials, observational studies, and health research. This course is designed to equip participants with the necessary statistical knowledge and practical skills to analyze healthcare data using R. From basic statistical methods to advanced analyses like survival analysis and multivariate regression, this course will empower healthcare professionals, researchers, and data scientists to draw meaningful conclusions from healthcare datasets and make informed decisions.

Objectives

By the end of this course, participants will:

• Understand the key statistical concepts in biostatistics and their applications in healthcare.
• Be proficient in using R for data manipulation, visualization, and analysis in healthcare settings.
• Master common statistical methods such as t-tests, ANOVA, regression, and survival analysis for healthcare data.
• Learn how to handle healthcare data (e.g., clinical trials, observational studies) and perform real-world analysis.
• Be able to interpret results from statistical analyses and communicate findings effectively in a healthcare context.

Who Should Attend?

This course is ideal for:

• Healthcare professionals (doctors, nurses, and clinicians) who need to understand statistical data for decision-making.
• Data scientists, statisticians, and researchers working in healthcare or public health fields.
• Students and professionals interested in applying statistical methods to healthcare data.
• Anyone looking to enhance their analytical skills for working with healthcare data using R.

Day 1: Introduction to Biostatistics and R for Healthcare

Morning Session: Introduction to Biostatistics

• What is Biostatistics? Understanding its role in healthcare and research.
• Types of data in healthcare: Categorical vs. continuous, nominal vs. ordinal.
• Descriptive statistics: Mean, median, mode, variance, standard deviation, and range.
• Data visualization: Creating and interpreting graphs such as histograms, box plots, and bar charts.
• Introduction to R: Setting up RStudio and basic R functions for statistical analysis.
• Hands-on: Importing and manipulating healthcare datasets in R (e.g., hospital data, patient records).

Afternoon Session: Descriptive Statistics in R

• Summarizing healthcare data: Measures of central tendency and spread.
• Visualizing data: Using ggplot2 to create insightful healthcare data visualizations.
• Exploring healthcare datasets: Patient demographics, disease prevalence, and treatment outcomes.
• Hands-on: Analyzing and visualizing a healthcare dataset using basic descriptive statistics in R.

Day 2: Hypothesis Testing and Statistical Inference

Morning Session: Introduction to Hypothesis Testing

• What is hypothesis testing? Null and alternative hypotheses, p-values, and significance levels.
• Common tests: t-test (one-sample, two-sample), chi-square test, and Fisher’s exact test.
• Type I and Type II errors: Understanding risks in healthcare research.
• Confidence intervals and their interpretation in healthcare research.
• Hands-on: Conducting hypothesis tests on healthcare data in R (e.g., comparing patient groups, treatment effects).

Afternoon Session: Advanced Hypothesis Testing Techniques

• ANOVA (Analysis of Variance): One-way and two-way ANOVA for comparing multiple groups.
• Non-parametric tests: Mann-Whitney U test, Kruskal-Wallis test, and their use in healthcare data.
• Power analysis: Determining sample sizes for healthcare studies.
• Hands-on: Performing ANOVA and non-parametric tests in R with healthcare datasets.

Day 3: Regression Analysis in Healthcare

Morning Session: Introduction to Linear Regression

• What is regression analysis? Understanding relationships between variables.
• Simple linear regression: Model fitting, coefficients, and assumptions.
• Multiple linear regression: Handling multiple predictors in healthcare data.
• Interpreting regression coefficients in the context of healthcare outcomes.
• Hands-on: Building a linear regression model in R to predict healthcare outcomes (e.g., patient survival, treatment success).

Afternoon Session: Logistic Regression and Multivariate Analysis

• Logistic regression: Modeling binary outcomes (e.g., disease vs. no disease).
• Odds ratios and interpreting coefficients in logistic regression.
• Multivariate regression: Analyzing the effect of multiple variables on health outcomes.
• Hands-on: Building a logistic regression model in R for predicting healthcare-related outcomes (e.g., disease incidence, treatment response).

Day 4: Survival Analysis in Healthcare

Morning Session: Introduction to Survival Analysis

• What is survival analysis? Application to healthcare data (e.g., patient survival, time to event data).
• Key concepts: Survival function, hazard function, and censoring.
• Kaplan-Meier estimator: Plotting survival curves and comparing groups.
• Log-rank test: Comparing survival curves across groups.
• Hands-on: Performing survival analysis in R using healthcare datasets (e.g., cancer survival, patient recovery time).

Afternoon Session: Cox Proportional Hazards Model

• Introduction to the Cox model: Understanding the relationship between survival time and covariates.
• Assumptions of the Cox model and how to test them.
• Interpreting hazard ratios and applying them to healthcare research.
• Hands-on: Fitting a Cox proportional hazards model to a healthcare dataset in R.

Day 5: Advanced Topics and Practical Applications

Morning Session: Mixed Models and Repeated Measures

• Introduction to mixed-effects models: When and why they are used in healthcare research.
• Random and fixed effects: Understanding their role in healthcare data analysis.
• Repeated measures: Dealing with data collected over time or from multiple observations.
• Hands-on: Analyzing repeated measures data (e.g., patient vitals over time) using mixed models in R.

Afternoon Session: Final Project and Course Wrap-Up

• Final project: Participants apply their knowledge to a real-world healthcare dataset and present their findings.
• Data analysis for clinical trials, observational studies, or health surveys.
• Interpreting statistical results and translating them into actionable healthcare decisions.
• Final Q&A and discussion: Addressing challenges faced by participants in analyzing healthcare data.
• Certification of completion for those who successfully complete the course and final project.

Materials and Tools:

• Software and tools: R, RStudio, ggplot2, survival, lme4, dplyr, tidyr, caret
• Real-world healthcare datasets: Public health data, clinical trial results, hospital patient data, epidemiological studies
• Recommended readings: Key chapters from “Biostatistics for the Health Sciences” and “The Essence of Biostatistics”

Conclusion and Final Assessment

• Recap of key concepts: Hypothesis testing, regression analysis, survival analysis, and mixed models.
• Final assessment: Project presentation, Q&A, and evaluation of practical knowledge.
• Certification of completion for those who successfully complete the course and final project.