A SELF LEARNING COURSE: Statistical Simulation with SAS and R

"Harnessing Empirical Evidence Through Smart Simulation with SAS and R" 

 

In today’s data-driven world, the ability to design and conduct robust simulation studies is a vital skill for statisticians and researchers alike. Whether validating a new statistical method or exploring the performance of existing ones, smart simulation can transform theoretical ideas into convincing empirical evidence — but only if done with precision and care. 

Join us for this specialized short course led by Dr. Mehmet Kocak of Lumina Statistical, where we will dive deep into the art and science of simulation-based research. Designed for those seeking a more strategic and insightful approach, this course goes beyond basic simulation practices to address real-world challenges like reproducibility, computing efficiency, and design optimization across multiple statistical platforms. 

 

Throughout four focused modules, you will gain hands-on experience in both SAS and R, build univariable and multivariable simulation models, and learn to navigate common pitfalls that can undermine even the most well-intentioned studies. You’ll also have the opportunity to bring your own challenges to our "design studio" sessions for personalized guidance and feedback. 

If you're ready to elevate your simulation skills and empower your research with stronger empirical evidence, this course is your next step. 

 

Let’s move from simulation to smart simulation — and transform your approach to statistical research! 

 

We have designed the course as a set of practical simulation projects from quite basic levels to quite advanced levels after a general introduction to the concept of statistical simulations and the SAS and R platforms. Here are the four layers of the learning process in this course: 

 

Module-1: Simulating data for univariate random variables following Gaussian Distribution, Student-t-Distribution, Gamma Distribution and its special cases, Beta Distribution, Binomial Distribution, Poisson Distribution, etc.  

 

Module-2: Simulation designs for one-sample hypothesis testing for continuous, binary, and survival endpoints. In this module, we will also illustrate iterative simulation designs such as Phase-I Dose Escalation Design, and Simon’s Two-stage designs.  

 

Module-3: Simulation designs for two- or more-sample hypothesis testing for continuous, binary, and survival endpoints. One of the main focuses here will be Empirical Power calculations for Randomized Clinical Trials.  

 

Module-4: Simulation designs for Multivariate random variables and designs that require iterative processing. We will compare and contrast SAS and R in terms of efficiency in simulation design.  

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

• Understand the fundamental role of simulation studies in evaluating statistical methods and research designs. 

• Design and implement simulation studies for univariable and multivariable data across a variety of distributions (e.g., Gaussian, t, Gamma, Beta, Binomial, Poisson). 

• Develop simulation frameworks for hypothesis testing scenarios, including one-sample and two-sample tests for continuous, binary, and survival data endpoints. 

• Create iterative and conditional simulation models, including advanced designs such as Phase-I Dose Escalation and Simon’s Two-Stage Designs. 

• Identify and avoid common pitfalls in simulation study design that could compromise reproducibility, generalizability, or computational efficiency. 

• Capture and manage essential metadata to improve transparency, reproducibility, and efficiency in simulation workflows. 

• Compare and contrast simulation approaches between SAS and R, recognizing the strengths and limitations of each platform. 

• Apply empirical power calculation methods to evaluate the performance of clinical trial designs and other research studies. 

• Collaboratively design solutions to real-world simulation challenges through hands-on design studio sessions. 

• Develop a critical perspective on how to structure, document, and report simulation studies in a way that meets high scientific and ethical standards.