Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model

This paper provides a new insight into an economical and effective sampling design method relying on the outcome-dependent sampling (ODS) design in large-scale cohort research. Firstly, the importance and originality of this paper is that it explores how to fit the covariate-adjusted additive Hazard...

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Main Authors: Yingli Pan, Songlin Liu, Yanli Zhou, Guangyu Song
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/2790123
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author Yingli Pan
Songlin Liu
Yanli Zhou
Guangyu Song
author_facet Yingli Pan
Songlin Liu
Yanli Zhou
Guangyu Song
author_sort Yingli Pan
collection DOAJ
description This paper provides a new insight into an economical and effective sampling design method relying on the outcome-dependent sampling (ODS) design in large-scale cohort research. Firstly, the importance and originality of this paper is that it explores how to fit the covariate-adjusted additive Hazard model under the ODS design; secondly, this paper focused on estimating the distortion function through nonparametric regression and required observation of the covariate on the confounding factors of distortion; moreover, this paper further calibrated the contaminated covariates and proposed the estimators of the parameters by analyzing the calibrated covariates; finally, this paper established the large sample property and asymptotic normality of the proposed estimators and conducted many more simulations to evaluate the finite sample performance of the proposed method. Empirical research demonstrates that the results from both artificial and real data verified good performance and practicality of the proposed ODS method in this paper.
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institution Kabale University
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language English
publishDate 2020-01-01
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record_format Article
series Complexity
spelling doaj-art-63648c7cc1014f8ebfe54196c3ed3c9c2025-02-03T01:27:57ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/27901232790123Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards ModelYingli Pan0Songlin Liu1Yanli Zhou2Guangyu Song3Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, ChinaHubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, ChinaSchool of Finance, Zhongnan University of Economics and Law, Wuhan 430073, ChinaHubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, ChinaThis paper provides a new insight into an economical and effective sampling design method relying on the outcome-dependent sampling (ODS) design in large-scale cohort research. Firstly, the importance and originality of this paper is that it explores how to fit the covariate-adjusted additive Hazard model under the ODS design; secondly, this paper focused on estimating the distortion function through nonparametric regression and required observation of the covariate on the confounding factors of distortion; moreover, this paper further calibrated the contaminated covariates and proposed the estimators of the parameters by analyzing the calibrated covariates; finally, this paper established the large sample property and asymptotic normality of the proposed estimators and conducted many more simulations to evaluate the finite sample performance of the proposed method. Empirical research demonstrates that the results from both artificial and real data verified good performance and practicality of the proposed ODS method in this paper.http://dx.doi.org/10.1155/2020/2790123
spellingShingle Yingli Pan
Songlin Liu
Yanli Zhou
Guangyu Song
Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model
Complexity
title Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model
title_full Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model
title_fullStr Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model
title_full_unstemmed Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model
title_short Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model
title_sort regression analysis for outcome dependent sampling design under the covariate adjusted additive hazards model
url http://dx.doi.org/10.1155/2020/2790123
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AT guangyusong regressionanalysisforoutcomedependentsamplingdesignunderthecovariateadjustedadditivehazardsmodel