Skip to main content

Spatial Survival Analysis

  • Reference work entry
  • First Online:
Encyclopedia of GIS

Synonyms

Correlated frailty models; Spatial frailty modeling

Definition

The statistical modeling of censored time-to-event data is the realm of survival analysis; when such data can be located in space and there is scientific interest in understanding the spatial variation in survival outcomes, a spatial survival analysis is performed. The concept of censoring is what makes survival data statistically interesting: for each observation in our dataset, we either observe the time of an event (such as death, disease progression, successful treatment, etc.) or the time or interval of time in which the observation was lost to follow-up. The observations that are lost to follow-up are the censored observations. Spatial survival analysis combines techniques from geostatistics and survival analysis to answer scientific questions like: where in space is the rate of events unusually high (or low)?

Historical Background

Survival analysis per se has a long tradition in the medical sciences, the...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,599.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,999.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aalen O (1978) Nonparametric inference for a family of counting processes. Ann Stat 6(4):701–726

    Article  MathSciNet  MATH  Google Scholar 

  • Banerjee S, Carlin BP (2002) Case studies in Bayesian statistics. Spatial semi-parametric proportional hazards models for analyzing infant mortality rates in Minnesota counties, chapter 6 Springer, New York, pp 137–151

    Google Scholar 

  • Banerjee S, Carlin BP (2003) Semiparametric spatio-temporal frailty modeling. Environmetrics 14(5): 523–535

    Article  Google Scholar 

  • Banerjee S, Carlin BP (2004) Parametric spatial cure rate models for interval-censored time-to-relapse data. Biometrics 60(1):268–275

    Article  MathSciNet  MATH  Google Scholar 

  • Banerjee S, Carlin BP, Gelfand AE (2014) Hierarchical modeling and analysis for spatial data, 2nd edn. Chapman and Hall/CRC, Boca Raton

    MATH  Google Scholar 

  • Banerjee S, Dey DK (2005) Semiparametric proportional odds models for spatially correlated survival data. Lifetime Data Anal 11(2):175–191

    Article  MathSciNet  MATH  Google Scholar 

  • Banerjee S, Wall MM, Carlin BP (2003) Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics 4(1):123–142

    Article  MATH  Google Scholar 

  • Besag J (1974) Spatial interaction and the statistical analysis of lattice systems. J R Stat Soc Ser B (Methodol) 36(2):192–236

    MathSciNet  MATH  Google Scholar 

  • Besag J, Kooperberg C (1995) On conditional and intrinsic autoregression. Biometrika 82(4):733–746

    MathSciNet  MATH  Google Scholar 

  • Besag J, York JC, Mollie A (1991) Bayesian image restoration with two applications in spatial statistics. Ann Inst Stat Math 43:22–24

    MathSciNet  MATH  Google Scholar 

  • Bhatt V, Tiwari N (2014) A spatial scan statistic for survival data based on Weibull distribution. Stat Med 33(11):1867–1876

    Article  MathSciNet  Google Scholar 

  • Brix A, Diggle PJ (2001) Spatiotemporal prediction for log-Gaussian Cox processes. J R Stat Soc Ser B 63(4):823–841

    Article  MathSciNet  MATH  Google Scholar 

  • Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B 34(2):187–220

    MathSciNet  MATH  Google Scholar 

  • Cox DR, Oakes D (1984) Analysis of survival data. Chapman & Hall/CRC monographs on statistics & applied probability. CRC Press, London/New York

    Google Scholar 

  • Darmofal D (2009) Bayesian spatial survival models for political event processes. Am J Polit Sci 53(1):241–257

    Article  Google Scholar 

  • Diva U, Dey DK, Banerjee S (2008) Parametric models for spatially correlated survival data for individuals with multiple cancers. Stat Med 27(12):2127–44

    Article  MathSciNet  Google Scholar 

  • Henderson R, Shimakura S, Gorst D (2002) Modeling spatial variation in leukemia survival data. J Am Stat Assoc 97:965–972

    Article  MathSciNet  MATH  Google Scholar 

  • Hennerfeind A, Brezger A, Fahrmeir L (2006) Geoadditive survival models. J Am Stat Assoc 101(475): 1065–1075

    Article  MathSciNet  MATH  Google Scholar 

  • Huang L, Kulldorff M, Gregorio D (2007) A spatial scan statistic for survival data. Biometrics 63(1):109–118

    Article  MathSciNet  MATH  Google Scholar 

  • Jerrett M, Burnett RT, Ma R, Arden Pope III C, Krewski D, Newbold KB, Thurston G, Shi Y, Finkelstein N, Calle EE, et al (2005) Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 16(6):727–736

    Article  Google Scholar 

  • Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53(282):457–481

    Article  MathSciNet  MATH  Google Scholar 

  • Klein JP, Ibrahim JG, Scheike TH, van Houwelingen JC, Van Houwelingen HC (2013) Handbook of survival analysis. Chapman and Hall/CRC handbooks of modern statistical methods series. Taylor & Francis Group, Boca Raton

    MATH  Google Scholar 

  • Klein JP, Moeschberger ML (2003) Survival analysis: techniques for censored and truncated data. Statistics for biology and health. Springer, New York

    MATH  Google Scholar 

  • Krige D (1951) A statistical approach to some basic mine valuation problems on the witwatersrand. J Chem Metall Mining Soc S Afr 52:119–139

    Google Scholar 

  • Li Y, Lin X (2006) Semiparametric normal transformation models for spatially correlated survival data. J Am Stat Assoc 101(474):591–603

    Article  MathSciNet  MATH  Google Scholar 

  • Li Y, Ryan L (2002) Modeling spatial survival data using semiparametric frailty models. Biometrics 58(2): 287–297

    Article  MathSciNet  MATH  Google Scholar 

  • Lindgren F, Rue H, Lindström J (2011) An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J R Stat Soc Ser B 73(4):423–498

    Article  MathSciNet  MATH  Google Scholar 

  • Nelson W (1969) Hazard plotting for incomplete failure data. J Qual Technol 1:27–52

    Google Scholar 

  • Paik J, Ying Z (2012) A composite likelihood approach for spatially correlated survival data. Comput Stat Data Anal 56(1):209–216

    Article  MathSciNet  MATH  Google Scholar 

  • Pickles AR, Crouchley R (1994) Generalizations and applications of frailty models for survival and event data. Stat Methods Med Res 3:263–278

    Article  Google Scholar 

  • Royston P, Parmar MK (2002) Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med 21(15):2175–2197

    Article  Google Scholar 

  • Rue H, Martino S, Chopin N (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Ser B 71(2):319–392

    Article  MathSciNet  MATH  Google Scholar 

  • Taylor BM (2015) Auxiliary variable Markov chain Monte Carlo for spatial survival and geostatistical models. Available from http://arxiv.org/abs/ISO1.01665http://arxiv.org/abs/ISO1.01665

  • Taylor BM, Rowlingson BS (2014, to appear) spatsurv: an R package for Bayesian inference with spatial survival models. J Stat Softw

    Google Scholar 

  • Tonda T, Satoh K, Otani K, Sato Y, Maruyama H, Kawakami H, Tashiro S, Hoshi M, Ohtaki M (2012) Investigation on circular asymmetry of geographical distribution in cancer mortality of Hiroshima atomic bomb survivors based on risk maps: analysis of spatial survival data. Radiat Environ Biophys 51(2):133–141

    Article  Google Scholar 

  • Wall MM (2004) A close look at the spatial structure implied by the CAR and SAR models. J Stat Plan Inf 121:311–324

    Article  MathSciNet  MATH  Google Scholar 

  • Wienke A (2010) Frailty models in survival analysis. Chapman & Hall/CRC biostatistics series. CRC Press, Boca Raton

    Book  Google Scholar 

  • Zhang J, Lawson AB (2011) Bayesian parametric accelerated failure time spatial model and its application to prostate cancer. J Appl Stat 38(2):591–603

    Article  MathSciNet  Google Scholar 

  • Zhao L, Hanson TE (2011) Spatially dependent Polya tree modeling for survival data. Biometrics 67(2):391–403

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The author is very grateful to Professor Robin Henderson for allowing him to access and use an anonymized version of the leukemia data from Henderson et al. (2002). These data were used to produce figures1–3.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benjamin M. Taylor .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this entry

Cite this entry

Taylor, B.M. (2017). Spatial Survival Analysis. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1639

Download citation

Publish with us

Policies and ethics