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 …

The Cox proportional hazards model is commonly used in evaluating risk factors in cancer
survival data. The model assumes an additive, linear relationship between the risk factors …

Abstract Background Convolutional neural networks (CNNs) have emerged as a novel
method for evaluating heart failure (HF) in adult electrocardiograms (ECGs). However, such …

In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left
truncation leading to selection bias. For estimation of the distribution of the time to event …

Abstract Background Anti-obesity medications (AOMs) can be initiated in conjunction with
participation in the VA national behavioral weight management program, MOVE!, to help …

Survival or time-to-event data with time-varying covariates are common in practice, and
exploring the non-stationarity in covariates is essential to accurately analyzing the nonlinear …

Time‐varying covariates are often available in survival studies, and estimation of the hazard
function needs to be updated as new information becomes available. In this article, we …

To draw real-world evidence about the comparative effectiveness of multiple time-varying
treatments on patient survival, we develop a joint marginal structural survival model and a …

In this work, we introduce JointLIME, a novel interpretation method for explaining black‐box
survival (BBS) models with endogenous time‐varying covariates (TVCs). Existing …

Mobile health apps are revolutionizing the healthcare ecosystem by improving
communication, efficiency, and quality of service. In low-and middle-income countries, they …