Observational studyStatistics in medicine2021
Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high-dimensional data.
Observational study in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Evaluation of a serum protein signature as monitoring biomarker for Duchenne muscular dystrophy in a long-term clinical trial with corticosteroids.Skeletal muscle · 2026Trial
- Evaluation of a serum protein signature as monitoring biomarker for Duchenne Muscular Dystrophy in a long-term clinical trial with corticosteroids.medRxiv : the preprint server for health sciences · 2026Article
- Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach.Statistics in medicine · 2025Article
- A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data.Biometrics · 2025Article
- Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.Statistical methods in medical research · 2024Article
- Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high-dimensional data.Statistics in medicine · 2021Observational
- Peripheral blood transcriptome profiling enables monitoring disease progression in dystrophic mice and patients.EMBO molecular medicine · 2021Article
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Authors and funding
5 authors.
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Abstract
Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ensure a proper internal validation of the fitted PRC models, we furthermore develop a cluster bootstrap optimism correction procedure that allows to correct for the optimistic bias of apparent measures of predictiveness. PRC and the CBOCP are implemented in the R package pencal, available from CRAN. After studying the behavior of PRC via simulations, we conclude by illustrating an application of PRC to data from an observational study that involved patients affected by Duchenne muscular dystrophy, where the goal is predict time to loss of ambulation using longitudinal blood biomarkers.
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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.