The New England Journal of Statistics in Data Science

Artificial intelligence (AI) holds great promise for supporting clinical trials, from patient recruitment and endpoint assessment to treatment response prediction. However, deploying AI without safeguards poses significant risks, particularly when evaluating patient endpoints that directly impact trial conclusions. We compared two AI frameworks against human-only assessment for medical image-based disease evaluation, measuring cost, accuracy, robustness, and generalization ability. To stress-test these frameworks, we injected bad models—ranging from random guesses to naive predictions—to ensure that observed treatment effects remain valid even under severe model degradation. We evaluated the frameworks using two randomized controlled trials with endpoints derived from spinal X-ray images. Our findings indicate that using AI as a supporting reader (AI-SR) is the most suitable approach for clinical trials, as it meets all criteria across various model types, even with bad models. This method consistently provides reliable disease estimation, preserves clinical trial treatment effect estimates and conclusions, and retains these advantages when applied to different populations.

Prompted by social upheaval in 2019, Chile initiated its first foray into entirely online education. Subsequently, owing to the global pandemic in 2020, all activities underwent an unavoidable shift to digital platforms, both on a global scale and within the university context. In response to this novel distance-learning system, the university encountered and surmounted new challenges during the four semesters that were conducted in this modality. This paper examines from a spatial statistics perspective the academic performance of first-year students enrolled in basic science courses across all engineering majors at Universidad Técnica Federico Santa María (USM) in Chile. This article emphasizes students’ performance as a georeferenced variable in space and compares it with a regular semester in which lectures are conducted in a face-to-face format. In particular, we discuss (a) the spatial patterns observed in the two largest cities, (b) the social variables that are pertinent for the study, and (c) the quality and performance of conditionally autoregressive (CAR)-type processes in modeling multivariate lattice data. We also reflect on the opportunity to enhance the learning experience linked to the retention rate of freshman engineering students.

Disease registries often involve cohorts of patients seen repeatedly at tertiary care centers with biospecimens collected at periodic clinic visits. With such registries it is possible to study the association between time-dependent biomarkers and a failure time of interest through Cox regression. However it can be prohibitively expensive or labor-intensive to assay all biospecimens for all members of the registry and in practice it is common to select a single biosample to assay for each individual. We derive the asymptotic bias of estimators obtained from the Cox model based on common strategies. We investigate the nature of this bias from a joint multistate model for the marker and failure processes. Alternative selection methods are then developed for consistent and efficient sub-sampling under budgetary constraints. The asymptotic relative efficiency of regression coefficients obtained from the Fisher information is then explored and an optimal design is identified within a class of designs. The proposed selection method is illustrated in a registry of patients with psoriatic arthritis where we investigate the association between a time-dependent biomarker (ESR_CRP) and the risk of developing arthritis mutilans.