New versions of JMP® life sciences solutions fuel discoveries in clinical trials, genomics research
JMP® Clinical adds risk-based monitoring; JMP® Genomics extends tools for prediction of outcomes
Risk-based monitoring methods new in JMP® Clinical software from SAS limit costly on-site audits of source data during clinical trials while protecting study participants. JMP Genomics now offers enhanced capabilities for analyzing data related to agriculture, pharmacogenomics, biotechnology and more.
JMP Clinical 5.0 and JMP Genomics 7.0 were introduced today at the Bio-IT World Conference and Expo in Boston.
Both solutions integrate sophisticated SAS® statistical algorithms with interactive JMP data visualization to make discovery from life sciences data faster and easier. JMP is a business unit of SAS, the leader in business analytics software and services.
“This new version of JMP Clinical enables our customers to effectively monitor and statistically analyze safety and efficacy data. More than that, it reduces risk and identifies possible misconduct at the patient, site or country level,” said JMP Product Manager Geoff Mann. “For the first time, all of that is under one hood."
New and enhanced features in JMP Clinical 5.0:
- Evaluate risk efficiently, limiting the need for on-site monitoring that can account for 30 percent of clinical trial costs.
- Easily identify errors and fraud pertaining to individual subjects and clinical sites.
- Analyze data for safety issues at the trial level and for individual patients.
- Identify and analyze events, findings and interventions.
- Reveal patterns and predict outcomes from clinical trial data.
- Facilitate information sharing among colleagues.
JMP Genomics 7.0 includes methods to:
- Correlate clinical phenotypes with rare or common genetic variants.
- Analyze gene expression, miRNA or metabolomics data for differential values between experimental classes.
- Normalize and analyze NGS data with a wide variety of options.
- Discover biomarker profiles from high-dimensional data sets through cross-validation and model comparisons.
- Create pathway scores from gene expression data and explore with ANOVA or clustering methods.
What customers are saying:
“From my perspective, JMP Clinical has become absolutely essential to an efficient, cost-effective drug-development process.” Mark Williams, Vice President and CIO, Applied Clinical Intelligence.
“There’s no way I could have done this work with typical statistical programs and methods. JMP Genomics was key to its success.” Ray Langley, Lovelace Respiratory Research Institute, author of a 2013 article in Science Translational Medicine about a study that led to development of a blood test for early identification of life-threatening forms of sepsis.
SAS created JMP in 1989 to empower scientists and engineers to explore data visually. Since then, JMP has grown from a single product into a family of statistical discovery tools, each one tailored to meet specific needs. John Sall, SAS co-founder and Executive Vice President, heads the JMP business unit.