Hints to using SAS and R when Estimating Sample Size

Meet Andrii Artemchuk

Andrii Artemchuk is a Statistical Programmer Analyst working at Intego Group, specifically for one of our Pharma clients.  In 2018 Andrii gave a presentation at the PhUSE Conference in Frankfurt Germany titled Sample Size: a couple more hints to handle it right using SAS and R

Andrii graduated from Karazin Kharkiv National University, the department of mechanics and mathematics, and got his Master’s Degree in Applied Mathematics. Andrii has been working as a Clinical SAS programmer for over 3 years. His personal hobbies are cycling and skiing; he also likes driving and being out in the fresh air.

Introduction to Presentation

One of the key points in clinical trials is sample analysis. During the development of the protocol, the team must determine the target sample size for the trial and take multiple possible scenarios into consideration. What to do in the case of rare diseases where very few patients pass the inclusion criteria for the study? How many of these patients should be enrolled to make so the results achieve statistical significance? This presentation answer these questions. Two main topics are covered by the paper: the algorithms that calculate optimal sample size and their statistical background, and a check of the hypothesis statement that a sample analysis will yield a statistically significant result. The calculations are performed using (but not limited to) PROC POWER, PROC GLMPOWER in SAS and functions from the PWR packa

Sample Size: a couple more hints to handle it right using SAS and Rge in R; comparison of the results is performed.