Complex Data Sets in Genomic Diagnostics Require Multiple Analytic Methods
Revolution Helps Accelerate Process, Reducing Project Time
CardioDx is a cardiovascular genomic diagnostics company located in Palo Alto. The company fuses expertise in genomics, biostatistics, and cardiology to develop clinically validated genomic tests that aid in assessing and tailoring care of individuals with cardiovascular disease, including coronary artery disease (CAD), cardiac arrhythmias, and heart failure.
Analyzing complex clinical data from thousands of patients, and leveraging the results to built diagnostic algorithms used by physicians to determine the likelihood that their patients have obstructive coronary artery disease.
CardioDX’s biostatisticians rely on a wide range of R packages to run multiple analyses of large data sets from clinical trials. “We use a variety of statistical modeling techniques,” says Michael Elashoff, the company’s director of biostatistics. “We tend to use newer statistical methods because we’re dealing with large numbers of potential variables. It’s a significant analytical challenge.”
The complexity of the data makes it virtually impossible to determine in advance precisely which analytic method will provide the most useful results. As a consequence, the biostatistics team runs thousands – and in some instances, millions – of analyses to achieve the results it needs to complete its work successfully. “There’s no road map showing us the best method,” says Elashoff. “Selecting the important variables, while ignoring all the rest, is a difficult statistical problem. Most often we run a whole series of analyses. What we see in the first ten analyses determines the additional steps we’ll take next. It’s a very iterative process.”
The availability of numerous R packages developed by the open-source R community makes it easier to find the best methodology for solving the particular challenges posed by the data sets. “We use R for all of our analysis,” says Elashoff. “I think it’s fair to say that R really is the foundation of a lot of the work that we do.”
To speed up the process without sacrificing accuracy, the team also uses Revolution R analytic products. “We use R seven or eight hours per day, so any improvement in speed is helpful, particularly when you’re looking at a million biomarkers and wondering if you’ll need to re-run a million analyses.”
Open-source R packages enable the biostatisticians at CardioDX to run a broad range of analyses, accurately and effectively, on a routine basis. Adding Revolution R products to the mix improves processing speeds and makes it easier to crunch large data sets. Accelerating the analytic process reduces overall project time, increasing the team’s efficiency. “Revolution R is faster than regular R,” says Elashoff. “The faster we can analyze data, the less time it takes us to build our diagnostic algorithms.”
About Revolution Analytics
Revolution Analytics was founded in 2007 to foster the R community, as well as support the growing needs of commercial users. Our name derives from combining the letter "R" with the word "evolution." It speaks to the ongoing development of the R language from an open-source academic research tool into commercial applications for industrial use.
Though our Revolution R products, we aim to make the power of predictive analytics accessible to every type of user & budget. We provide free and premium software and services that bring high-performance, productivity and ease-of-use to R – enabling statisticians and scientists to derive greater meaning from large sets of critical data in record time.
We also offer our full-featured production-grade software to the academic community for FREE, in order to support the continued spread of R's popularity to the next generation of analysts.
For customers such as Pfizer, Novartis, Yale Cancer Center, Bank of America and others, our flagship Revolution R Enterprise product stands for faster drug development, reduced time of data analysis, and more powerful and efficient financial models.