ArticleJAMIA open2026
Deep learning survival analysis with time-varying covariates: extending PyCox to assess COVID-19 antiviral treatments and long-COVID associations.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To extend the PyCox to accommodate time-varying covariates using counting process data, addressing immortal time bias in survival analysis. Materials and methods: We modified PyCox to support counting process data structures for time-varying covariates and applied it to 2 246 913 Medicare beneficiaries aged ≥65 with COVID-19 (January-September 2022; 2 785 807 longitudinal records). We compared traditional Cox regression, original PyCox, and modified PyCox in estimating associations between early antiviral treatment (nirmatrelvir or molnupiravir) and long-COVID. Performance metrics included concordance index (C-index), integrated Brier score (IBS), and integrated negative binomial log-likelihood (IBLL) and time-dependent Area Under the Receiver Operating Characteristic Curve (AUC), Brier score, and permutation importance. Results: Among patients, 19.5% received nirmatrelvir, 2.6% received molnupiravir, and 14% developed long-COVID. Traditional Cox and modified PyCox produced concordant hazard ratios (HR) for nirmatrelvir (0.874 and 0.878) and molnupiravir (0.909 and 0.918); original PyCox estimated stronger associations (HR = 0.822 and 0.879). The treatment-effect difference formed a gradient: 3.5-4.0 percentage-points for time-varying models, 4.6 for time-fixed Cox, and 5.7 for time-fixed PyCox. Discrimination, calibration, and fit were comparable overall; modified PyCox showed higher time-dependent AUCs (0.536-0.609) than time-fixed PyCox (0.481-0.519) and relied on largely different top predictors. Modified and original PyCox trained in 1 min 14 s and 3 min 18 s, vs 5 min for traditional Cox. Discussion: Treatment-contrast magnitude and covariate importance varied by models, suggesting temporal covariate structure and modeling approach jointly influence treatment-effect estimates. Conclusion: Extending PyCox to accommodate time-varying covariates improves computational efficiency while maintaining estimation accuracy comparable to standard time-varying methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.