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 language | English (US) |
|---|---|
| Pages (from-to) | 1-23 |
| Number of pages | 23 |
| Journal | Annals of the Institute of Statistical Mathematics |
| DOIs | |
| State | Accepted/In press - Mar 10 2018 |
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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 journal › Article
}
TY - JOUR
T1 - Weighted estimating equations for additive hazards models with missing covariates
AU - Qi, Lihong
AU - Zhang, Xu
AU - Sun, Yanqing
AU - Wang, Lu
AU - Zhao, Yichuan
PY - 2018/3/10
Y1 - 2018/3/10
N2 - 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.
AB - 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.
KW - Kernel smoother
KW - Missing covariates
KW - Nonparametric method
KW - Weighted estimating equations
KW - Weighted estimators
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UR - http://www.scopus.com/inward/citedby.url?scp=85043363037&partnerID=8YFLogxK
U2 - 10.1007/s10463-018-0648-y
DO - 10.1007/s10463-018-0648-y
M3 - Article
AN - SCOPUS:85043363037
SP - 1
EP - 23
JO - Annals of the Institute of Statistical Mathematics
JF - Annals of the Institute of Statistical Mathematics
SN - 0020-3157
ER -