IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i1p140-150.html
   My bibliography  Save this article

Noniterative adjustment to regression estimators with population‐based auxiliary information for semiparametric models

Author

Listed:
  • Fei Gao
  • K. C. G. Chan

Abstract

Disease registries, surveillance data, and other datasets with extremely large sample sizes become increasingly available in providing population‐based information on disease incidence, survival probability, or other important public health characteristics. Such information can be leveraged in studies that collect detailed measurements but with smaller sample sizes. In contrast to recent proposals that formulate additional information as constraints in optimization problems, we develop a general framework to construct simple estimators that update the usual regression estimators with some functionals of data that incorporate the additional information. We consider general settings that incorporate nuisance parameters in the auxiliary information, non‐i.i.d. data such as those from case‐control studies, and semiparametric models with infinite‐dimensional parameters common in survival analysis. Details of several important data and sampling settings are provided with numerical examples.

Suggested Citation

  • Fei Gao & K. C. G. Chan, 2023. "Noniterative adjustment to regression estimators with population‐based auxiliary information for semiparametric models," Biometrics, The International Biometric Society, vol. 79(1), pages 140-150, March.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:140-150
    DOI: 10.1111/biom.13585
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13585
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13585?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ying Sheng & Yifei Sun & Detian Deng & Chiung‐Yu Huang, 2020. "Censored linear regression in the presence or absence of auxiliary survival information," Biometrics, The International Biometric Society, vol. 76(3), pages 734-745, September.
    2. Chiung-Yu Huang & Jing Qin & Huei-Ting Tsai, 2016. "Efficient Estimation of the Cox Model with Auxiliary Subgroup Survival Information," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 787-799, April.
    3. Guido W. Imbens & Tony Lancaster, 1994. "Combining Micro and Macro Data in Microeconometric Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 655-680.
    4. Nilanjan Chatterjee & Yi-Hau Chen & Paige Maas & Raymond J. Carroll, 2016. "Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 107-117, March.
    5. Peisong Han & Lu Wang, 2013. "Estimation with missing data: beyond double robustness," Biometrika, Biometrika Trust, vol. 100(2), pages 417-430.
    6. Jing Qin & Han Zhang & Pengfei Li & Demetrius Albanes & Kai Yu, 2015. "Using covariate-specific disease prevalence information to increase the power of case-control studies," Biometrika, Biometrika Trust, vol. 102(1), pages 169-180.
    7. Han Zhang & Lu Deng & Mark Schiffman & Jing Qin & Kai Yu, 2020. "Generalized integration model for improved statistical inference by leveraging external summary data," Biometrika, Biometrika Trust, vol. 107(3), pages 689-703.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Han Zhang & Lu Deng & William Wheeler & Jing Qin & Kai Yu, 2022. "Integrative analysis of multiple case‐control studies," Biometrics, The International Biometric Society, vol. 78(3), pages 1080-1091, September.
    2. Ziqi Chen & Jing Ning & Yu Shen & Jing Qin, 2021. "Combining primary cohort data with external aggregate information without assuming comparability," Biometrics, The International Biometric Society, vol. 77(3), pages 1024-1036, September.
    3. Ruoyu Wang & Qihua Wang & Wang Miao, 2023. "A robust fusion-extraction procedure with summary statistics in the presence of biased sources," Biometrika, Biometrika Trust, vol. 110(4), pages 1023-1040.
    4. Yu‐Jen Cheng & Yen‐Chun Liu & Chang‐Yu Tsai & Chiung‐Yu Huang, 2023. "Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity," Biometrics, The International Biometric Society, vol. 79(3), pages 1996-2009, September.
    5. Ying Sheng & Yifei Sun & Chiung‐Yu Huang & Mi‐Ok Kim, 2022. "Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach," Biometrics, The International Biometric Society, vol. 78(2), pages 679-690, June.
    6. Jie He & Hui Li & Shumei Zhang & Xiaogang Duan, 2019. "Additive hazards model with auxiliary subgroup survival information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 128-149, January.
    7. Bo Han & Ingrid Van Keilegom & Xiaoguang Wang, 2022. "Semiparametric estimation of the nonmixture cure model with auxiliary survival information," Biometrics, The International Biometric Society, vol. 78(2), pages 448-459, June.
    8. Tian Gu & Jeremy Michael George Taylor & Bhramar Mukherjee, 2023. "A synthetic data integration framework to leverage external summary‐level information from heterogeneous populations," Biometrics, The International Biometric Society, vol. 79(4), pages 3831-3845, December.
    9. Cao, Yongxiu & Yu, Jichang, 2023. "Adjusting for unmeasured confounding in survival causal effect using validation data," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    10. Ying Sheng & Yifei Sun & Detian Deng & Chiung‐Yu Huang, 2020. "Censored linear regression in the presence or absence of auxiliary survival information," Biometrics, The International Biometric Society, vol. 76(3), pages 734-745, September.
    11. Chixiang Chen & Ming Wang & Shuo Chen, 2023. "An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 2947-2960, December.
    12. Prosenjit Kundu & Nilanjan Chatterjee, 2023. "Logistic regression analysis of two‐phase studies using generalized method of moments," Biometrics, The International Biometric Society, vol. 79(1), pages 241-252, March.
    13. Ryosuke Igari & Takahiro Hoshino, 2018. "A Bayesian Gamma Frailty Model Using the Sum of Independent Random Variables: Application of the Estimation of an Interpurchase Timing Model," Keio-IES Discussion Paper Series 2018-021, Institute for Economics Studies, Keio University.
    14. Chen, Sixia & Haziza, David, 2023. "A unified framework of multiply robust estimation approaches for handling incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    15. Debashis Ghosh & Michael S. Sabel, 2022. "A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 363-379, December.
    16. Xiong, Wei & Wang, Dehui & Deng, Dianliang & Wang, Xinyang & Zhang, Wanying, 2022. "Penalized multiply robust estimation in high-order autoregressive processes with missing explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    17. Leora Friedberg & Steven Stern, 2014. "Marriage, Divorce, And Asymmetric Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(4), pages 1155-1199, November.
    18. Peisong Han & Linglong Kong & Jiwei Zhao & Xingcai Zhou, 2019. "A general framework for quantile estimation with incomplete data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 305-333, April.
    19. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    20. Liu, Tianqing & Yuan, Xiaohui, 2012. "Combining quasi and empirical likelihoods in generalized linear models with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 39-58.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:140-150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.