IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v36y2016i1p74-82.html
   My bibliography  Save this article

Methods to Explore Uncertainty and Bias Introduced by Job Exposure Matrices

Author

Listed:
  • Sander Greenland
  • Heidi J. Fischer
  • Leeka Kheifets

Abstract

Job exposure matrices (JEMs) are used to measure exposures based on information about particular jobs and tasks. JEMs are especially useful when individual exposure data cannot be obtained. Nonetheless, there may be other workplace exposures associated with the study disease that are not measured in available JEMs. When these exposures are also associated with the exposures measured in the JEM, biases due to uncontrolled confounding will be introduced. Furthermore, individual exposures differ from JEM measurements due to differences in job conditions and worker practices. Uncertainty may also be present at the assessor level since exposure information for each job may be imprecise or incomplete. Assigning individuals a fixed exposure determined by the JEM ignores these uncertainty sources. We examine the uncertainty displayed by bias analyses in a study of occupational electric shocks, occupational magnetic fields, and amyotrophic lateral sclerosis.

Suggested Citation

  • Sander Greenland & Heidi J. Fischer & Leeka Kheifets, 2016. "Methods to Explore Uncertainty and Bias Introduced by Job Exposure Matrices," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 74-82, January.
    • Handle: RePEc:wly:riskan:v:36:y:2016:i:1:p:74-82
    DOI: 10.1111/risa.12438
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.12438
    Download Restriction: no
    File URL: https://libkey.io/10.1111/risa.12438?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. Nicola Orsini & Rino Bellocco & Matteo Bottai & Alicja Wolk & Sander Greenland, 2008. "A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies," Stata Journal, StataCorp LP, vol. 8(1), pages 29-48, February.
    2. Raydonal Ospina & Silvia Ferrari, 2010. "Inflated beta distributions," Statistical Papers, Springer, vol. 51(1), pages 111-126, January.
    3. Sander Greenland, 2000. "When Should Epidemiologic Regressions Use Random Coefficients?," Biometrics, The International Biometric Society, vol. 56(3), pages 915-921, September.
    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. Yeojin Chung & Sophia Rabe-Hesketh & Vincent Dorie & Andrew Gelman & Jingchen Liu, 2013. "A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 685-709, October.
    2. Dries P.J. Kuijper & Jakub W. Bubnicki & Marcin Churski & Bjorn Mols & Pim van Hooft, 2015. "Context dependence of risk effects: wolves and tree logs create patches of fear in an old-growth forest," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(6), pages 1558-1568.
    3. Guillermo Martínez-Flórez & Artur J. Lemonte & Germán Moreno-Arenas & Roger Tovar-Falón, 2022. "The Bivariate Unit-Sinh-Normal Distribution and Its Related Regression Model," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
    4. Lucio Masserini & Matilde Bini & Monica Pratesi, 2017. "Effectiveness of non-selective evaluation test scores for predicting first-year performance in university career: a zero-inflated beta regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 693-708, March.
    5. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.
    6. Ricardo Ocaña-Riola & Carmen Pérez-Romero & Mª Isabel Ortega-Díaz & José Jesús Martín-Martín, 2021. "Multilevel Zero-One Inflated Beta Regression Model for the Analysis of the Relationship between Exogenous Health Variables and Technical Efficiency in the Spanish National Health System Hospitals," IJERPH, MDPI, vol. 18(19), pages 1-18, September.
    7. Silvia Noirjean & Mario Biggeri & Laura Forastiere & Fabrizia Mealli & Maria Nannini, 2023. "Estimating causal effects of community health financing via principal stratification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1317-1350, October.
    8. Maria Gheorghe & Susan Picavet & Monique Verschuren & Werner B. F. Brouwer & Pieter H. M. Baal, 2017. "Health losses at the end of life: a Bayesian mixed beta regression approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 723-749, June.
    9. Ehsan Bahrami Samani & Elham Tabrizi, 2023. "Joint Linear Modeling of Mixed Data and Its Application to Email Analysis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 175-209, May.
    10. Murilo Wohlgemuth & Carlos Ernani Fries & Ângelo Márcio Oliveira Sant’Anna & Ricardo Giglio & Diego Castro Fettermann, 2020. "Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression," Annals of Operations Research, Springer, vol. 286(1), pages 703-717, March.
    11. Fabian Dunker & Konstantin Eckle & Katharina Proksch & Johannes Schmidt-Hieber, 2017. "Tests for qualitative features in the random coefficients model," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 225, Courant Research Centre PEG.
    12. Gauss Cordeiro & Denise Botter & Alexsandro Cavalcanti & Lúcia Barroso, 2014. "Covariance matrix of the bias-corrected maximum likelihood estimator in generalized linear models," Statistical Papers, Springer, vol. 55(3), pages 643-652, August.
    13. Yury R. Benites & Vicente G. Cancho & Edwin M. M. Ortega & Roberto Vila & Gauss M. Cordeiro, 2022. "A New Regression Model on the Unit Interval: Properties, Estimation, and Application," Mathematics, MDPI, vol. 10(17), pages 1-17, September.
    14. Tanoue, Yuta & Kawada, Akihiro & Yamashita, Satoshi, 2017. "Forecasting loss given default of bank loans with multi-stage model," International Journal of Forecasting, Elsevier, vol. 33(2), pages 513-522.
    15. Patricia Frenz & Jay S. Kaufman & Carolina Nazzal & Gabriel Cavada & Francisco Cerecera & Nicolás Silva, 2017. "Mediation of the effect of childhood socioeconomic position by educational attainment on adult chronic disease in Chile," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(9), pages 1007-1017, December.
    16. Kathryn M. Irvine & T. J. Rodhouse & Ilai N. Keren, 2016. "Extending Ordinal Regression with a Latent Zero-Augmented Beta Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 619-640, December.
    17. Guillermo Martínez-Flórez & Roger Tovar-Falón & Carlos Barrera-Causil, 2022. "Inflated Unit-Birnbaum-Saunders Distribution," Mathematics, MDPI, vol. 10(4), pages 1-14, February.
    18. Hildete P. Pinheiro & Rafael P. Maia & Eufrásio A. Lima Neto & Mariana Rodrigues-Motta, 2019. "Zero-one augmented beta and zero-inflated discrete models with heterogeneous dispersion for the analysis of student academic performance," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 749-767, December.
    19. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    20. Maria Gheorghe & Werner Brouwer & Pieter Baal, 2015. "Did the health of the Dutch population improve between 2001 and 2008? Investigating age- and gender-specific trends in quality of life," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(8), pages 801-811, November.

    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:wly:riskan:v:36:y:2016:i:1:p:74-82. 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: https://doi.org/10.1111/(ISSN)1539-6924 .

    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.