Scalable confounding adjustment in real-world evidence: benchmarking data-adaptive and investigator-specified...

Real-world evidence (RWE) increasingly informs clinical decisions, yet manual adjustment for confounding limits scalability. Data-adaptive (DA) algorithms for high-dimensional proxy adjustment show promise but have not been systematically compared to investigator-specified (IS) approaches across diverse treatment scenarios. We evaluated whether DA strategies perform comparably to manually curated IS models using claims-based emulations of 15 randomized trials from the RCT-DUPLICATE initiative.

We identified new-user cohorts for 15 trial emulations in Optum's de-identified Clinformatics Data Mart Database (2004-2023). Treatment effects were estimated using 3 adjustment strategies: (1) IS models with manually tailored covariates; (2) full-DA strategies using empirical features from semiautomated pipelines; and (3) hybrid-DA models incorporating both empirical and investigator-defined covariates. Agreement with RCT benchmarks was assessed via binary metrics and difference-in-differences.

Outcome-adaptive LASSO achieved better RWE-RCT agreement than IS adjustment in 73% of full-DA and 87% of hybrid-DA emulations. Other DA methods considering feature associations with both treatment and outcome performed similarly well, while models tuned solely for treatment prediction performed poorly. Performance of IS vs DA strategies differed across emulated trials.

Top DA algorithms matched manual IS models on average, but impact varied by emulation. Case studies illustrate the continued importance of subject-matter knowledge, particularly for complex treatment strategies.

Data-adaptive algorithms show promise for scalable confounding adjustment in large-scale evidence systems and as augmentation tools for investigator-specified designs. Hybrid strategies combining algorithmic methods with investigator expertise offer the most reliable approach for individual causal questions.