Beyond Participant-Level Cross-Validation: Reliable Inference for Longitudinal Machine Learning

Abstract

Longitudinal sensing studies routinely collect thousands of windows from a few dozen participants. The records are numerous; the independent scientific units are not. When the outcome is defined per participant, this mismatch makes apparently precise findings vulnerable to pseudo-replication, to partition choice, and to the ordinary analytic flexibility of comparing several pipelines before reporting one. Splitting on participants prevents a person's records from straddling a split, but it does not calibrate the label-dependent workflow fold construction, preprocessing, tuning, calibration, and candidate selection that produced the reported number. We define a participant-level estimand and obtain an analysis-matched null by permuting participant labels and rerunning that entire workflow. In controlled simulation, window-level inference rejects in 70-80% of replicates when no effect exists and a window bootstrap rejects at the same rate; a participant bootstrap still rejects at 10-17%; the analysis-matched test holds 0.025-0.100 across cohorts of 20 to 80 participants. Freezing the selected pipeline instead of repeating the search inflates Type-I error to 0.240 with eight candidates, where repeating it holds 0.040. Applied to two public cohorts, wrist actigraphy (n=55) yields participant AUROC 0.928 with p=0.0050, a conclusion that persists under a scale-robust rank-pooled statistic and under a matched permutation null computed after excluding hospitalized participants (p=0.0089). Smartphone sensing (n=38, 7 positives) yields 0.636 and does not reject (p=0.1724) despite sufficient resolution, with sensitivity 0.143. The practical rule is narrow: every step that reads labels belongs inside the permuted analysis, and repeated records do not create additional independent participants.