Software Application Profile: dynamicLM—a tool for performing dynamic risk prediction using a landmark supermodel for survival data under competing risks
AH Fries, E Choi, JT Wu, JH Lee… - International Journal …, 2023 - academic.oup.com
Motivation Providing a dynamic assessment of prognosis is essential for improved
personalized medicine. The landmark model for survival data provides a potentially powerful …
personalized medicine. The landmark model for survival data provides a potentially powerful …
[HTML][HTML] Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients
Z Geraili, K HajianTilaki, M Bayani… - Journal of Education …, 2024 - journals.lww.com
BACKGROUND: The objective of the present study is to evaluate the association between
longitudinal and survival outcomes in the presence of competing risk events. To illustrate the …
longitudinal and survival outcomes in the presence of competing risk events. To illustrate the …
Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research
Joint modeling of longitudinal data such as quality of life data and survival data is important
for palliative care researchers to draw efficient inferences because it can account for the …
for palliative care researchers to draw efficient inferences because it can account for the …
A joint model of the individual mean and within-subject variability of a longitudinal outcome with a competing risks time-to-event outcome
S Li, DS Nuyujukian, RL McClelland… - arXiv preprint arXiv …, 2023 - arxiv.org
Motivated by recent findings that within-subject (WS) visit-to-visit variabilities of longitudinal
biomarkers can be strong risk factors for health outcomes, this paper introduces and …
biomarkers can be strong risk factors for health outcomes, this paper introduces and …
A Joint Model of Competing Risks in Discrete Time with Longitudinal Information
AM Salazar, J Huertas - Revista Colombiana de Estadística, 2023 - revistas.unal.edu.co
The survival competing risks model in discrete time based on multinomial logistic
regression, proposed by Luo et al.(2016), models the non-linear and irregular shape of …
regression, proposed by Luo et al.(2016), models the non-linear and irregular shape of …
Latent Crohn's Disease Subgroups are Identified by Longitudinal Faecal Calprotectin Profiles
N Constantine-Cooke, K Monterrubio-Gómez, N Plevris… - medRxiv, 2022 - medrxiv.org
Background High faecal calprotectin is associated with poor outcomes in Crohn's disease.
Monitoring of faecal calprotectin trajectories could characterise disease progression before …
Monitoring of faecal calprotectin trajectories could characterise disease progression before …
[图书][B] New Approaches to Joint Modeling of Longitudinal and Time-to-Event Outcomes: with Applications to Dynamic Prediction of Health Outcomes Using Massive …
S Li - 2023 - search.proquest.com
It is often of interest to study the temporal patterns of longitudinal biomarker (s) that are
potentially correlated and predictive of time-to-event outcomes in biomedical studies. In this …
potentially correlated and predictive of time-to-event outcomes in biomedical studies. In this …
A Joint Model of Competing Risks in Discrete Time with Longitudinal Information.
A MARCELA SALAZAR… - Colombian Journal of …, 2023 - search.ebscohost.com
The survival competing risks model in discrete time based on multinomial logistic
regression, proposed by Luo et al.(2016), models the non-linear and irregular shape of …
regression, proposed by Luo et al.(2016), models the non-linear and irregular shape of …
[PDF][PDF] Research Article Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to …
S Li, N Li, H Wang, J Zhou, H Zhou, G Li - 2022 - hua-zhou.github.io
Semiparametric joint models of longitudinal and competing risk data are computationally
costly, and their current implementations do not scale well to massive biobank data. This …
costly, and their current implementations do not scale well to massive biobank data. This …