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In clinical research, few problems cause more hidden damage than confounding. It distorts treatment effects, creates false associations, and leads investigators to conclusions that are statistically valid, but scientifically wrong. What makes confounding especially dangerous is that it often remains invisible until long after the paper is published. Fortunately, you do not need advanced modeling to detect it. Many of the most powerful warning signs of confounding appear long

Just like flicking a light switch, OpenSimplify lets you illuminate your data in an instant, no steep learning curve, no buried code, just insights on demand.

Discover how OpenSimplify helps researchers go from raw data to publication-ready manuscripts, automatically generating tables, summaries, and literature comparisons to streamline the publishing process.

In the world of scientific publishing, getting from data to a polished manuscript is often a slow, manual, and error-prone process. Researchers spend hours cleaning tables, formatting statistics, and searching through existing literature to support their findings. But what if your manuscript could start building itself the moment your analysis ends? OpenSimplify is changing the game by automating one of the most tedious parts of research: translating raw results into clean &

We are excited to announce that OpenSimplify’s MyAnalyst (Clinical Reports v1.2.0) was recently cited in a peer-reviewed clinical study conducted by researchers at Albert Einstein College of Medicine. The paper, Clinical Features and Surgical Outcomes in Patients with Symptomatic Carotid Artery Webs, explored risk factors and surgical outcomes using advanced statistical modeling. And they used MyAnalyst to get there.

Clinical researchers today are under more pressure than ever to generate insights quickly, accurately, and at scale. But behind every breakthrough finding is a long list of tedious, time-consuming tasks that can drain resources and delay progress. At OpenSimplify, we’ve worked with researchers across hospitals, academic institutions, and CROs and the same five bottlenecks keep showing up. The good news? These can now be automated with no-code tools like OpenSimplify.

Clinical research has the power to transform medicine, but anyone who’s worked with research data knows it’s no walk in the park. From messy datasets to regulatory minefields, clinical research data analysis brings its own unique set of headaches. Let’s explore the top five challenges researchers face and more importantly, how OpenSimplify helps solve them using powerful, AI-driven solutions for healthcare analytics.