Maximum partial-rank correlation estimation for left-truncated and right-censored survival data

Shao Hsuan Wang, Chin Tsang Chiang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This article presents a general single-index hazard regression model to assess the effects of covariates on a failure time. Based on left-truncated and right-censored survival data, a new partial-rank correlation function is proposed to estimate the index coefficients in the presence of covariate-dependent truncation and censoring. Furthermore, an efficient computational algorithm is proposed for the computation that maximizes the constructed target function. The developed approach can be extended to include right-truncation and left-censoring under a reverse-time hazard regression model. Based on the maximum rank correlation estimator in the literature, we establish the consistency and asymptotic normality of the maximum partial-rank correlation estimator. A series of simulations shows that the proposed estimator has satisfactory finite-sample performance compared with that of its competitors. Lastly, we demonstrate our methodology by applying it to data from the US Health and Retirement Study.

Original languageEnglish
Pages (from-to)2141-2161
Number of pages21
JournalStatistica Sinica
Volume29
Issue number4
DOIs
StatePublished - 2020

Keywords

  • Asymptotic normality
  • Consistency
  • Left-censoring
  • Left-truncation. partial-rank correlation estimation
  • Random weighted bootstrap
  • Rank correlation estimation
  • Right-censoring
  • Right-truncation
  • U-statistic

Fingerprint

Dive into the research topics of 'Maximum partial-rank correlation estimation for left-truncated and right-censored survival data'. Together they form a unique fingerprint.

Cite this