Gain unmatched insight into safety and efficacy data at every phase of the drug development process. Register for this complimentary info kit and see how the latest version of JMP Clinical lets you:
A smarter approach to monitoring clinical trials
Watch this on-demand webinar and see how JMP Clinical enables the centralized review of clinical trial data and other site-level metrics, helping you determine where extensive on-site quality review is warranted.
JMP Clinical expert Richard Zink, PhD, outlines the process for risk-based monitoring introduced in JMP Clinical 5 and explains how these unique capabilities protect the integrity of clinical trials while controlling costs, so you work smarter, not harder. Read his multi-part blog series.
Is it just bad data, or could it be fraud?
Fraud in clinical trials is thought to be relatively rare. But instances of misconduct may have gone undetected due to over-reliance on the manual monitoring of clinical trial data to assess quality and a lack of statistical and graphical tools. In this webinar, JMP Clinical expert Richard Zink, PhD, discusses a centralized approach to clinical trial data review in JMP Clinical and describes some analyses to identify patient- and investigator-perpetrated fraud.
Is your trial data hiding safety issues?
Summarize count and frequency of adverse events. Visually track trends in labs and vitals across time. Identify patients at risk for liver injury. Generate patient profiles, tables and auto-generated narratives. JMP Clinical brings unmatched statistical power to these tasks and more. This on-demand webinar also shows how these reports can be combined in tailored data review that allows clinicians to quickly and efficiently monitor safety concerns in a clinical trial.
Bring unmatched statistical power to your trials.
This webinar explores a variety of statistical capabilities in JMP Clinical that enable biostatisticians to identify, analyze and then share information about events, findings and interventions in clinical trials. Examples will include adverse event signal detection methods with multiplicity adjustments, Bayesian Hierarchical models for rare adverse event detection, benefit-risk assessment, repeated measures models for findings data, meta-analysis, and crossover analysis capabilities.
See patterns, predict outcomes in clinical data
Industry-leading predictive modeling methods in JMP Clinical and JMP Genomics offer a broad array of methods as well as options for predictor filtering, model comparison, and cross-validation. The software guides statistical researchers through comprehensive tools to build, test and cross-validate models in a flexible framework for applications in clinical trials, biomarker analysis, genomic and agronomic research. Pattern discovery capabilities in the software include clustering techniques and correlation techniques that identify individuals or subgroups of patients who are at risk of serious safety issues that could stop the trial.