Health Outcomes Analysis and Publications

When it comes to evidence-based medicine, hypothesis testing and hypothesis generation are opposite sides of the same coin for discerning the "best evidence". Hypothesis testing determines those answers obtained through the utmost rigor; hypothesis generation determines those questions that may be the most important to ask. On the surface they appear similar; however, they involve different approaches and disciplines. ProSanos has developed unique, in-depth expertise in each discipline.

Biometric Services (Hypothesis Testing)
ProSanos' experienced biostatisticians and SAS programmers work closely with physicians and clinical pharmacists. These integrated teams have familiarity with a range of clinical settings - from small prospective clinical trials to large registry data repositories and electronic health record information with more than 1,000,000 patients - and a thorough understanding of FDA and ICH GCP requirements.

Data Mining (Hypothesis Generation)
Data mining (sometimes called exploratory data analysis) uses a variety of data analysis tools to discover previously unknown patterns and relationships in data. Data mining is a process; not a particular technique or algorithm. The goal of data mining is knowledge discovery, by describing (previously unknown) data characteristics and predicting future results by generalizing a discovered pattern to other data.

Sophisticated tools are now available for data mining clinical data. In the right hands, these tools can provide valuable information to the scientific community (via medical publications, manuscripts, CME and new observations), as well as augment and focus traditional analysis, and can do so in a way that is robust and can be validated. ProSanos has a unique approach to data mining that is tailored to clinical and healthcare data sets. At its foundation is a robust clinical data integration, management, and visualization software platform. We have the expertise, the tools, the techniques, and the experience to be your partner for data mining projects.

Other Services Offered by ProSanos

Safety & Risk Management

CLÆRITY^®

Observational Pharmacovigilance

PV Plan

PVmap™ of the Week

Registries & Observational Studies

Health Outcomes Analysis & Publications

The Importance of Appropriate Visualization:

The plot above describes percent of total cholesterol improvement at 12 weeks versus 52 weeks for patients enrolled in an intensive lifestyle modification program. From this visual summarization of the data, it is easy to see that there are few "late bloomers", e.g., patients showing improvement at 52 weeks who did not also show improvement at 12 weeks. Because the program was time and resource intensive (and expensive), this finding was important - patients who did not show improvement at 12 weeks could be re-directed to an alternative form of therapy.