Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Multivariate Bayesian models with flexible shared interactions for analyzing spatio-temporal patterns of rare cancers (2403.10440v2)

Published 15 Mar 2024 in stat.ME and stat.AP

Abstract: Rare cancers affect millions of people worldwide each year. However, estimating incidence or mortality rates associated with rare cancers presents important difficulties and poses new statistical methodological challenges. In this paper, we expand the collection of multivariate spatio-temporal models by introducing adaptable shared spatio-temporal components to enable a comprehensive analysis of both incidence and cancer mortality in rare cancer cases. These models allow the modulation of spatio-temporal effects between incidence and mortality, allowing for changes in their relationship over time. The new models have been implemented in INLA using r-generic constructions. We conduct a simulation study to evaluate the performance of the new spatio-temporal models. Our results show that multivariate spatio-temporal models incorporating a flexible shared spatio-temporal term outperform conventional multivariate spatio-temporal models that include specific spatio-temporal effects for each health outcome. We use these models to analyze incidence and mortality data for pancreatic cancer and leukaemia among males across 142 administrative health care districts of Great Britain over a span of nine biennial periods (2002-2019).

Definition Search Book Streamline Icon: https://streamlinehq.com
References (47)
  1. Alleviating confounding in spatio-temporal areal models with an application on crimes against women in india. Statistical Modelling, 23(1):9–30, 2023. doi: https://doi.org/10.1177/1471082X211015452.
  2. Julian Besag. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society: Series B (Methodological), 36(2):192–225, 1974. doi: https://doi.org/10.1111/j.2517-6161.1974.tb00999.x.
  3. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1):1–20, 1991. doi: https://doi.org/10.1007/BF00116466.
  4. Epidemiology and etiology of leukemia and lymphoma. Cold Spring Harbor perspectives in medicine, 10(6), 2020. doi: https://doi.org/10.1101/cshperspect.a034819.
  5. A unifying modeling framework for highly multivariate disease mapping. Statistics in Medicine, 34(9):1548–1559, 2015. doi: https://doi.org/10.1002/sim.6423.
  6. Bayesian estimates of the incidence of rare cancers in europe. Cancer Epidemiology, 54:95–100, 2018. doi: https://doi.org/10.1016/j.canep.2018.04.003.
  7. Incidence and survival of rare cancers in the US and Europe. Cancer Medicine, 9(15):5632–5642, 2020. doi: https://doi.org/10.1002/cam4.3137.
  8. Trends in colorectal cancer incidence and survival in iowa seer data: The timing of it all. Clinical colorectal cancer, 18(2):e261–e274, 2019. doi: https://doi.org/10.1016/j.clcc.2018.12.001.
  9. Bayesian spatio-temporal model with inla for dengue fever risk prediction in costa rica. Environmental and Ecological Statistics, 30(4):687–713, 2023. doi: https://doi.org/10.1007/s10651-023-00580-9.
  10. Inferring lung cancer risk factor patterns through joint bayesian spatio-temporal analysis. Cancer Epidemiology, 39(3):430–439, 2015. doi: https://doi.org/10.1016/j.canep.2015.03.001.
  11. Joint modelling of brain cancer incidence and mortality using Bayesian age-and gender-specific shared component models. Stochastic Environmental Research and Risk Assessment, 32(10):2951–2969, 2018. doi: https://doi.org/10.1007/s00477-018-1567-4.
  12. Using mortality to predict incidence for rare and lethal cancers in very small areas. Biometrical Journal, 65(3):2200017, 2023. doi: https://doi.org/10.1002/bimj.202200017.
  13. Pancreatic cancer incidence and survival and the role of specialist centres in resection rates in england, 2000 to 2014: a population-based study. Pancreatology, 20(3):454–461, 2020. doi: https://doi.org/10.1016/j.pan.2020.01.012.
  14. Rare cancers are not so rare: The rare cancer burden in europe. European Journal of Cancer, 47(17):2493–2511, 2011. doi: https://doi.org/10.1016/j.ejca.2011.08.008.
  15. Burden and centralised treatment in europe of rare tumours: results of rarecarenet - a population-based study. The Lancet Oncology, 18(8):1022–1039, 2017. doi: https://doi.org/10.1016/S1470-2045(17)30445-X.
  16. Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics, 4(1):11–15, 2003. doi: https://doi.org/10.1093/biostatistics/4.1.11.
  17. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477):359–378, 2007. doi: https://doi.org/10.1198/016214506000001437.
  18. In spatio-temporal disease mapping models, identifiability constraints affect pql and inla results. Stochastic Environmental Research and Risk Assessment, 32(3):749–770, 2018. doi: https://doi.org/10.1007/s00477-017-1405-0.
  19. Age–space–time car models in bayesian disease mapping. Statistics in Medicine, 35(14):2391–2405, 2016. doi: https://doi.org/10.1002/sim.6873.
  20. Virgilio Gómez-Rubio. Bayesian inference with INLA. Chapman and Hall/CRC, New York, NY, 2020. doi: https://doi.org/10.1201/9781315175584.
  21. Towards joint disease mapping. Statistical Methods in Medical Research, 14(1):61–82, 2005. doi: https://doi.org/10.1191/0962280205sm389oa.
  22. Joint spatial analysis of gastrointestinal infectious diseases. Statistical Methods in Medical Research, 15(5):465–480, 2006. doi: https://doi.org/10.1177/0962280206071642.
  23. Generalized hierarchical multivariate car models for areal data. Biometrics, 61(4):950–961, 2005. doi: https://doi.org/10.1111/j.1541-0420.2005.00359.x.
  24. Order-free co-regionalized areal data models with application to multiple-disease mapping. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(5):817–838, 2007. doi: https://doi.org/10.1111/j.1467-9868.2007.00612.x.
  25. Estimating areas of common risk in low birth weight and infant mortality in namibia: A joint spatial analysis at sub-regional level. Spatial and spatio-temporal Epidemiology, 12:27–37, 2015. doi: https://doi.org/10.1016/j.sste.2015.02.001.
  26. Leonhard Knorr-Held. Bayesian modelling of inseparable space-time variation in disease risk. Statistics in Medicine, 19(17-18):2555–2567, 2000.
  27. A bayesian spatial shared component model for identifying crime-general and crime-specific hotspots. Annals of GIS, 26(1):65–79, 2020. doi: https://doi.org/10.1080/19475683.2020.1720290.
  28. Ying C MacNab. On gaussian markov random fields and bayesian disease mapping. Statistical Methods in Medical Research, 20(1):49–68, 2011. doi: https://doi.org/10.1177/0962280210371561.
  29. Methodological aspects of estimating rare cancer prevalence in Europe: the experience of the RARECARE project. Cancer Epidemiology, 37(6):850–856, 2013. doi: https://doi.org/10.1016/j.canep.2013.08.001.
  30. KV Mardia. Multi-dimensional multivariate gaussian markov random fields with application to image processing. Journal of Multivariate Analysis, 24(2):265–284, 1988. doi: https://doi.org/10.1016/0047-259X(88)90040-1.
  31. Miguel A Martinez-Beneito. A general modelling framework for multivariate disease mapping. Biometrika, 100(3):539–553, 2013. doi: https://doi.org/10.1093/biomet/ast023.
  32. Integrated nested Laplace approximations (INLA). Wiley StatsRef: Statistics Reference Online, pages 1–19, 2019. doi: https://doi.org/10.1002/9781118445112.stat08212.
  33. Implementing approximate Bayesian inference using integrated nested Laplace approximation: A manual for the inla program. Department of Mathematical Sciences, NTNU, Norway, 2009.
  34. Bayesian computing with inla: New features. Computational Statistics & Data Analysis, 67:68–83, 2013. doi: https://doi.org/10.1016/j.csda.2013.04.014.
  35. Estimating locp cancer mortality rates in small domains in spain using its relationship with lung cancer. Scientific Reports, 11(1):22273, 2021. doi: https://doi.org/10.1038/s41598-021-01765-7.
  36. Predicting cancer incidence in regions without population-based cancer registries using mortality. Journal of the Royal Statistical Society Series A: Statistics in Society, page qnad077, 2023. doi: https://doi.org/10.1093/jrsssa/qnad077.
  37. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4):1145–1165, 2016. doi: https://doi.org/10.1177/0962280216660421.
  38. Gaussian Markov random fields: theory and applications. Chapman and Hall/CRC, New York, NY, 2005. doi: https://doi.org/10.1201/9780203492024.
  39. Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(2):319–392, 2009. doi: https://doi.org/10.1111/j.1467-9868.2008.00700.x.
  40. Estimating country-specific incidence rates of rare cancers: comparative performance analysis of modeling approaches using European cancer registry data. American Journal of Epidemiology, 191(3):487–498, 2022. doi: https://doi.org/10.1093/aje/kwab262.
  41. Bayesian spatio-temporal analysis of stomach cancer incidence in iran, 2003–2010. Stochastic environmental research and risk assessment, 32:2943–2950, 2018. doi: https://doi.org/10.1007/s00477-018-1531-3.
  42. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4):583–639, 2002. doi: https://doi.org/10.1111/1467-9868.00353.
  43. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 71(3):209–249, 2021. doi: https://doi.org/10.3322/caac.21660.
  44. Yung Liang Tong. The multivariate normal distribution. Springer, New York, NY, 2012. doi: https://doi.org/10.1007/978-1-4613-9655-0.
  45. Generalized common spatial factor model. Biostatistics, 4(4):569–582, 2003. doi: https://doi.org/10.1093/biostatistics/4.4.569.
  46. Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 2010.
  47. Pancreatic cancer: a review of risk factors, diagnosis, and treatment. Technology in cancer research & treatment, 19:1533033820962117, 2020. doi: https://doi.org/10.1177/1533033820962117.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets