Overview
Promises and pitfalls of using predicted data for downstream inference
Background and Motivation
Artificial intelligence and machine learning (AI/ML) have become essential tools in biomedical research, enabling large-scale analyses across diverse domains such as genomics, structural biology, and electronic health records-based research. Increasingly, researchers rely on model-generated predictions, rather than directly measured variables, as inputs for downstream statistical analyses. For example, predicted gene expression values or polygenic risk scores are often used in place of experimental assays, allowing researchers to expand cohort sizes and explore hypotheses when traditional data collection is infeasible, costly, or time-consuming.
While this practice of “using predictions as data” holds promise for accelerating scientific discovery, it presents significant challenges for statistical inference. When predicted values are used in place of true variables, the resulting estimates of association can be biased and misleading if uncertainty in the prediction step is not properly accounted for.
Modern biomedical analyses increasingly rely on machine learning predictions as inputs to downstream statistical models. This can expand scope and improve feasibility, but treating predictions as if they were observed data can bias effect estimates and understate uncertainty.