Annex Clinical consolidates clinical trial quality management strategies with computer data science to develop and execute a risk mitigation prediction method for protocol and study design that is optimized to your company’s operational models. This approach Improves study design through continuous prospective design rather than fixed retrospective analysis.
1. Clinical Trial Data Feasibility
Developing predictive risk models requires historical clinical trial data, and models with strong predictability require more information. Annex Clinical can help determine whether you have sufficient data to develop predictive risk models.
2. Aggregate and Explore Study Data
Clinical study data can be muddled. Annex Clinical helps you navigate this jumbled disarray by underst anding where to look for and find significant data, and which technologies to bring in to aggregate the data. Once clinical trial data is aggregated (or is already accessible), Annex Clinical aligns its quality categorization structure to develop an integrated quality management planning system (IQRMP).
3. Generate Predictive Models
When research objectives are defined, Annex Clinical leverages computer data science and novel statistical techniques to explore the data in order to generate the most powerful predictive models that require the minimum quantity of data for maximum study performance.
4. Execute Predictive Models
Generating strong predictive models is not enough. Study teams have to be able to access a user-friendly system. Annex Clinical executes the predictive models through customized workflow designs using open-source technology.
5. Optimize Predictive Models
Developing predictive models on historical study data yields a categorical level of strength and confidence. Annex Clinical intensifies the power of these models by continuing to feed fresh study data into the design to leverage more powerful statistical applications that yield optimum predictive results.