Summarized from: https://www.hcinnovationgroup.com/analytics-ai/blog/55299830/how-advanced-statistical-methods-can-transform-patient-outcomes-and-operations
Written by: Chris McSpritt
healthcare orgs, in the past, had limited data from labs and electronic data capture (EDC) systems.
NOW, they have ‘vast swaths’ of data from EHRs, wearables, and other real-world sources.
80% of wearables say they’re willing to share wearable data with healthcare provider.
90% of office-based physicians now use EHR’s.
this influx of data creates exciting new opportunities to gain deeper insights into effects of various treatments over time, and ultimately drive breakthroughs in patient care.
regulatory bodies like FDA are also increasingly encouraging the use of real-world evidence (RWE)
Advanced statistical methods, like Bayesian inference and mixed-effects models have become central to harnessing data to fuel modern clinical research, especially given context of adaptive trial designs and longitudinal data analysis.
Bayesian inference & mixed-effects models are prime use cases for ongoing data collection + interim analysis over time.
- Those models help spot trends/patterns earlier enabling adjustments to clinical trials in real time, without compromising the study’s validity.
- synthetic endpoints are model-derived outcomes (i.e. predicted survival time) based on existing data. – these endpoints can fill gaps where real-world outcomes take too long to observe, or are hard to measure.
- it does all that, while maintaining statistical rigor.
Bayesian Inference = updates probability of a hypothesis as new data becomes available, combining prior beliefs with observed data using bayes’ theorem.
mixed-effects models = account for both fixed effects that are consistent across all observations, PLUS random effects (capturing variability across groups/individuals). invaluable in longitudinal data analysis, where participant dropouts and natural correlations between repeated measurements on same subject over time can threaten validity of study findings.
Things that are needed, in order to integrate advanced statistical measures for analysis:
- Advanced statistical methods need high computational power; scalable and cost-effective computing solutions are essential to apply them at scale.
- Collaborative, secure workspaces enable teams to share data and tools efficiently, ensuring compliance while moving beyond outdated file formats like CSVs and PDFs.
- Technology must support traceability and reproducibility to ensure explainability, meet regulatory standards, and maintain internal quality control.
- To fully harness advanced statistical methods, organizations need scalable compute, collaborative tools, and traceable workflows that turn big data into impactful medical breakthroughs.