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Spatial Relative Risk of Upper Aerodigestive Tract Cancers Incidence in French Northern Region

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Abstract

In this work, kernel spatial relative risk function estimation is of interest. We consider the case where covariates that may affect the spatial patterns of disease are contaminated by measurement errors. Finite sample properties were carried out in order to illustrate our methodology with real cancer data. We perform relative risk functions estimation on upper aerodigestive tract cancer (UADT) data to investigate locations of high and low incidence concentration in NPDC (Nord-Pas-de-Calais) French region.

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Notes

  1. https://cran.r-project.org/web/packages/sparr/sparr.pdf.

  2. This is an extended version of the iteratively weighted least squares of GLMs.

References

  1. Davies TM, Hazelton ML. Adaptive kernel estimation of spatial relative risk. Stat Med. 2010;29(23):2423–37. https://doi.org/10.1002/sim.3995.

    Article  MathSciNet  Google Scholar 

  2. Kelsall JE, Diggle PJ. Spatial variation in risk of disease: a nonparametric binary regression approach. J R Stat Soc: Ser C (Appl Stat). 1998;47(4):559–73.

    Article  MATH  Google Scholar 

  3. Hazelton ML. Testing for changes in spatial relative risk. Stat Med. 2017;36(17):2735–49. https://doi.org/10.1002/sim.7306.

    Article  MathSciNet  Google Scholar 

  4. Kelsall JE, Diggle PJ. Kernel estimation of relative risk. Bernoulli. 1995;1(1–2):3–16. https://doi.org/10.2307/3318678.

    Article  MathSciNet  MATH  Google Scholar 

  5. Davies TM, Jones K, Hazelton ML. Symmetric adaptive smoothing regimens for estimation of the spatial relative risk function. Comput Statist Data Anal. 2016;101:12–28. https://doi.org/10.1016/j.csda.2016.02.008.

    Article  MathSciNet  MATH  Google Scholar 

  6. Borchers D, Buckland S, Priede I, Ahmadi S. Improving the precision of the daily egg production method using generalized additive models. Can J Fish Aquat Sci. 1997;54(12):2727–42.

    Article  Google Scholar 

  7. Bellido J, Pierce G, Wang J. Modelling intra-annual variation in abundance of squid loligo forbesi in scottish waters using generalised additive models. Fish Res. 2001;52(1):23–39.

    Article  Google Scholar 

  8. Zheng X, Pierce G, Reid D, Jolliffe I. Does the north atlantic current affect spatial distribution of whiting? testing environmental hypotheses using statistical and gis techniques. ICES J Mar Sci: J du Conseil. 2002;59(2):239–53.

    Article  Google Scholar 

  9. Ozonoff A, Webster T, Vieira V, Weinberg J, Ozonoff D, Aschengrau A. Cluster detection methods applied to the upper cape cod cancer data. Environ Health. 2005;4(1):1.

    Article  Google Scholar 

  10. Diggle PJ, Giorgi E. Model-based geostatistics for prevalence mapping in low-resource settings. J Am Stat Assoc. 2016;111(515):1096–120. https://doi.org/10.1080/01621459.2015.1123158.

    Article  MathSciNet  Google Scholar 

  11. Prates MO, Kulldorff M, Assunção RM. Relative risk estimates from spatial and space-time scan statistics: are they biased? Stat Med. 2014;33(15):2634–44. https://doi.org/10.1002/sim.6143.

    Article  MathSciNet  Google Scholar 

  12. Mathialagan PMC. Computer vision techniques for upper aero-digestive tract tumor grading classification-addressing pathological challenges; 2021.

  13. Shankargouda Patil KHA, et al. Machine learning and its potential applications to the genomic study of head and neck cancer-a systematic review 2019:533–549

  14. Hastie T, Tibshirani R. Generalized additive models: some applications. J Am Stat Assoc. 1987;82(398):371–86.

    Article  MATH  Google Scholar 

  15. Poljak Z, Dewey CE, Rosendal T, Friendship RM, Young B, Berke O. Spread of porcine circovirus associated disease (pcvad) in ontario (canada) swine herds: Part i. exploratory spatial analysis. BMC Vet Res. 2010;6(1):1.

    Google Scholar 

  16. Prentice R. Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika. 1982;69(2):331–42.

    Article  MathSciNet  MATH  Google Scholar 

  17. Carroll RJ, Hall P. Optimal rates of convergence for deconvolving a density. J Am Stat Assoc. 1988;83(404):1184–6.

    Article  MathSciNet  MATH  Google Scholar 

  18. Fan J. Asymptotic normality for deconvolution kernel density estimators. Sankhyā Ser A. 1991;53(1):97–110.

    MathSciNet  MATH  Google Scholar 

  19. Truong YK. Survival time regression involving covariate measurement error. Bull Inform Cybernet. 1995;27(1):31–51.

    Article  MathSciNet  MATH  Google Scholar 

  20. Delaigle A, Gijbels I. Estimation of integrated squared density derivatives from a contaminated sample. J R Stat Soc Ser B Stat Methodol. 2002;64(4):869–86. https://doi.org/10.1111/1467-9868.00366.

    Article  MathSciNet  MATH  Google Scholar 

  21. Fan J, Masry E. Multivariate regression estimation with errors-in-variables: asymptotic normality for mixing processes. J Multivar Anal. 1992;43(2):237–71.

    Article  MathSciNet  MATH  Google Scholar 

  22. Fan J, Truong YK. Nonparametric regression with errors in variables. Ann Stat. 1993;21(4):1900–25. https://doi.org/10.1214/aos/1176349402.

    Article  MathSciNet  MATH  Google Scholar 

  23. Devroye L. Consistent deconvolution in density estimation. Can J Stat. 1989;17(2):235–9. https://doi.org/10.2307/3314852.

    Article  MathSciNet  MATH  Google Scholar 

  24. Stefanski LA. Rates of convergence of some estimators in a class of deconvolution problems. Stat Probab Lett. 1990;9(3):229–35. https://doi.org/10.1016/0167-7152(90)90061-B.

    Article  MathSciNet  MATH  Google Scholar 

  25. Stefanski LA, Carroll RJ. Deconvolving kernel density estimators. Statistics. 1990;21(2):169–84.

    Article  MathSciNet  MATH  Google Scholar 

  26. Fan J. On the optimal rates of convergence for nonparametric deconvolution problems. Ann Stat. 1991;19(3):1257–72. https://doi.org/10.1214/aos/1176348248.

    Article  MathSciNet  MATH  Google Scholar 

  27. Fan J. Global behavior of deconvolution kernel estimates. Statistica Sinica, 1991;541–551

  28. Fan J. Deconvolution with supersmooth distributions. Can J Stat. 1992;20(2):155–69.

    Article  MathSciNet  MATH  Google Scholar 

  29. Masry E. Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes. J Multivar Anal. 1993;44(1):47–68. https://doi.org/10.1006/jmva.1993.1003.

    Article  MathSciNet  MATH  Google Scholar 

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Author notes

  1. All the authors have contributed equally to this work.

Authors and Affiliations

  1. Université de Lille, CNRS, UMR 8524-Laboratoire Paul Painlevé, INRIA-MODAL, 59000, Lille, France

    Sophie Dabo-Niang

  2. Université de Lille, IUT, 59000, Lille, France

    Emad Darwich

  3. Ecole nationale Supérieure en Informatique (ESI), Laboratoire LCSI, BP 68M, Oued El Smar, 16309, El Harrach, Algeria

    Leila Hamdad

  4. Université de Lille, CNRS, UMR 8524-Laboratoire Paul Painlevé, 59000, Lille, France

    Baba Thiam

Authors
  1. Sophie Dabo-Niang
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  2. Emad Darwich
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  3. Leila Hamdad
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  4. Baba Thiam
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Correspondence to Leila Hamdad.

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This article is part of the topical collection “Innovative AI in Medical Applications” guest edited by Lydia Bouzar-Benlabiod, Stuart H. Rubin and Edwige Pissaloux.

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Dabo-Niang, S., Darwich, E., Hamdad, L. et al. Spatial Relative Risk of Upper Aerodigestive Tract Cancers Incidence in French Northern Region. SN COMPUT. SCI. 4, 30 (2023). https://doi.org/10.1007/s42979-022-01426-0

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