Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models
In routine care, individuals identified a priori as high-risk are usually tested for conditions more frequently. Protected attributes, such as sex or ethnicity may also determine testing frequency. Such heterogeneous detection rates across a population induce label error. This causes systematic model error for specific groups and biases performance metrics during validation. This paper proposes a method to correct for such bias in prediction models due to differential diagnostic delay. We use a causal inference framework to define our target estimand: an individual's diagnosis probability in a counterfactual scenario where their diagnosis rate matches that of a reference group. We model t...
| Authors | Jose Benitez-Aurioles, Ricardo Silva, Brian McMillan et al. |
|---|---|
| Primary category | stat.AP |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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