An overview of joint modeling of time-to-event and longitudinal outcomes
In this review, we present an overview of joint models for longitudinal and time-to-event data.
We introduce a generalized formulation for the joint model that incorporates multiple …
We introduce a generalized formulation for the joint model that incorporates multiple …
Individualized tumor-informed circulating tumor DNA analysis for postoperative monitoring of non-small cell lung cancer
K Chen, F Yang, H Shen, C Wang, X Li, O Chervova… - Cancer cell, 2023 - cell.com
We report a personalized tumor-informed technology, Patient-specific pROgnostic and
Potential tHErapeutic marker Tracking (PROPHET) using deep sequencing of 50 patient …
Potential tHErapeutic marker Tracking (PROPHET) using deep sequencing of 50 patient …
Dynamic-deephit: A deep learning approach for dynamic survival analysis with competing risks based on longitudinal data
Currently available risk prediction methods are limited in their ability to deal with complex,
heterogeneous, and longitudinal data such as that available in primary care records, or in …
heterogeneous, and longitudinal data such as that available in primary care records, or in …
Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for Cox proportional hazards models
DJ McLernon, D Giardiello, B Van Calster… - Annals of internal …, 2023 - acpjournals.org
Risk prediction models need thorough validation to assess their performance. Validation of
models for survival outcomes poses challenges due to the censoring of observations and …
models for survival outcomes poses challenges due to the censoring of observations and …
Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC
Accurately evaluating minimal residual disease (MRD) could facilitate early intervention and
personalized adjuvant therapies. Here, using ultradeep targeted next-generation …
personalized adjuvant therapies. Here, using ultradeep targeted next-generation …
Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis
Background Clinical research and medical practice can be advanced through the prediction
of an individual's health state, trajectory, and responses to treatments. However, the majority …
of an individual's health state, trajectory, and responses to treatments. However, the majority …
Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker
KL Pickett, K Suresh, KR Campbell, S Davis… - BMC medical research …, 2021 - Springer
Background Risk prediction models for time-to-event outcomes play a vital role in
personalized decision-making. A patient's biomarker values, such as medical lab results, are …
personalized decision-making. A patient's biomarker values, such as medical lab results, are …
Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods
Abstract Background Clinical prediction models (CPMs) predict the risk of health outcomes
for individual patients. The majority of existing CPMs only harness cross-sectional patient …
for individual patients. The majority of existing CPMs only harness cross-sectional patient …
Multivariate joint models for the dynamic prediction of psychosis in individuals with clinical high risk
This study attempted to construct and validate dynamic prediction via multivariate joint
models and compare the prognostic performance of these models to both static and …
models and compare the prognostic performance of these models to both static and …
[HTML][HTML] Joint models for longitudinal and discrete survival data in credit scoring
The inclusion of time-varying covariates into survival analysis has led to better predictions of
the time to default in behavioural credit scoring models. However, when these time-varying …
the time to default in behavioural credit scoring models. However, when these time-varying …