How Advanced Statistical Methods Can Transform Patient Outcomes

Healthcare organizations have access to more data than ever before. Whereas in the past they were mostly limited to data from labs and electronic data capture (EDC) systems, they now have vast swaths of data from electronic health records (EHRs), wearable devices, and other real-world sources at their disposal.

This data will only continue to grow in volume and accessibility. For example, the wearable medical devices market is projected to reach $156 billion by 2032, and over 80 percent of users say they would share their data with a healthcare provider. Furthermore, nearly 90 percent of office-based physicians now use EHRs, making a wealth of patient data more readily available.

This influx of data creates exciting new opportunities to gain deeper insights into the effects of various treatments over time, and ultimately drive breakthroughs in patient care. In fact, regulatory bodies like the FDA are increasingly encouraging the use of real-world evidence (RWE)—like data from wearables and EHRs—to supplement clinical trials. But life sciences organizations need ways to effectively wrangle and analyze these droves of data before they can provide such value, and that requires the right technology.

Advanced statistical methods, like Bayesian inference and mixed-effects models, have become central to harnessing this data to fuel modern clinical research, especially within the context of adaptive trial designs and longitudinal data analysis. Below, we’ll explore how these methods are enabling deeper insights over time, and how supportive technology makes it all possible.

The benefits of advanced statistical methods

The success of adaptive trial designs and longitudinal data analysis hinges on ongoing data collection and interim analysis over time. This makes them a prime use case for advanced statistical methods like Bayesian inference and mixed-effects models.

With these methods, researchers can uncover critical insights in longitudinal studies, make adjustments to clinical trials in real time, and use synthetic endpoints to supplement real-world data—all while maintaining statistical rigor. This results in faster trial timelines, the ability to conduct a greater number and variety of trials (including those on rare diseases), and ultimately better patient outcomes.

 Here’s what it looks like in action:

 Bayesian inference

Bayesian inference works by updating the probability of a hypothesis as new evidence becomes available, combining prior beliefs with observed data using Bayes' theorem. This is especially useful in adaptive clinical trials. As more information on the effects of treatment emerges throughout the course of the trial, researchers can make adjustments or course-correct if needed to support the best possible outcome for patients.

For example, in an adaptive oncology trial comparing multiple experimental drugs, researchers might use Bayesian inference to update how likely it is each drug will work as new data is made available. If one drug shows strong promise early on, they may opt to adjust the trial by enrolling more patients into that group and/or pulling back on the less effective drugs sooner. Bayesian inference enables adjustments without compromising the statistical validity of the trial.

Mixed-effects models

Mixed-effects models account for both fixed effects that are consistent across all observations, in addition to random effects, which capture variability across groups or individuals. This is invaluable in longitudinal data analysis, where factors like participant dropouts and the natural correlations between repeated measurements on the same subject over time can threaten the validity of study findings if not properly addressed.

For instance, a researcher studying how diabetes progresses might use a mixed-effects model to analyze patients’ blood sugar levels over the course of several years. Using a mixed-effects model in this scenario would account for fixed effects, like treatment type, in addition to random effects, like the natural variability that exists in patients’ baseline glucose levels.

The role of supportive technology

Advanced statistical methods are essential for analyzing the huge amounts of data available to life sciences organizations today. But these methods require significant computational power to carry out their analyses. Solutions that provide easy, cost-effective access to high-power compute are essential for using advanced statistical methods at scale.

Additionally, life sciences organizations need tools that facilitate collaboration. Having a secure, centralized workspace lets statisticians, data scientists, and clinical researchers securely access shared data, tools, and compute environments, allowing them to work in parallel while maintaining compliance and governance. As advanced statistical methods help life sciences organizations conduct a higher volume of studies and clinical trials, they’ll also need modern ways to share their findings and collaborate with key stakeholders outside of traditional, document-centric approaches like CSV files and PDFs.

Finally, life sciences organizations need technology that upholds the traceability and reproducibility of their work so that it is explainable both internally and externally. Explainability is essential not only for internal quality control, but also to meet the rigorous documentation standards required by regulatory bodies.

The promise of advanced statistical methods is clear, but healthcare organizations need the right technology in place to realize their full potential. Things like scalable compute, collaborative workspaces, and traceable workflows are critical for turning mountains of data into insights that enable better patient outcomes and ultimately turn today’s research into tomorrow’s lifesaving breakthroughs.

Chris McSpiritt is vice president, life sciences strategy, at Domino Data Lab.