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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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2790123 |
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| _version_ | 1832560314735919104 |
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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. |
| format | Article |
| id | doaj-art-63648c7cc1014f8ebfe54196c3ed3c9c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| 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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