Using mixture cure models to address algorithmic bias in diagnostic timing: autism as a test case.

Objectives: To address algorithmic bias in clinical prediction models related to the timing of diagnosis, we evaluated the efficacy of mixture cure models that integrate time-to-event and binary classification frameworks to predict diagnoses. Materials and Methods: We conducted a simulation and analyzed real-world North Carolina Medicaid data for children born in 2014, followed until 2023. The study evaluated traditional time-to-event and classification models against mixture cure models under scenarios with varied diagnostic timing and censoring. Results: Simulation results demonstrated that traditional models exhibit increased bias as diagnosis timing differences widened, whereas mixture cure models yielded unbiased estimates across varying censoring times. In real-world analyses, significant racial and ethnic variations in autism diagnosis rates were observed, with non-Hispanic White children having higher diagnosis rates compared to other groups. The mixture cure model effectively adjusted for these disparities, providing fairer and more accurate diagnostic predictions across varying levels of censoring. Discussion: Mixture cure models effectively address algorithmic bias by providing unbiased estimates regardless of variations in diagnostic timing and censoring, making them particularly suitable for conditions like autism where not all individuals will receive a diagnosis. This approach shifts focus from when an event will occur to whether it will occur, aligning more closely with clinical needs in early detection of pediatric developmental conditions. Conclusion: Mixture cure models offer a promising tool to enhance accuracy and fairness in predictive modeling, especially when the outcome of interest is not uniformly observed across groups.