Cox models with time‐varying covariates and partly‐interval censoring–A maximum penalised likelihood approach
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DOI: 10.1002/sim.9645
Abstract: Time‐varying covariates can be important predictors when model based predictions are considered. A Cox model that includes time‐varying covariates is usually referred to as an extended Cox model. When only right censoring is presented in… read more here.
Keywords: varying covariates; cox; cox models; method ... See more keywords
Estimating time-varying treatment effects in longitudinal studies.
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DOI: 10.1037/met0000574.supp
Abstract: Longitudinal study designs are frequently used to investigate the effects of a naturally observed predictor (treatment) on an outcome over time. Because the treatment at each time point or wave is not randomly assigned, valid… read more here.
Keywords: time; varying covariates; treatment outcome; estimation ... See more keywords
On maximum likelihood estimation of the semi-parametric Cox model with time-varying covariates
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DOI: 10.1080/02664763.2019.1681946
Abstract: Including time-varying covariates is a popular extension to the Cox model and a suitable approach for dealing with non-proportional hazards. However, partial likelihood (PL) estimation of this model has three shortcomings: (i) estimated regression coefficients… read more here.
Keywords: baseline hazard; time varying; cox model; time ... See more keywords
Bayesian imputation of time-varying covariates in linear mixed models
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DOI: 10.1177/0962280217730851
Abstract: Studies involving large observational datasets commonly face the challenge of dealing with multiple missing values. The most popular approach to overcome this challenge, multiple imputation using chained equations, however, has been shown to be sub-optimal… read more here.
Keywords: bayesian imputation; imputation; time varying; models bayesian ... See more keywords
Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning
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DOI: 10.3390/diagnostics12122947
Abstract: Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We… read more here.
Keywords: varying covariates; mortality; risk; deep learning ... See more keywords