Model averaging in microbial risk assessment using fractional polynomials

Risk Analysis

Volumn 28, Issue 4, 2008, Pages 891-905

  Namata, Harriet ^a   Aerts, Marc ^a   Faes, Christel ^a   Teunis, Peter ^b  

^a HASSELT UNIVERSITY   (Belgium)

^b NATIONAL INSTITUTE FOR PUBLIC HEALTH AND THE ENVIRONMENT   (Netherlands)

Author keywords

Dose response models; Low dose extrapolation; Model selection; Model uncertainity; Risk analysis

Indexed keywords

CANNING; ERROR ANALYSIS; ESTIMATION; FINANCIAL DATA PROCESSING; FOOD PRESERVATION; HEALTH RISKS; MODAL ANALYSIS; MODEL STRUCTURES; PARAMETER ESTIMATION; POLYNOMIAL APPROXIMATION; POLYNOMIALS; RISK MANAGEMENT; RISK PERCEPTION; RISKS; SAFETY ENGINEERING; STANDARDS;

AKAIKE INFORMATION CRITERION; BIOLOGICAL PLAUSIBILITY; CAMPYLOBACTER JEJUNI; CANDIDATE MODELS; COMPETITIVE MODELS; CONFIDENCE INTERVALS; COVERAGE PROBABILITIES; DATA SETS; DOSE-RESPONSE MODELING; DOSE-RESPONSE MODELS; FITTING MODELS; FOOD-BORNE DISEASES; FRACTIONAL POLYNOMIALS; GENERAL PUBLIC; GOODNESS OF FITS; INFECTION RISK; LOW-DOSE; LOW-DOSE EXTRAPOLATION; MICROBIAL PATHOGENS; MICROBIAL RISK ASSESSMENT; MODEL AVERAGING; MODEL SELECTION; MODEL UNCERTAINITY; RISK ANALYSIS; RISK ESTIMATION; SALMONELLA TYPHI; SIMULATION STUDIES; STANDARD ERRORS;

RISK ASSESSMENT;

DOSE-RESPONSE RELATIONSHIP; FOOD SAFETY; MICROBIAL ACTIVITY; MODELING; PROBABILITY; RISK ASSESSMENT; UNCERTAINTY ANALYSIS;

ARTICLE; CAMPYLOBACTER JEJUNI; CONFIDENCE INTERVAL; MATHEMATICAL MODEL; MICROORGANISM; NONHUMAN; RISK ASSESSMENT; SALMONELLA TYPHI;

CAMPYLOBACTER INFECTIONS; CAMPYLOBACTER JEJUNI; FOOD MICROBIOLOGY; HUMANS; MODELS, THEORETICAL; RISK ASSESSMENT; SALMONELLA INFECTIONS; SALMONELLA TYPHI;

CAMPYLOBACTER JEJUNI; SALMONELLA TYPHI;

EID: 48349095576     PISSN: 02724332     EISSN: 15396924     Source Type: Journal    
DOI: 10.1111/j.1539-6924.2008.01063.x     Document Type: Article

References (28)

1. Agresti, A. (2002). Categorical Data Analysis, 2nd ed. New York : Wiley.
2. Bailer, A. J., Noble, R. B., & Wheeler, M. W. (2005). Model uncertainty and risk estimation for experimental studies of quantal responses. Risk Analysis, 25, 292 299.
3. Black, R. E., Levine, M. M., Clements, M. L., Hughes, T. P., & Blaser, M. J. (1988). Experimental Campylobacter jejuni infections in humans. Journal of Infectious Diseases, 157 (3 472 479.
4. Buckland, S. T., Burnham, K. P., & Augustin, N. H. (1997). Model selection: An integral part of inference. Biometrics, 53, 603 618.
5. Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. New York : Springer Verlag.
6. Burnham, K. P., Anderson, D. R., White, G. C., Brownie, C., & Pollock, K. H. (1987). Design and analysis methods for fish survival experiments based on release-recapture. American Fisheries Society, Monograph 5.
7. Claeskens, G., & Hjort, N. L. (2003). The focussed information criterion. Journal of the American Statistical Association, 98, 900 916.
8. DuPont, H. L., Chappell, C. L., Sterling, C. R., Okhuysen, P. C., Rose, J. B., & Jakubowski, W. (1995). The infectivity of Cryptosporidium parvum in healthy volunteers. New England Journal of Medicine, 332 (13 855 859.
9. Faes, C., Aerts, M., Geys, H., & Molenberghs, G. (2006b). Model averaging using fractional polynomials to estimate a safe level of exposure. Risk Analysis, 27, 111 123.
10. Faes, C., Geys, H., Aerts, M., & Molenberghs, G. (2003). On the use of fractional polynomial predictors for quantitative risk assessment in developmental toxicity studies. Statistical Modelling, 3, 109 126.
11. Faes, C., Hens, N., Aerts, M., Shkedy, Z., Geys, H., Mintiens, K., Laevens, H., & Boelaert, F. (2006a). Random-effects models for clustered binary data using monotone fractional polynomials; with application to the estimation of a herd-specific force of infection. Applied Statistics, 55, 595 613.
12. Furumoto, W. A., & Mickey, R. (1967). A mathematical model for the infectivity-dilution curve of tobacco mosaic virus: Theoretical considerations. Virology, 32, 216 223.
13. Haas, C. N., Rose, J. B., & Gerba, C. P. (1999). Quantitative Microbial Risk Assessment. New York : Wiley.
14. Hens, N., Faes, C., Aerts, M., Shkedy, Z., Mintiens, K., Laevens, H., & Boelaert, F. (2007). Handling missingness when modelling the force of infection from clustered zeroprevalence data. Journal of Agricultural, Biological and Environmental Statistics, 12, 1 16.
15. Hornick, R. B., Greisman, S. E., Woodward, T. E., DuPont, H. L., Dawkins, A. T., & Snyder, M. J. (1970). Typhoid fever: Pathogenesis and immunological control. New England Journal of Medicine, 283 (13 686 691.
16. Kang, S., Kodell, R. L., & Chen, J. J. (2000). Incorporating model uncertainties along with data uncertainties in microbial risk assessment. Regulatory Toxicology and Pharmacology, 32, 68 72.
17. Kodell, R. L., Kang, S., & Chen, J. J. (2002). Statistical models of health risk due to microbial contamination of foods. Environmental and Ecological Statistics, 9, 259 271.
18. Marks, H. M., Coleman, M. E., Lin, C. J., & Roberts, T. (1998). Topics in microbial risk assessment; Dynamic flow tree process. Risk Analysis, 18, 309 328.
19. Molenberghs, G., & Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York : Springer Verlag.
20. Moon, H., Kim, H., Chen, J. J., & Kodell, R. L. (2005). Model averaging using Kullback information criterion in estimating effective doses for microbial infection and illness. Risk Analysis, 25 (5 1147 1159.
21. Pinsky, P. F. (2000). Assessment of risk from long term exposure to waterborne pathogens. Environmental and Ecological Statistics, 7, 155 175.
22. Royston, P., & Altman, D. (1994). Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling (with discussion). Applied Statistics, 43, 429 467.
23. Shkedy, Z., Aerts, M., Molenberghs, M., Beutels, Ph., & Van Damme, P. (2006). Modeling age-dependent force of infection from prevalence data using fractional polynomials. Statistics in Medicine, 25, 1577 1591.
24. Teunis, P. F. M., & Havelaar, A. H. (2000). The beta Poisson model is not a single hit model. Risk Analysis, 20 (4 511 518.
25. Teunis, P. F. M., Nagelkerke, N. J. D., & Haas, C. N. (1999). Dose-response models for infectious gastroenteritis. Risk Analysis, 19 (6 1251 1260.
26. Teunis, P. F. M., Takumi, K., & Shinagawa, K. (2004). Dose-response for infection by Escherichia coli O157:H7 from outbreak data. Risk Analysis, 24 (2 401 408.
27. Teunis, P. F. M., Van der Heijden, O. G, Van der Giessen, W. B., & Havelaar, A. H. (1996). The Dose-Response Relation in Human Volunteers for Gastro-Intestinal Pathogens. Report Nr. 284550002. National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
28. Wheeler, M. W., & Bailer, A. J. (2007). Properties of model-averaged BMDLs: A study of model averaging in dichotomous response risk estimation. Risk Analysis, 27 (3 659 670.