A Bayesian normal mixture accelerated failure time spatial model and its application to prostate cancer

Statistical Methods in Medical Research

Volumn 25, Issue 2, 2016, Pages 793-806

  Wang, Songfeng ^a   Zhang, Jiajia ^a   Lawson, Andrew B ^b  

^a UNIVERSITY OF SOUTH CAROLINA   (United States)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

Accelerated failure time spatial model; conditional autoregressive model; Log pseudo marginal likelihood; normal mixture

Indexed keywords

AGED; ARTICLE; BAYESIAN NORMAL MIXTURE ACCELERATED FAILURE TIME SPATIAL MODEL; CANCER MORTALITY; CANCER REGISTRY; CONTROLLED STUDY; GEOGRAPHIC DISTRIBUTION; HUMAN; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL MODEL; METHODOLOGY; PREDICTION; PROBABILITY; PROSTATE CANCER; RACE DIFFERENCE; RISK ASSESSMENT; RISK FACTOR; SIMULATION; SURVIVAL TIME; UNITED STATES; ANCESTRY GROUP; BAYES THEOREM; COMPUTER SIMULATION; GEOGRAPHIC MAPPING; INCIDENCE; KAPLAN MEIER METHOD; LOUISIANA; MORTALITY; PROSTATE TUMOR; STATISTICS AND NUMERICAL DATA;

BAYES THEOREM; COMPUTER SIMULATION; CONTINENTAL POPULATION GROUPS; GEOGRAPHIC MAPPING; HUMANS; INCIDENCE; KAPLAN-MEIER ESTIMATE; LOUISIANA; MALE; PROSTATIC NEOPLASMS;

EID: 84964309644     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212466189     Document Type: Article

References (33)

1. National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch. Surveillance, epidemiology and end results (SEER) Program, www.seer.cancer.gov, limited-use data (1973-2007). (released April 2008, based on the November 2007 submission).
2. Zhang J, Lawson AB,. Bayesian parametric accelerated failure time spatial model and its application to prostate cancer. J Appl Statis 2011; 38 (3): 591-603.
3. Osnes K, Aalen OO,. Spatial smoothing of cancer survival: a Bayesian approach. Stat Med 1999; 18 (16): 2087-2099.
4. Henderson R, Shimakura S, Gorst D,. Modeling spatial variation in leukemia survival data. J Am Stat Assoc 2002; 97 (460): 965-972.
5. Li Y, Ryan L,. Modeling spatial survival data using semiparametric frailty models. Biometrics 2002; 58 (2): 287-297.
6. Banerjee S, Wall MM, Carlin BP,. Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota. Biostatistics 2003; 4 (1): 123-142.
7. Banerjee S, Dey DK,. Semiparametric proportional odds models for spatially correlated survival data. Lifetime Data Anal 2005; 11 (2): 175-191.
8. Hennerfeind A, Brezger A, Fahrmeir L,. Geoadditive survival models. J Am Stat Assoc 2006; 101 (475): 1065-1075.
9. Darmofal D,. Bayesian spatial survival models for political event processes. Am J Polit Sci 2009; 53 (1): 241-257.
10. Schoenfeld D,. Partial residuals for the proportional hazards regression model. Biometrika 1982; 69 (1): 239-241.
11. Komárek A, Lesaffre E, Hilton JF,. Accelerated failure time model for arbitrarily censored data with smoothed error distribution. J Comput Graph Statist 2005; 14 (3): 726-745.
12. Komárek A, Lesaffre E,. Bayesian semi-parametric accelerated failure time model for paired doubly interval-censored data. Stat Modelling 2006; 6 (1): 3-22.
13. Komárek A, Lesaffre E,. Bayesian accelerated failure time model for correlated interval-censored data with a normal mixture as error distribution. Stat Sinica 2007; 17 (2): 549-569.
14. Besag J, York J, Mollié A,. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math 1991; 43 (1): 1-59.
15. Roeder K, Wasserman L,. Practical Bayesian density estimation using mixtures of normals. J Am Stat Assoc 1997; 92 (439): 894-902.
16. Spiegelhalter DJ, Best NG, Carlin BP, et al. Bayesian measures of model complexity and fit. J R Stat Soc, Ser B 2002; 64 (4): 583-639.
17. Spiegelhalter DJ, Thomas A, Best NG, WinBUGS User Manual. Cambridge: Medical Research Council Biostatistics Unit, 2003.
18. Celeux G, Forbes F, Robert CP, et al. Deviance information criteria for missing data models. Bayesian Anal 2006; 1 (4): 651-673.
19. Zhao L, Hanson TE, Carlin BP,. Mixtures of Polya trees for flexible spatial frailty survival modelling. Biometrika 2009; 96 (2): 263-276.
20. Xu L, Hanson T, Bedrick E, et al. Hypothesis tests on mixture model components with applications in ecology and agriculture. J Agric Biol Environ Stat 2010; 15: 308-326.
21. Geisser S, Eddy WF,. A predictive approach to model selection. J Am Stat Assoc 1979; 74 (365): 153-160.
22. Gelfand AE, Mallick BK,. Bayesian analysis of proportional hazards models built from monotone functions. Biometrics 1995; 51 (3): 843-852.
23. Sinha D, Dey DK,. Semiparametric Bayesian analysis of survival data. J Am Stat Assoc 1997; 92 (439): 1195-1212.
24. Brooks SP, Gelman A,. General methods for monitoring convergence of iterative simulations. Comput Stat Data Anal 1998; 7 (4): 434-455.
25. Richardson S, Green PJ,. On Bayesian analysis of mixtures with an unknown number of components. J Royal Stat Soc, Ser B 1997; 59 (4): 731-792.
26. Lunn DJ, Best N, Whittaker JC,. Generic reversible jump MCMC using graphical models. Stat Comput 2009; 19 (4): 395-408.
27. Chen T, Morris J, Martin E,. Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring. J Royal Stat Soc, Ser C 2006; 55 (5): 699-715.
28. Rasmussen CE, Solla SA, Leen TK, Miller KR,. The infinite Gaussian mixture model. Advances in neural information processing systems 12. Cambridge, MA: MIT Press, 2000, 554-560.
29. Blackwell D, MacQueen JB,. Ferguson distributions via Pólya urn schemes. Ann Statist 1973; 1: 353-355.
30. Ferguson TS,. A Bayesian analysis of some nonparametric problems. Ann Statist 1973; 1: 209-230.
31. Antoniak CE,. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Ann Statist 1974; 2: 1152-1174.
32. Christensen R, Johnson W,. Modelling accelerated failure time with a Dirichlet process. Biometrika 1988; 75 (4): 693-704.
33. Kuo L, Mallick B,. Bayesian semiparametric inference for the accelerated failure-time model. Canad J Statist 1997; 25 (4): 457-472.