IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v70y2014i1p202-211.html
   My bibliography  Save this article

Regression for skewed biomarker outcomes subject to pooling

Author

Listed:
  • Emily M. Mitchell
  • Robert H. Lyles
  • Amita K. Manatunga
  • Michelle Danaher
  • Neil J. Perkins
  • Enrique F. Schisterman

Abstract

No abstract is available for this item.

Suggested Citation

  • Emily M. Mitchell & Robert H. Lyles & Amita K. Manatunga & Michelle Danaher & Neil J. Perkins & Enrique F. Schisterman, 2014. "Regression for skewed biomarker outcomes subject to pooling," Biometrics, The International Biometric Society, vol. 70(1), pages 202-211, March.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:1:p:202-211
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12134
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Clarice R. Weinberg & David M. Umbach, 1999. "Using Pooled Exposure Assessment to Improve Efficiency in Case-Control Studies," Biometrics, The International Biometric Society, vol. 55(3), pages 718-726, September.
    2. S. Vansteelandt & E. Goetghebeur & T. Verstraeten, 2000. "Regression Models for Disease Prevalence with Diagnostic Tests on Pools of Serum Samples," Biometrics, The International Biometric Society, vol. 56(4), pages 1126-1133, December.
    3. Chang-Xing Ma & Albert Vexler & Enrique F. Schisterman & Lili Tian, 2011. "Cost-efficient designs based on linearly associated biomarkers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2739-2750, January.
    4. Zhiwei Zhang & Paul S. Albert, 2011. "Binary Regression Analysis with Pooled Exposure Measurements: A Regression Calibration Approach," Biometrics, The International Biometric Society, vol. 67(2), pages 636-645, June.
    5. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    6. Yaakov Malinovsky & Paul S. Albert & Enrique F. Schisterman, 2012. "Pooling Designs for Outcomes under a Gaussian Random Effects Model," Biometrics, The International Biometric Society, vol. 68(1), pages 45-52, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Robert H. Lyles & Dane Van Domelen & Emily M. Mitchell & Enrique F. Schisterman, 2015. "A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled Samples," IJERPH, MDPI, vol. 12(11), pages 1-18, November.
    2. Dewei Wang & Xichen Mou & Yan Liu, 2022. "Varying‐coefficient regression analysis for pooled biomonitoring," Biometrics, The International Biometric Society, vol. 78(4), pages 1328-1341, December.
    3. Karyn Heavner & Craig Newschaffer & Irva Hertz-Picciotto & Deborah Bennett & Igor Burstyn, 2015. "Pooling Bio-Specimens in the Presence of Measurement Error and Non-Linearity in Dose-Response: Simulation Study in the Context of a Birth Cohort Investigating Risk Factors for Autism Spectrum Disorder," IJERPH, MDPI, vol. 12(11), pages 1-20, November.
    4. Mou, Xichen & Wang, Dewei, 2024. "Additive partially linear model for pooled biomonitoring data," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    5. Wang, Dewei & McMahan, Christopher S. & Tebbs, Joshua M. & Bilder, Christopher R., 2018. "Group testing case identification with biomarker information," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 156-166.

    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. Yaakov Malinovsky & Paul S. Albert & Enrique F. Schisterman, 2012. "Pooling Designs for Outcomes under a Gaussian Random Effects Model," Biometrics, The International Biometric Society, vol. 68(1), pages 45-52, March.
    2. Robert H. Lyles & Dane Van Domelen & Emily M. Mitchell & Enrique F. Schisterman, 2015. "A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled Samples," IJERPH, MDPI, vol. 12(11), pages 1-18, November.
    3. Peng Chen & Joshua M. Tebbs & Christopher R. Bilder, 2009. "Group Testing Regression Models with Fixed and Random Effects," Biometrics, The International Biometric Society, vol. 65(4), pages 1270-1278, December.
    4. Dewei Wang & Xichen Mou & Yan Liu, 2022. "Varying‐coefficient regression analysis for pooled biomonitoring," Biometrics, The International Biometric Society, vol. 78(4), pages 1328-1341, December.
    5. Wang, Dewei & McMahan, Christopher S. & Tebbs, Joshua M. & Bilder, Christopher R., 2018. "Group testing case identification with biomarker information," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 156-166.
    6. Igor Burstyn & Jonathan W. Martin & Sanjay Beesoon & Fiona Bamforth & Qiaozhi Li & Yutaka Yasui & Nicola M. Cherry, 2013. "Maternal Exposure to Bisphenol-A and Fetal Growth Restriction: A Case-Referent Study," IJERPH, MDPI, vol. 10(12), pages 1-14, December.
    7. Xianzheng Huang & Joshua M. Tebbs, 2009. "On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response," Biometrics, The International Biometric Society, vol. 65(3), pages 710-718, September.
    8. Shu Yang & Jae Kwang Kim, 2016. "Likelihood-based Inference with Missing Data Under Missing-at-Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 436-454, June.
    9. Hemant Kulkarni & Jayabrata Biswas & Kiranmoy Das, 2019. "A joint quantile regression model for multiple longitudinal outcomes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 453-473, December.
    10. Tatiyana V. Apanasovich & David Ruppert & Joanne R. Lupton & Natasa Popovic & Nancy D. Turner & Robert S. Chapkin & Raymond J. Carroll, 2008. "Aberrant Crypt Foci and Semiparametric Modeling of Correlated Binary Data," Biometrics, The International Biometric Society, vol. 64(2), pages 490-500, June.
    11. Ricardo Smith Ramírez, 2007. "FIML estimation of treatment effect models with endogenous selection and multiple censored responses via a Monte Carlo EM Algorithm," Working Papers DTE 403, CIDE, División de Economía.
    12. Brent A. Coull & Alan Agresti, 2000. "Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit-Normal Distribution," Biometrics, The International Biometric Society, vol. 56(1), pages 73-80, March.
    13. J. E. Mills & C. A. Field & D. J. Dupuis, 2002. "Marginally Specified Generalized Linear Mixed Models: A Robust Approach," Biometrics, The International Biometric Society, vol. 58(4), pages 727-734, December.
    14. Jan Pablo Burgard & Patricia Dörr & Ralf Münnich, 2020. "Monte-Carlo Simulation Studies in Survey Statistics – An Appraisal," Research Papers in Economics 2020-04, University of Trier, Department of Economics.
    15. Kauermann, Goran & Xu, Ronghui & Vaida, Florin, 2008. "Stacked Laplace-EM algorithm for duration models with time-varying and random effects," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2514-2528, January.
    16. Steffen Nestler & Sarah Humberg, 2022. "A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 506-532, June.
    17. Koutchade, Philippe & Carpentier, Alain & Féménia, Fabienne, 2015. "Empirical modeling of production decisions of heterogeneous farmers with random parameter models," Working Papers 210097, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    18. Mou, Xichen & Wang, Dewei, 2024. "Additive partially linear model for pooled biomonitoring data," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    19. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    20. Gonzalez, Jorge & Tuerlinckx, Francis & De Boeck, Paul & Cools, Ronald, 2006. "Numerical integration in logistic-normal models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1535-1548, December.

    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:70:y:2014:i:1:p:202-211. 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.