From Queries to Causality: Ensuring Design Integrity and Reproducibility in Real World Data

Real world data (RWD) in healthcare-including multi-institution electronic health records, claims, registries, and linked datasets-enables rapid, large-scale observational studies that inform clinical decision-making and policy. As volume and complexity of RWD grow, consulting and collaborating biostatisticians must lead and partner with interdisciplinary teams to shape study design, align causal questions with data realities, and ensure methodological rigor. Meeting these demands requires tools and workflows that support transparency, adaptability, and reproducibility. Real-world data platforms like TriNetX, Epic Cosmos, and Medeloop offer automated functionalities, sometimes including large language models (LLMs), that promise efficiency gains. However, these tools also introduce concerns around validity, documentation, and interpretability. This panel explores how consulting and collaborative biostatisticians can contribute principled and practical solutions including what to standardize, document, and scrutinize when working with RWD platforms, LLMs, and federated networks. We structure the panel around three themes: (i) Reproducibility in Dynamic Data Environments: Automation and LLMs can enhance efficiency, but evolving data pipelines, site heterogeneity, and constraints on patient-level export complicate reproducibility. We present practice recommendations such as snapshot/version governance, "cohort-as-code" with executable definitions, phenotype validation, preregistration of estimands and hypotheses, and shareable, auditable pipelines that help teams maintain rigor in automated settings. (ii) Causal Design Integrity in Automated Workflows: Automated cohort builders and LLM-assisted query generation can obscure critical design decisions such as inclusion/exclusion logic, index/eligibility windows, and code sets that implicitly define the target population. These decisions must be aligned with causal estimands (e.g., ATE, ATT/ATC, ATO; ITT-like vs per-protocol vs as-treated). We map RWD platform features (e.g., propensity score matching, outcome modeling, balance diagnostics) and LLM-generated code to causal assumptions (exchangeability, positivity, consistency), and expose pitfalls like immortal time bias, measurement error, and time-varying confounding. (iii) Team Science and Collaboration Implications: Automation and LLMs heighten, rather than replace, need for interdisciplinary teams. As RWD tools become more sophisticated, successful implementation depends on clear communication and coordinated roles. We outline team structures that support robust use of automated RWD platforms and LLMs: establishing shared vocabulary aligning population definitions, causal hypotheses, and analyses; dividing responsibilities across clinical domain leads, data stewards, and statisticians; and adopting transparent reporting, training, and governance to federated data. Attendees will gain improved understanding of (1) team science needs with RWD; (2) mapping platform features to assumptions and estimands; and (3) adopting reproducibility templates and team practices that position collaborative biostatisticians to lead efficient research.