Course Outline

Module 1: Introduction to R Programming

• Understand how to work with variables, vectors, matrices, factors, data frames, lists, and arrays
• Learn the various data types
• Learning the different functions for reading and writing data
• Learn various loop functions and structures
• Understand simulation and profiling
• Case Study: Building a Data Analysis Pipeline for Clinical Trial Data Using R

Module 2: Statistical Methods in R

• Learn errors in statistical analysis
• Understanding the logic in and the choice of significance tests
• Comparison of two independent and paired groups of data
• Multiplicity and comparison of more than two groups of data
• Calculation of variable correlation
• Equivalence and non-inferiority tests
• Confidence intervals versus p-values and trends toward significance
• Using power analysis
• Case Study: Analyzing the Effectiveness of a New Drug by Comparing Multiple Treatment Groups

Module 3: The Weibull Model

• Interpret coefficients and compute the Weibull model using the ggsurvplot() and ggsurvplot_df()
• Computing the survival curves
• Visualizing a Weibull model
• Working with the survreg() arguments
• Computing the Weibull Model and the Log-Normal Model
• Case Study: Assessing the Reliability of Medical Devices Using Weibull Survival Analysis

Module 4: Survival Analysis Using Kaplan-Meier Graphs and the Log-Rank Test

• Why use Kaplan-Meier estimate
• Kaplan-Meier estimate and various functions to compute it
• Estimating and visualizing a survival curve using the Kaplan-Meier curve
• Exercising with ignoring censoring
• Estimating and visualizing the survival curve
• Comparison of the Weibull and Kaplan-Meier curves
• The Log-Rank Test
• Case Study: Comparing Survival Rates of Different Cancer Treatments Using Kaplan-Meier Analysis

Module 5: The Cox Model for Survival Analysis

• Introduction to the Cox model
• Computation and visualization of the Cox model
• Understanding the proportional hazard assumption
• Computation of the survival curve from the Cox model
• Using surv_summary() for analysis
• Comparison of the survival curves
• Case Study: Investigating the Impact of Various Risk Factors on Patient Survival Using the Cox Model