Weighted estimating equations for additive hazards models with missing covariates

Lihong Qi, Xu Zhang, Yanqing Sun, Lu Wang, Yichuan Zhao

Research output: Contribution to journalArticle

Abstract

This paper presents simple weighted and fully augmented weighted estimators for the additive hazards model with missing covariates when they are missing at random. The additive hazards model estimates the difference in hazards and has an intuitive biological interpretation. The proposed weighted estimators for the additive hazards model use incomplete data nonparametrically and have close-form expressions. We show that they are consistent and asymptotically normal, and are more efficient than the simple weighted estimator which only uses the complete data. We illustrate their finite-sample performance through simulation studies and an application to study the progression from mild cognitive impairment to dementia using data from the Alzheimer’s Disease Neuroimaging Initiative as well as an application to the mouse leukemia study.

Original languageEnglish (US)
Pages (from-to)1-23
Number of pages23
JournalAnnals of the Institute of Statistical Mathematics
DOIs
StateAccepted/In press - Mar 10 2018

Fingerprint

Weighted Estimating Equations
Additive Hazards Model
Missing Covariates
Estimator
Neuroimaging
Dementia
Missing at Random
Alzheimer's Disease
Incomplete Data
Leukemia
Progression
Hazard
Mouse
Intuitive
Simulation Study
Estimate

Keywords

  • Kernel smoother
  • Missing covariates
  • Nonparametric method
  • Weighted estimating equations
  • Weighted estimators

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Weighted estimating equations for additive hazards models with missing covariates. / Qi, Lihong; Zhang, Xu; Sun, Yanqing; Wang, Lu; Zhao, Yichuan.

In: Annals of the Institute of Statistical Mathematics, 10.03.2018, p. 1-23.

Research output: Contribution to journalArticle

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