Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes

Tang, Yanlin; Song, Xinyuan; Yi, Grace Y.

doi:10.1007/s10985-021-09543-3

Cited by 2 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Third, we can consider other survival models such as time-varying proportional hazards, 21,[40][41][42] mean residual life, 43,44 and accelerated failure time models. 45,46 The inclusion of imaging predictors in various models can provide deep insight into the associations between images and survival outcomes of interest. Fourth, we assumed that the images and the associated time-varying coefficients could be expanded on the same eigenbases.…”

Section: Discussionmentioning

confidence: 99%

Additive hazards model with time-varying coefficients and imaging predictors

Yang

Wang

et al. 2022

Stat Methods Med Res

Self Cite

View full text Add to dashboard Cite

Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer’s disease neuroimaging initiative study further illustrates the utility of the methodology.

show abstract