1932

Abstract

With increasing accessibility to geographic information systems (GIS) software, statisticians and data analysts routinely encounter scientific data sets with geocoded locations. This has generated considerable interest in statistical modeling for location-referenced spatial data. In public health, spatial data routinely arise as aggregates over regions, such as counts or rates over counties, census tracts, or some other administrative delineation. Such data are often referred to as areal data. This review article provides a brief overview of statistical models that account for spatial dependence in areal data. It does so in the context of two applications: disease mapping and spatial survival analysis. Disease maps are used to highlight geographic areas with high and low prevalence, incidence, or mortality rates of a specific disease and the variability of such rates over a spatial domain. They can also be used to detect hot spots or spatial clusters that may arise owing to common environmental, demographic, or cultural effects shared by neighboring regions. Spatial survival analysis refers to the modeling and analysis for geographically referenced time-to-event data, where a subject is followed up to an event (e.g., death or onset of a disease) or is censored, whichever comes first. Spatial survival analysis is used to analyze clustered survival data when the clustering arises from geographical regions or strata. Illustrations are provided in these application domains.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-publhealth-032315-021711
2016-03-18
2024-10-06
Loading full text...

Full text loading...

/deliver/fulltext/publhealth/37/1/annurev-publhealth-032315-021711.html?itemId=/content/journals/10.1146/annurev-publhealth-032315-021711&mimeType=html&fmt=ahah

Literature Cited

  1. Auchincloss AH, Gebreab SY, Mair C, Diez Roux AV. 1.  2012. A review of spatial methods in epidemiology, 2000–2010. Annu. Rev. Public Health 33:107–22 [Google Scholar]
  2. Banerjee S, Carlin B. 2.  2002. Spatial semiparametric proportional hazards models for analyzing infant mortality rates in Minnesota counties. Case Studies in Bayesian Statistics VI C Gatsonis, R Kass, A Carriquiry, A Gelman, D Higdon 137–52 New York: Springer [Google Scholar]
  3. Banerjee S, Carlin B. 3.  2003. Semiparametric spatiotemporal frailty modeling. Environmetrics 14:523–35 [Google Scholar]
  4. Banerjee S, Carlin B. 4.  2004. Parametric spatial cure rate models for interval-censored time-to-relapse data. Biometrics 60:268–75 [Google Scholar]
  5. Banerjee S, Carlin B, Gelfand A. 5.  2014. Hierarchical Modeling and Analysis for Spatial Data Boca Raton, FL: Chapman and Hall/CRC Press, 2nd ed.. [Google Scholar]
  6. Banerjee S, Dey D. 6.  2005. Semiparametric proportional odds model for spatially correlated survival data. Lifetime Data Anal. 11:175–91 [Google Scholar]
  7. Banerjee S, Wall M, Carlin B. 7.  2003. Frailty modelling for spatially correlated survival data with application to infant mortality in Minnesota. Biostatistics 4:123–42 [Google Scholar]
  8. Bastos L, Gamerman D. 8.  2006. Dynamical survival models with spatial frailty. Lifetime Data Anal. 12:441–60 [Google Scholar]
  9. Bennett S. 9.  1983. Analysis of survival data by the proportional odds model. Stat. Med. 2:273–77 [Google Scholar]
  10. Besag J, York J, Mollié A. 10.  1991. Bayesian image restoration, with two applications in spatial statistics (with discussion). Ann. Inst. Stat. Math. 43:1–59 [Google Scholar]
  11. Carlin B, Banerjee S. 11.  2003. Hierarchical multivariate CAR models for spatio-temporally correlated survival data (with discussion). Bayesian Statistics 7 JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman 45–64 Oxford, UK: Oxford Univ. Press [Google Scholar]
  12. Chen M-H, Ibrahim JG, Sinha D. 12.  1999. A new Bayesian model for survival data with a surviving fraction. J. Am. Stat. Assoc. 94:909–19 [Google Scholar]
  13. Cooner F, Banerjee S, Carlin B, Sinha D. 13.  2007. Flexible cure rate modeling under latent activation schemes. J. Am. Stat. Assoc. 102:560–72 [Google Scholar]
  14. Cooner F, Banerjee S, McBean A. 14.  2006. Modelling geographically referenced survival data with a cure fraction. Stat. Methods Med. Res. 15:307–24 [Google Scholar]
  15. Cox D, Oakes D. 15.  1984. Analysis of Survival Data London: Chapman and Hall [Google Scholar]
  16. Cressie N. 16.  1993. Statistics for Spatial Data New York: Wiley, 2nd ed.. [Google Scholar]
  17. Cressie N, Wikle C. 17.  2011. Statistics for Spatio-Temporal Data New York: Wiley, 1st ed.. [Google Scholar]
  18. Cromley E, McLafferty S. 18.  2002. GIS and Public Health New York: Guilford [Google Scholar]
  19. Dean CB, Ugarte MD, Militino AF. 19.  2001. Detecting interaction between random region and fixed age effects in disease mapping. Biometrics 57:197–202 [Google Scholar]
  20. Finkelstein D. 20.  1986. A proportional hazards model for interval-censored failure time data. Biometrics 42:845–54 [Google Scholar]
  21. Gelfand A, Vounatsou P. 21.  2003. Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics 4:11–25 [Google Scholar]
  22. Gelman A, Carlin J, Stern H, Dunson D, Vehtari A, Rubin D. 22.  2013. Bayesian Data Analysis Boca Raton, FL: Chapman and Hall/CRC Press, 3rd ed.. [Google Scholar]
  23. Henderson R, Shikamura S, Gorst D. 23.  2002. Modeling spatial variation in leukemia survival data. J. Am. Stat. Assoc. 97:965–72 [Google Scholar]
  24. Hodges JS, Cui Y, Sargent DJ, Carlin BP. 24.  2007. Smoothing balanced single-error-term analysis of variance. Technometrics 49:12–25 [Google Scholar]
  25. Hurtado Rúa SM, Dey D. 25.  2012. A transformation class for spatio-temporal survival data with a cure fraction. Stat. Methods Med. Res. doi: 10.1177/0962280212445658 [Google Scholar]
  26. Ibrahim J, Chen MH, Sinha D. 26.  2001. Bayesian Survival Analysis New York: Springer-Verlag [Google Scholar]
  27. Jin X, Banerjee S, Carlin B. 27.  2007. Order-free coregionalized lattice models with application to multiple disease mapping. J. R. Stat. Soc. B 69:817–38 [Google Scholar]
  28. Jin X, Carlin B, Banerjee S. 28.  2005. Generalized hierarchical multivariate CAR models for areal data. Biometrics 61:950–61 [Google Scholar]
  29. Lawson A, Choi J, Zhang J. 29.  2014. Prior choice in discrete latent modeling of spatially referenced cancer survival. Stat. Methods Med. Res. 23:183–200 [Google Scholar]
  30. Leroux B, Lei X, Breslow N. 30.  1999. Estimation of disease rates in small areas: a new mixed model for spatial dependence. Statistical Models in Epidemiology, the Environment, and Clinical Trials ME Halloran, D Berry 135–78 New York: Springer [Google Scholar]
  31. Li Y, Ryan L. 31.  2002. Modeling spatial survival data using semiparametric frailty models. Biometrics 58:287–97 [Google Scholar]
  32. Møller J. 32.  2003. Spatial Statistics and Computational Methods New York: Springer [Google Scholar]
  33. Murray R, Anthonisen N, Connett J, Wise R, Lindgren P. 33.  et al. 1998. Effects of multiple attempts to quit smoking and relapses to smoking on pulmonary function. Lung Health Study Research Group. J. Clin. Epidemiol. 51:1317–26 [Google Scholar]
  34. Othus M, Barlogie B, LeBlanc M, Crowley J. 34.  2012. Cure models as a useful statistical tool for analyzing survival. Clin. Cancer Res. 18:3731–36 [Google Scholar]
  35. Robert C, Casella G. 35.  2005. Monte Carlo Statistical Methods New York: Springer [Google Scholar]
  36. Rushton G. 36.  2003. Public health, GIS, and spatial analytic tools. Annu. Rev. Public Health 24:43–56 [Google Scholar]
  37. Schabenberger O, Gotway C. 37.  2004. Statistical Methods for Spatial Data Analysis Boca Raton, FL: Chapman and Hall/CRC [Google Scholar]
  38. Wackernagel H. 38.  2003. Multivariate Geostatistics: An Introduction With Applications New York: Springer, 3rd ed.. [Google Scholar]
  39. Wall M. 39.  2004. A close look at the spatial structure implied by the CAR and SAR models. J. Stat. Plann. Inference 121:311–24 [Google Scholar]
  40. Waller L, Gotway C. 40.  2004. Applied Spatial Statistics for Public Health Data New York: Wiley [Google Scholar]
  41. Webster R, Oliver M. 41.  2001. Geostatistics for Environmental Scientists New York: Wiley [Google Scholar]
  42. Zhang J, Lawson AB. 42.  2011. Bayesian parametric accelerated failure time spatial model and its application to prostate cancer. J. Appl. Stat. 38:591–603 [Google Scholar]
  43. Zhang Y, Hodges J, Banerjee S. 43.  2009. Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. Ann. Appl. Stat. 3:1805–30 [Google Scholar]
  44. Zhou H, Lawson AB, Hebert J, Slate E, Hill E. 44.  2008. Joint spatial survival modelling for the date of diagnosis and the vital outcome for prostate cancer. Stat. Med. 27:3612–28 [Google Scholar]
/content/journals/10.1146/annurev-publhealth-032315-021711
Loading
/content/journals/10.1146/annurev-publhealth-032315-021711
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error