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Multiple Imputation for the Fatal Accident Reporting System

Author

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  • Daniel F. Heitjan
  • Roderick J. A. Little

Abstract

The Fatal Accident Reporting System (FARS) is a database collected for the US National Highway Traffic Safety Administration (NHTSA) at the site of all fatal traffic accidents. Variables include location and time of accident, number and position of vehicles, age, sex and driving record of the driver, seat‐belt use and blood alcohol content of the driver. The last two variables are of great interest but have substantial proportions of missing data. The NHTSA is interested in a method of imputation that allows appropriate estimates and standard errors to be computed from the filled‐in data. This paper explores the use of multiple imputation based on predictive mean matching as a means of achieving these goals. Two specific methods are described and applied to a sample of the FARS data. A simulation study compares the frequency properties of the methods.

Suggested Citation

  • Daniel F. Heitjan & Roderick J. A. Little, 1991. "Multiple Imputation for the Fatal Accident Reporting System," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 13-29, March.
    • Handle: RePEc:bla:jorssc:v:40:y:1991:i:1:p:13-29
    DOI: 10.2307/2347902
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    Cited by:

    1. Chia-Ning Wang & Roderick Little & Bin Nan & Siobán D. Harlow, 2011. "A Hot-Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories," Biometrics, The International Biometric Society, vol. 67(4), pages 1573-1582, December.
    2. Patrick Lloyd‐Smith, 2021. "The economic benefits of recreation in Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(4), pages 1684-1715, November.
    3. Diane C. Lestina & Michael Greene & Robert B. Voas & Joann Wells, 1999. "Sampling Procedures and Survey Methodologies for the 1996 Survey with Comparisons to Earlier National Roadside Surveys," Evaluation Review, , vol. 23(1), pages 28-46, February.
    4. Wenqing Jiang & Jiangjie Zhou & Baosheng Liang, 2023. "An Improved Dunnett’s Procedure for Comparing Multiple Treatments with a Control in the Presence of Missing Observations," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
    5. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    6. Gabriele B. Durrant & Chris Skinner, 2006. "Using data augmentation to correct for non‐ignorable non‐response when surrogate data are available: an application to the distribution of hourly pay," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 605-623, July.
    7. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    8. Lana Salih Joelsson & Evangelia Elenis & Kjell Wanggren & Anna Berglund & Anastasia N Iliadou & Carolyn E Cesta & Sunni L Mumford & Richard White & Tanja Tydén & Alkistis Skalkidou, 2019. "Investigating the effect of lifestyle risk factors upon number of aspirated and mature oocytes in in vitro fertilization cycles: Interaction with antral follicle count," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-15, August.
    9. Chenyang Gu & Roee Gutman, 2017. "Combining item response theory with multiple imputation to equate health assessment questionnaires," Biometrics, The International Biometric Society, vol. 73(3), pages 990-998, September.
    10. Chaton, Corinne & Gouraud, Alexandre, 2020. "Simulation of fuel poverty in France," Energy Policy, Elsevier, vol. 140(C).
    11. Kwon, Tae Yeon & Park, Yousung, 2015. "A new multiple imputation method for bounded missing values," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 204-209.
    12. Marco Di Zio & Ugo Guarnera, 2008. "A multiple imputation method for non-Gaussian data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 75-90.
    13. Patrick M. Joyce & Donald Malec & Roderick J. A. Little & Aaron Gilary & Alfredo Navarro & Mark E. Asiala, 2014. "Statistical Modeling Methodology for the Voting Rights Act Section 203 Language Assistance Determinations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 36-47, March.
    14. Encarnita Mariotti-Ferrandiz & Hang-Phuong Pham & Sophie Dulauroy & Olivier Gorgette & David Klatzmann & Pierre-André Cazenave & Sylviane Pied & Adrien Six, 2016. "A TCRβ Repertoire Signature Can Predict Experimental Cerebral Malaria," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    15. Madero-Cabib, Ignacio & Fasang, Anette Eva, 2016. "Gendered work-family life courses and financial well-being in retirement," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 27, pages 43-60.
    16. Chiu-Hsieh Hsu & Jeremy Taylor & Susan Murray, 2004. "Multiple Imputation For Interval Censored Data With Auxiliary Variables," The University of Michigan Department of Biostatistics Working Paper Series 1025, Berkeley Electronic Press.
    17. Shu Yang & Jae Kwang Kim, 2020. "Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 839-861, September.
    18. Schenker, Nathaniel & Taylor, Jeremy M. G., 1996. "Partially parametric techniques for multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 425-446, August.
    19. Vicki Freedman & Douglas Wolf, 1995. "A case study on the use of multiple imputation," Demography, Springer;Population Association of America (PAA), vol. 32(3), pages 459-470, August.
    20. repec:jss:jstsof:45:i02 is not listed on IDEAS
    21. Benjamin Gochanour & Sixia Chen & Laura Beebe & David Haziza, 2023. "A semiparametric multiply robust multiple imputation method for causal inference," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(5), pages 517-542, July.
    22. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
    23. Taylor, Jeremy M. G. & Murray, Susan & Hsu, Chiu-Hsieh, 2002. "Survival estimation and testing via multiple imputation," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 221-232, July.
    24. Daniel McNeish, 2017. "Missing data methods for arbitrary missingness with small samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 24-39, January.
    25. Chris Skinner & Nigel Stuttard & Gabriele Beissel‐Durrant & James Jenkins, 2002. "The Measurement of Low Pay in the UK Labour Force Survey," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(supplemen), pages 653-676, December.

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