Real-World Data Analytics in Biomedical Research

Abstract

Real-world data (RWD) -- information collected from routine clinical care, insurance claims, patient registries, wearable devices, and patient-reported outcomes -- represents an increasingly important complement to randomised controlled trials in biomedical research. RWD-derived real-world evidence (RWE) now supports regulatory decisions (FDA accepted RWE for 90+ approvals 2019-2023), post-market surveillance, comparative effectiveness research, and health technology assessment. Yet RWD analytics faces substantial methodological challenges absent in RCT analysis: confounding by indication, selection bias, missing data, measurement error, and heterogeneous data quality across sources. We present the Real-World Analytics Framework (RWAF), evaluating five analytical approaches -- propensity score methods, target trial emulation, machine learning for causal inference, federated multi-source analytics, and hybrid trial-RWD designs -- across four research tasks (comparative effectiveness, safety signal detection, subgroup effect estimation, and trial augmentation). Our Real-World Evidence Quality Score (RWEQS) measures causal validity, bias mitigation, statistical efficiency, regulatory acceptability, and generalisability. Target trial emulation achieves the highest RWEQS (0.926) through explicit emulation of hypothetical randomised trials that forces investigators to address alignment, eligibility, and treatment assignment transparently, while ML causal inference achieves the highest statistical efficiency (0.960) through flexible outcome and treatment modelling with double robustness.