IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v30y2024i3d10.1007_s10985-024-09627-w.html
   My bibliography  Save this article

Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros

Author

Listed:
  • Anindya Bhadra

    (Purdue University)

  • Rubin Wei

    (Eli Lilly and Company)

  • Ruth Keogh

    (Department of Medical Statistics, London School of Hygiene and Tropical Medicine)

  • Victor Kipnis

    (National Cancer Institute)

  • Douglas Midthune

    (National Cancer Institute)

  • Dennis W. Buckman

    (Information Management Services, Inc.)

  • Ya Su

    (Virginia Commonwealth University)

  • Ananya Roy Chowdhury

    (Texas A&M University, College Station)

  • Raymond J. Carroll

    (University of Technology Sydney)

Abstract

We consider measurement error models for two variables observed repeatedly and subject to measurement error. One variable is continuous, while the other variable is a mixture of continuous and zero measurements. This second variable has two sources of zeros. The first source is episodic zeros, wherein some of the measurements for an individual may be zero and others positive. The second source is hard zeros, i.e., some individuals will always report zero. An example is the consumption of alcohol from alcoholic beverages: some individuals consume alcoholic beverages episodically, while others never consume alcoholic beverages. However, with a small number of repeat measurements from individuals, it is not possible to determine those who are episodic zeros and those who are hard zeros. We develop a new measurement error model for this problem, and use Bayesian methods to fit it. Simulations and data analyses are used to illustrate our methods. Extensions to parametric models and survival analysis are discussed briefly.

Suggested Citation

  • Anindya Bhadra & Rubin Wei & Ruth Keogh & Victor Kipnis & Douglas Midthune & Dennis W. Buckman & Ya Su & Ananya Roy Chowdhury & Raymond J. Carroll, 2024. "Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(3), pages 600-623, July.
    • Handle: RePEc:spr:lifeda:v:30:y:2024:i:3:d:10.1007_s10985-024-09627-w
    DOI: 10.1007/s10985-024-09627-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-024-09627-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10985-024-09627-w?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.

    References listed on IDEAS

    as
    1. Victor Kipnis & Douglas Midthune & Dennis W. Buckman & Kevin W. Dodd & Patricia M. Guenther & Susan M. Krebs-Smith & Amy F. Subar & Janet A. Tooze & Raymond J. Carroll & Laurence S. Freedman, 2009. "Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes," Biometrics, The International Biometric Society, vol. 65(4), pages 1003-1010, December.
    2. Grace Y. Yi & Wenqing He & Raymond. J. Carroll, 2022. "Feature screening with large‐scale and high‐dimensional survival data," Biometrics, The International Biometric Society, vol. 78(3), pages 894-907, September.
    3. Liang Li & Jun Shao & Mari Palta, 2005. "A Longitudinal Measurement Error Model with a Semicontinuous Covariate," Biometrics, The International Biometric Society, vol. 61(3), pages 824-830, September.
    4. Victor Kipnis & Laurence S. Freedman & Raymond J. Carroll & Douglas Midthune, 2016. "A bivariate measurement error model for semicontinuous and continuous variables: Application to nutritional epidemiology," Biometrics, The International Biometric Society, vol. 72(1), pages 106-115, March.
    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. Zhang Saijuan & Krebs-Smith Susan M. & Midthune Douglas & Perez Adriana & Buckman Dennis W. & Kipnis Victor & Freedman Laurence S. & Dodd Kevin W. & Carroll Raymond J, 2011. "Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-32, January.
    2. Nicholas Beyler & Susanne James-Burdumy & Martha Bleeker & Jane Fortson & Max Benjamin, "undated". "Measurement Error Properties in an Accelerometer Sample of U.S. Elementary School Children," Mathematica Policy Research Reports 6c99580fa94443459f3cbd005, Mathematica Policy Research.
    3. repec:plo:pone00:0111619 is not listed on IDEAS
    4. Daniel R. Kowal & Bohan Wu, 2023. "Semiparametric count data regression for self‐reported mental health," Biometrics, The International Biometric Society, vol. 79(2), pages 1520-1533, June.
    5. Harris-Fry, Helen & Saville, Naomi M. & Paudel, Puskar & Manandhar, Dharma S. & Cortina-Borja, Mario & Skordis, Jolene, 2022. "Relative power: Explaining the effects of food and cash transfers on allocative behaviour in rural Nepalese households," Journal of Development Economics, Elsevier, vol. 154(C).
    6. Victor Kipnis & Douglas Midthune & Dennis W. Buckman & Kevin W. Dodd & Patricia M. Guenther & Susan M. Krebs-Smith & Amy F. Subar & Janet A. Tooze & Raymond J. Carroll & Laurence S. Freedman, 2009. "Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes," Biometrics, The International Biometric Society, vol. 65(4), pages 1003-1010, December.
    7. repec:jss:jstsof:46:c03 is not listed on IDEAS
    8. Ching-Yun Wang & Jean de Dieu Tapsoba & Catherine Duggan & Anne McTiernan, 2024. "Generalized Linear Models with Covariate Measurement Error and Zero-Inflated Surrogates," Mathematics, MDPI, vol. 12(2), pages 1-14, January.
    9. repec:mpr:mprres:7903 is not listed on IDEAS
    10. Harris-Fry, Helen & Lamson, Lauren & Roett, Katelyn & Katz, Elizabeth, 2022. "Reducing gender bias in household consumption data: Implications for food fortification policy," Food Policy, Elsevier, vol. 110(C).
    11. Cornelis J. Potgieter & Rubin Wei & Victor Kipnis & Laurence S. Freedman & Raymond J. Carroll, 2016. "Moment reconstruction and moment‐adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process," Biometrics, The International Biometric Society, vol. 72(4), pages 1369-1377, December.

    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:spr:lifeda:v:30:y:2024:i:3:d:10.1007_s10985-024-09627-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.