IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v59y1997i1p55-95.html
   My bibliography  Save this article

Inference for Non‐random Samples

Author

Listed:
  • J. B. Copas
  • H. G. Li

Abstract

Observational data are often analysed as if they had resulted from a controlled study, and yet the tacit assumption of randomness can be crucial for the validity of inference. We take some simple statistical models and supplement them by adding a parameter θ which reflects the degree of non‐randomness in the sample. For a randomized study θ is known to be 0. We examine the profile log‐likelihood for θ and the sensitivity of inference to small non‐zero values of θ. Particular models cover the analysis of survey data with item non‐response, the paired comparison t‐test and two group comparisons using observational data with covariates. Some practical examples are discussed. Allowing for sampling bias increases the uncertainty of estimation and weakens the significance of treatment effects, sometimes substantially so.

Suggested Citation

  • J. B. Copas & H. G. Li, 1997. "Inference for Non‐random Samples," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 55-95.
    • Handle: RePEc:bla:jorssb:v:59:y:1997:i:1:p:55-95
    DOI: 10.1111/1467-9868.00055
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9868.00055
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9868.00055?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
    ---><---

    Citations

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


    Cited by:

    1. Irsova, Zuzana & Bom, Pedro R. D. & Havranek, Tomas & Rachinger, Heiko, 2023. "Spurious Precision in Meta-Analysis," EconStor Preprints 268683, ZBW - Leibniz Information Centre for Economics.
    2. Mengke Li & Yukun Liu & Pengfei Li & Jing Qin, 2022. "Empirical likelihood meta-analysis with publication bias correction under Copas-like selection model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 93-112, February.
    3. Byeong Yeob Choi & Jason P. Fine & Roman Fernandez & M. Alan Brookhart, 2022. "Alternative sensitivity analyses for regression estimates of treatment effects to unobserved confounding in binary and survival data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 637-659, September.
    4. van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2018. "P-uniform," MetaArXiv zqjr9, Center for Open Science.
    5. Hirschauer, Norbert & Grüner, Sven & Mußhoff, Oliver & Becker, Claudia & Jantsch, Antje, 2020. "Can p-values be meaningfully interpreted without random sampling?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14, pages 71-91.
    6. Richard Valliant & Jill A. Dever, 2011. "Estimating Propensity Adjustments for Volunteer Web Surveys," Sociological Methods & Research, , vol. 40(1), pages 105-137, February.
    7. Atanu B & Gajendra V & Jesna J & Ramesh V, 2017. "Multiple Imputations for Determining an Optimum Biological Dose of a Metronomic Chemotherapy," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 3(5), pages 129-140, October.
    8. van Aert, Robbie Cornelis Maria, 2018. "Dissertation R.C.M. van Aert," MetaArXiv eqhjd, Center for Open Science.
    9. Stratton Leslie S. & Datta Gupta Nabanita & Reimer David & Holm Anders, 2018. "Modeling Completion of Vocational Education: The Role of Cognitive and Noncognitive Skills by Program Type," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 18(4), pages 1-17, October.
    10. Soojin Park & Gregory J. Palardy, 2020. "Sensitivity Evaluation of Methods for Estimating Complier Average Causal Mediation Effects to Assumptions," Journal of Educational and Behavioral Statistics, , vol. 45(4), pages 475-506, August.
    11. Tenglong Li & Kenneth A. Frank, 2019. "On the probability of a causal inference is robust for internal validity," Papers 1906.08726, arXiv.org.
    12. Emmanuel O. Ogundimu, 2022. "Regularization and variable selection in Heckman selection model," Statistical Papers, Springer, vol. 63(2), pages 421-439, April.
    13. Mengke Li & Yan Fan & Yang Liu & Yukun Liu, 2021. "Diagnostic test meta-analysis by empirical likelihood under a Copas-like selection model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(6), pages 927-947, August.
    14. Tenglong Li & Kenneth A. Frank & Mingming Chen, 2024. "A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study," Mathematics, MDPI, vol. 12(3), pages 1-14, January.

    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:jorssb:v:59:y:1997:i:1:p:55-95. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.