IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v115y2020i532p1946-1959.html
   My bibliography  Save this article

Optimal Designs of Two-Phase Studies

Author

Listed:
  • Ran Tao
  • Donglin Zeng
  • Dan-Yu Lin

Abstract

The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under such a design, the outcome and inexpensive covariates are measured on all subjects in the first phase and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase. Previous research on two-phase studies has focused largely on the inference procedures rather than the design aspects. We investigate the design efficiency of the two-phase study, as measured by the semiparametric efficiency bound for estimating the regression coefficients of expensive covariates. We consider general two-phase studies, where the outcome variable can be continuous, discrete, or censored, and the second-phase sampling can depend on the first-phase data in any manner. We develop optimal or approximately optimal two-phase designs, which can be substantially more efficient than the existing designs. We demonstrate the improvements of the new designs over the existing ones through extensive simulation studies and two large medical studies. Supplementary materials for this article are available online.

Suggested Citation

  • Ran Tao & Donglin Zeng & Dan-Yu Lin, 2020. "Optimal Designs of Two-Phase Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1946-1959, December.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:532:p:1946-1959
    DOI: 10.1080/01621459.2019.1671200
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2019.1671200
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2019.1671200?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brady Ryan & Ananthika Nirmalkanna & Candemir Cigsar & Yildiz E. Yilmaz, 2023. "Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 510-539, July.
    2. Zheng Xu & Song Yan & Shuai Yuan & Cong Wu & Sixia Chen & Zifang Guo & Yun Li, 2023. "Efficient Two-Stage Analysis for Complex Trait Association with Arbitrary Depth Sequencing Data," Stats, MDPI, vol. 6(1), pages 1-14, March.
    3. Keiji Takai & Kenichi Hayashi, 2023. "Model Selection with Missing Data Embedded in Missing-at-Random Data," Stats, MDPI, vol. 6(2), pages 1-11, April.

    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:taf:jnlasa:v:115:y:2020:i:532:p:1946-1959. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.