IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v25y2005i2p291-299.html
   My bibliography  Save this article

Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses

Author

Listed:
  • A. John Bailer
  • Robert B. Noble
  • Matthew W. Wheeler

Abstract

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure‐response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.

Suggested Citation

  • A. John Bailer & Robert B. Noble & Matthew W. Wheeler, 2005. "Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 291-299, April.
    • Handle: RePEc:wly:riskan:v:25:y:2005:i:2:p:291-299
    DOI: 10.1111/j.1539-6924.2005.00590.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6924.2005.00590.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1539-6924.2005.00590.x?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. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    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. Walter W. Piegorsch & Hui Xiong & Rabi N. Bhattacharya & Lizhen Lin, 2014. "Benchmark Dose Analysis via Nonparametric Regression Modeling," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 135-151, January.
    2. Enrique López Droguett & Ali Mosleh, 2008. "Bayesian Methodology for Model Uncertainty Using Model Performance Data," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1457-1476, October.
    3. Chu‐Chih Chen & James J. Chen, 2014. "Benchmark Dose Calculation for Ordered Categorical Responses," Risk Analysis, John Wiley & Sons, vol. 34(8), pages 1435-1447, August.
    4. Kan Shao & Jeffrey S. Gift, 2014. "Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 101-120, January.
    5. Edsel A. Peña & Wensong Wu & Walter Piegorsch & Ronald W. West & LingLing An, 2017. "Model Selection and Estimation with Quantal‐Response Data in Benchmark Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(4), pages 716-732, April.
    6. Jin‐Hua Chen & Chun‐Shu Chen & Meng‐Fan Huang & Hung‐Chih Lin, 2016. "Estimating the Probability of Rare Events Occurring Using a Local Model Averaging," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1855-1870, October.
    7. Matthew W. Wheeler & A. John Bailer, 2007. "Properties of Model‐Averaged BMDLs: A Study of Model Averaging in Dichotomous Response Risk Estimation," Risk Analysis, John Wiley & Sons, vol. 27(3), pages 659-670, June.
    8. Enrique López Droguett & Ali Mosleh, 2013. "Integrated treatment of model and parameter uncertainties through a Bayesian approach," Journal of Risk and Reliability, , vol. 227(1), pages 41-54, February.
    9. Robert B. Noble & A. John Bailer & Robert Park, 2009. "Model‐Averaged Benchmark Concentration Estimates for Continuous Response Data Arising from Epidemiological Studies," Risk Analysis, John Wiley & Sons, vol. 29(4), pages 558-564, April.
    10. Steven B. Kim & Scott M. Bartell & Daniel L. Gillen, 2015. "Estimation of a Benchmark Dose in the Presence or Absence of Hormesis Using Posterior Averaging," Risk Analysis, John Wiley & Sons, vol. 35(3), pages 396-408, March.
    11. Harriet Namata & Marc Aerts & Christel Faes & Peter Teunis, 2008. "Model Averaging in Microbial Risk Assessment Using Fractional Polynomials," Risk Analysis, John Wiley & Sons, vol. 28(4), pages 891-905, August.
    12. Hojin Moon & Hyun‐Joo Kim & James J. Chen & Ralph L. Kodell, 2005. "Model Averaging Using the Kullback Information Criterion in Estimating Effective Doses for Microbial Infection and Illness," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1147-1159, October.
    13. Walter W. Piegorsch, 2010. "Translational benchmark risk analysis," Journal of Risk Research, Taylor & Francis Journals, vol. 13(5), pages 653-667, July.
    14. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.
    15. Matthew W. Wheeler & Todd Blessinger & Kan Shao & Bruce C. Allen & Louis Olszyk & J. Allen Davis & Jeffrey S Gift, 2020. "Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1706-1722, September.
    16. Matteo Spada & Peter Burgherr, 2020. "Comparative Risk Assessment for Fossil Energy Chains Using Bayesian Model Averaging," Energies, MDPI, vol. 13(2), pages 1-21, January.
    17. Enrique López Droguett & Ali Mosleh, 2014. "Bayesian Treatment of Model Uncertainty for Partially Applicable Models," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 252-270, February.
    18. Signe M. Jensen & Christian Ritz, 2015. "Simultaneous Inference for Model Averaging of Derived Parameters," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 68-76, January.
    19. Hojin Moon & Steven B. Kim & James J. Chen & Nysia I. George & Ralph L. Kodell, 2013. "Model Uncertainty and Model Averaging in the Estimation of Infectious Doses for Microbial Pathogens," Risk Analysis, John Wiley & Sons, vol. 33(2), pages 220-231, February.
    20. Walter W. Piegorsch & Susan L. Cutter & Frank Hardisty, 2007. "Benchmark Analysis for Quantifying Urban Vulnerability to Terrorist Incidents," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1411-1425, December.

    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. Claudia García-García & Catalina B. García-García & Román Salmerón, 2021. "Confronting collinearity in environmental regression models: evidence from world data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 895-926, September.
    2. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    3. Sai Ding & John Knight, 2011. "Why has China Grown So Fast? The Role of Physical and Human Capital Formation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 141-174, April.
    4. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    5. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    6. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    7. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    8. Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
    9. Coleman, Stephen, 2005. "Testing Theories with Qualitative and Quantitative Predictions," MPRA Paper 105171, University Library of Munich, Germany.
    10. Ewout W. Steyerberg, 2005. "Local Applicability of Clinical and Model-Based Probability Estimates," Medical Decision Making, , vol. 25(6), pages 678-680, November.
    11. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    12. Brooks, Jeremy S., 2010. "The Buddha mushroom: Conservation behavior and the development of institutions in Bhutan," Ecological Economics, Elsevier, vol. 69(4), pages 779-795, February.
    13. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
    14. Brian Knaeble & Seth Dutter, 2017. "Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 97-105, April.
    15. John Knight & Sai Ding, 2008. "Why has China Grown so Fast? The Role of Structural Change," Economics Series Working Papers 415, University of Oxford, Department of Economics.
    16. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2021. "Stochastic coherency in forecast reconciliation," International Journal of Production Economics, Elsevier, vol. 240(C).
    17. Steven M. Shugan, 2002. "In Search of Data: An Editorial," Marketing Science, INFORMS, vol. 21(4), pages 369-377.
    18. Fletcher, David & Dillingham, Peter W., 2011. "Model-averaged confidence intervals for factorial experiments," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3041-3048, November.
    19. Liu, Min & He, Honglin & Ren, Xiaoli & Sun, Xiaomin & Yu, Guirui & Han, Shijie & Wang, Huimin & Zhou, Guoyi, 2015. "The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems," Ecological Modelling, Elsevier, vol. 303(C), pages 30-41.
    20. Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.

    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:25:y:2005:i:2:p:291-299. 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.