English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Semiparametric accelerated failure time (AFT) models directly relate thepredicted failure times to covariates and are a useful alternative to modelsthat work on the hazard function or the survival function. For case-cohortdata, much less development has been done with AFT models. In addition to themissing covariates outside of the sub-cohort in controls, challenges from AFTmodel inferences with full cohort are retained. The regression parameterestimator is hard to compute because the most widely used rank-based estimatingequations are not smooth. Further, its variance depends on the unspecifiederror distribution, and most methods rely on computationally intensivebootstrap to estimate it. We propose fast rank-based inference procedures forAFT models, applying recent methodological advances to the context ofcase-cohort data. Parameters are estimated with an induced smoothing approachthat smooths the estimating functions and facilitates the numerical solution.Variance estimators are obtained through efficient resampling methods fornonsmooth estimating functions that avoids full blown bootstrap. Simulationstudies suggest that the recommended procedure provides fast and validinferences among several competing procedures. Application to a tumor studydemonstrates the utility of the proposed method in routine data analysis.

Fast Accelerated Failure Time Modeling for Case-Cohort Data