Surrogate Markers
The practice of using a surrogate marker to replace a primary outcome in clinical studies has become widespread. Typically, the primary outcome requires long-term patient follow-up, is expensive, or is invasive or burdensome on patients to measure, while the surrogate marker is not (or less so).
This website is designed to share information on established and emerging statistical methods for rigorously assessing and utilizing surrogate markers in clinical research.
Methods
There has been a tremendous amount of statistical and clinical research focused on evaluating and validating surrogate markers over the past 35 years. Although there is ongoing debate over the optimal evaluation method, the development of new approaches and insights has greatly enriched the field. There are three typically used frameworks for surrogate evaluation: the proportion of treatment effect explained framework, the principal stratification framework, and the meta-analytic framework. Each framework has its own strengths and limitations. Peer-reviewed statistical publications and software are available to implement each framework.
News
Each year, methodological advancements are made in the field of surrogate marker evaluation and use (in future studies). In addition, new drugs are approved based on demonstrated effectiveness on a surrogate marker on an ongoing basis.
Software
There exist several software packages and browser-based applications to evaluate surrogate markers using existing a new methods. The software packages require use of the programming language R. The browser-based applications still use R but do not require the user to directly run code in R.
LP Lab
The LP Lab is led by Dr. Layla Parast, an Associate Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. The LP Lab created and maintains this website, and is supported by funding from the National Institute of Diabetes and Digestive and Kidney Diseases. The lab includes undergraduate and graduate researchers working on developing and applying statistical methods for surrogate evaluation and appropriate use of surrogate markers in future trials to make decisions about treatment effectiveness.
