This year the PSI Training Committee are delighted to offer a pre-conference, set to run on Sunday 11 June 2023. This will be held at the same location used for the conference itself – at the Novotel London West.
The course will run on Sunday 11 June 13:00 - 17:00.
Pre-conference course: Improving Precision and Power in Randomized Trials by Leveraging Baseline Variables
In May 2021, the U.S. Food and Drug Administration (FDA) released a revised draft guidance for the industry on “Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products”. Covariate adjustment is a statistical analysis method for improving precision and power in clinical trials by adjusting for pre-specified, prognostic baseline variables. Here, the term “covariates” refers to baseline variables, that is, variables that are measured before randomization such as age, gender, BMI, comorbidities. The resulting sample size reductions can lead to substantial cost savings, and also can lead to more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Though covariate adjustment is recommended by the FDA and the European Medicines Agency (EMA), many trials do not exploit the available information in baseline variables or only make use of the baseline measurement of the outcome.
In Part 1, we introduce the concept of covariate adjustment. In particular, we explain what covariate adjustment is, how it works, when it may be useful to apply, and how to implement it (in a preplanned way that is robust to model misspecification) for a variety of scenarios.
In Part 2, we present a new statistical method that enables us to easily combine covariate adjustment with group sequential designs. The result will be faster, more efficient trials for many disease areas, without sacrificing validity or power. This approach can lead to faster trials even when the experimental treatment is ineffective; this may be more ethical in settings where it is desirable to stop as early as possible to avoid unnecessary exposure to side effects.
In Part 3, we demonstrate the impact of covariate adjustment using completed trial data sets in multiple disease areas. We provide step-by-step, clear documentation of how to apply the software in each setting. Participants will have the time to apply the software tools on the different datasets in small groups.
- Participants will need to bring a laptop with R installed
- A basic knowledge about R is desirable, but not necessary as there will be tutorials available where the focus is on the output and interpretation rather than on the code
- Participants should be familiar with concepts like Type I error, power, bias and variance
Course instructors: Kelly Van Lancker (Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium), Josh Betz (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.) and Michael Rosenblum (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.)
The number of places on this course is limited, register today!
The registration fees for the pre-conference courses are as follows.
|Registration Item||Standard Rate|
All amounts are in GBP and include VAT.
The course is now available to register to attend alongside your event registration, here.