Training on Advanced Statistical Models for Bio-Statisticians using R

In modern healthcare and life sciences, robust statistical modeling is essential for generating reliable evidence and supporting data-driven decisions. Biostatisticians and research professionals must be able to analyze complex datasets, account for variability, and produce reproducible results that inform clinical and public health interventions.

This course provides an in-depth, practical foundation in advanced statistical modeling using R, one of the most widely used programming languages in biostatistics and health research. It is designed to equip participants with the skills to build, analyze, and interpret sophisticated statistical models across biomedical, clinical, and epidemiological applications.

Participants will gain hands-on experience with advanced regression techniques, including generalized linear models (GLMs), logistic regression, and Poisson regression. The training also covers survival analysis methods such as Kaplan–Meier estimation and Cox proportional hazards models, which are critical for analyzing time-to-event data in clinical studies.

The course further explores mixed-effects models for handling hierarchical and longitudinal data, as well as multivariate statistical techniques for analyzing complex biological relationships. Emphasis is placed on model selection, diagnostics, validation, and interpretation to ensure analytical accuracy and scientific rigor.

Using real-world datasets, participants will develop practical skills in data preparation, statistical programming in R, and reproducible research workflows. The training also highlights best practices for communicating statistical findings in scientific reports, publications, and policy-relevant outputs.

By the end of the course, participants will be able to apply advanced statistical models confidently, improve the quality and credibility of their research, and support evidence-based decision-making in healthcare and life sciences.

Duration

 5 Days

Who Should Attend

• Biostatisticians and data analysts
• Epidemiologists and public health researchers
• Clinical trial and health research professionals
• Data scientists working in healthcare and life sciences

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