On longitudinal prediction with time-to-event outcome: comparison of modeling options
Long-term follow-up is common in many medical investigations where the interest lies in
predicting patients' risks for a future adverse outcome using repeatedly measured predictors …
predicting patients' risks for a future adverse outcome using repeatedly measured predictors …
[PDF][PDF] Random Forest for Dynamic Risk Prediction or Recurrent Events: A Pseudo-Observation Approach
Recurrent events are common in clinical, healthcare, social and behavioral studies. A recent
analysis framework for potentially censored recurrent event data is to construct a censored …
analysis framework for potentially censored recurrent event data is to construct a censored …
[HTML][HTML] Dynamic prediction of repeated events data based on landmarking model: application to colorectal liver metastases data
I Yokota, Y Matsuyama - BMC medical research methodology, 2019 - Springer
Background In some clinical situations, patients experience repeated events of the same
type. Among these, cancer recurrences can result in terminal events such as death …
type. Among these, cancer recurrences can result in terminal events such as death …
Baseline age effect on parameter estimates in Cox models
The Cox proportional hazards model is widely used in time-to-event analysis. Two time
scales are used in practice: time-on-study and chronological age. The former is the most …
scales are used in practice: time-on-study and chronological age. The former is the most …
[HTML][HTML] Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical …
N Ternès, F Rotolo, S Michiels - BMC medical research methodology, 2017 - Springer
Background Thanks to the advances in genomics and targeted treatments, more and more
prediction models based on biomarkers are being developed to predict potential benefit …
prediction models based on biomarkers are being developed to predict potential benefit …
A two‐stage approach for dynamic prediction of time‐to‐event distributions
Dynamic prediction uses longitudinal biomarkers for real‐time prediction of an individual
patient's prognosis. This is critical for patients with an incurable disease such as cancer …
patient's prognosis. This is critical for patients with an incurable disease such as cancer …
A change-point model for detecting heterogeneity in ordered survival responses
O Bouaziz, G Nuel - Statistical methods in medical research, 2018 - journals.sagepub.com
In this article, we suggest a new statistical approach considering survival heterogeneity as a
breakpoint model in an ordered sequence of time-to-event variables. The survival responses …
breakpoint model in an ordered sequence of time-to-event variables. The survival responses …
Estimation and variable selection in a joint model of survival times and longitudinal outcomes with random effects
This paper considers a joint survival and mixed-effects model to explain the survival time
from longitudinal data and high-dimensional covariates. The longitudinal data is modeled …
from longitudinal data and high-dimensional covariates. The longitudinal data is modeled …
Estimation of the distribution of longitudinal biomarker trajectories prior to disease progression
Most studies characterize longitudinal biomarker trajectories by looking forward at them from
a commonly used time origin, such as the initial treatment time. For a better understanding of …
a commonly used time origin, such as the initial treatment time. For a better understanding of …
[图书][B] Joint models for longitudinal and survival data
L Yang - 2013 - search.proquest.com
Epidemiologic and clinical studies routinely collect longitudinal measures of multiple
outcomes. These longitudinal outcomes can be used to establish the temporal order of …
outcomes. These longitudinal outcomes can be used to establish the temporal order of …