Training on Advanced Statistical Models for Bio-Statisticians using R

In the era of data-driven healthcare and life sciences, the ability to apply advanced statistical modeling is essential for bio-statisticians and research professionals. This course provides an in-depth understanding of how to use R programming to build, analyze, and interpret complex statistical models relevant to biomedical, clinical, and public health data. Participants will gain hands-on experience in advanced regression models, survival analysis, mixed models, and multivariate techniques — all grounded in real-world bio-statistical applications. The course is designed to strengthen analytical precision, enhance research credibility, and support evidence-based decision-making in health and biological research contexts.

Duration

5 Days

Who Should Attend

• Bio-statisticians and data analysts

• Epidemiologists and public health researchers

• Clinical trial and health research professionals

• Data scientists working in life sciences and healthcare

Organizational Impact:

• Enhanced analytical rigor in biomedical and public health research

• Stronger capacity for data-driven insights and policy recommendations

• Improved accuracy and reproducibility in clinical and epidemiological studies

• Strengthened institutional research credibility and publication output

Individual Impact:

• Mastery of advanced modeling techniques using R

• Improved capacity to analyze and interpret complex biomedical data

• Increased proficiency in automating and visualizing statistical results

• Greater confidence in presenting analytical findings to stakeholders and research peers

By the end of the course, participants will be able to:

• Master advanced statistical models and methods relevant to biostatistics.
• Develop proficiency in using R for complex data analysis and visualization.
• Apply statistical techniques to real-world biostatistical problems and datasets.
• Understand and implement model validation and diagnostic techniques.
• Interpret and communicate results from advanced statistical analyses effectively.

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 in R and their applications

• Master data input/output: functions for reading and writing data

• Explore loop functions, conditional structures, and vectorized operations

• Understand simulation techniques and code profiling for performance optimization
  Case Study: Building a Data Analysis Pipeline for Clinical Trial Data Using R

Module 2: Statistical Methods in R

• Identify and manage errors in statistical analysis

• Understand the logic and choice of significance tests

• Compare two independent and paired data groups

• Perform multiplicity testing across more than two groups

• Calculate correlations between variables

• Conduct equivalence and non-inferiority tests

• Interpret confidence intervals versus p-values and trends toward significance

• Apply power analysis to determine appropriate sample sizes
  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 ggsurvplot and ggsurvplot_df

• Compute and visualize survival curves

• Understand and use survreg arguments

• Compare Weibull and Log-Normal models for survival data
  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

• Understand why and when to use the Kaplan-Meier estimator

• Compute survival probabilities using Kaplan-Meier methods

• Estimate and visualize survival curves with censoring

• Compare survival outcomes using the Log-Rank test

• Evaluate differences between Weibull and Kaplan-Meier curves
  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 Proportional Hazards Model

• Compute and visualize the Cox model outputs

• Test the proportional hazards assumption

• Derive and interpret survival curves from Cox models

• Use surv_summary for comprehensive survival data analysis

• Compare survival outcomes across risk groups
  Case Study: Investigating the Impact of Various Risk Factors on Patient Survival Using the Cox Model