Model-based estimation of the link between the daily survival probability and a time-varying covariate, application to mosquitofish survival data

Mathematical Biosciences

Volumn 210, Issue 2, 2007, Pages 508-522

  Soubeyrand, Samuel ^a   Beaudouin, Rémy ^b   Desassis, Nicolas ^a   Monod, Gilles ^b  

^a INRA   (France)

^b CEMAGREF   (France)

Author keywords

Bayesian estimation; Generalized non linear mixed model; Non stationary Markov process; Survival analysis; Time period censoring

Indexed keywords

BIODIVERSITY; DATA STRUCTURES; MARKOV PROCESSES; PROBABILITY; TIME VARYING NETWORKS;

BAYESIAN ESTIMATION; GENERALIZED NON-LINEAR MIXED MODEL; NON-STATIONARY MARKOV PROCESS; SURVIVAL ANALYSIS; TIME-PERIOD CENSORING;

PARAMETER ESTIMATION;

CANNIBALISM; COVARIANCE ANALYSIS; ECOLOGICAL MODELING; ENVIRONMENTAL STRESS; ESTIMATION METHOD; PREDATION; SURVIVAL; TELEOST;

ALGORITHM; ARTICLE; BAYES THEOREM; CANNIBALISM; ENVIRONMENTAL STRESS; FISH; GAMBUSHIA HOLBROOKI; MESOCOSM; NONHUMAN; PREDATION; PROBABILITY; SURVIVAL RATE;

ANIMALS; CYPRINODONTIFORMES; ECOSYSTEM; MARKOV CHAINS; MODELS, BIOLOGICAL; NONLINEAR DYNAMICS; POPULATION DYNAMICS; SURVIVAL ANALYSIS;

GAMBUSIA AFFINIS; GAMBUSIA HOLBROOKI;

EID: 36148997619     PISSN: 00255564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mbs.2007.06.005     Document Type: Article

References (29)

1. Botsford L.W., Vondracek B., Wainwright T.C., Linden A.L., Kope R.G., Reed D.E., and Cech Jr. J.J. Population development of the mosquitofish, Gambusia affinis, in rice fields. Environ. Biol. Fish. 20 (1987) 143
2. Box-Steffensmeier J.M., and Jones B.S. Event History Modeling, A Guide for Social Scientists. Cambridge University (2004)
3. Brooks S.P. Discussion on the paper by spiegelhalter, best, carlin and van der linde. J. R. Stat. Soc. B 64 (2002) 616
4. Brown C.R., and Bomberger Brown M. Group size and ectoparasitism affect daily survival probability in a colonial bird. Behav. Ecol. Sociobiol. 56 (2004) 498
5. Cabral J.A., Marques J.C., and Nielsen S.N. Modeling mosquitofish (Gambusia holbrooki) response to genapol 0xd-080, a non ionic surfactant, in rice fields. Ecol. Eng. 16 (2001) 537
6. Chalita L.V.A., Colosimo E.A., and de Souza Passos J.R. Modeling grouped survival data with time-dependent covariates. Commun. Stat.: Simul. Comput. 35 (2006) 975
7. Davidian M., and Giltinan D.M. Nonlinear Models for Repeated Measurement Data (1995), Chapman & Hall, London
8. Dinsmore S.J., White G.C., and Knopf F.L. Adv anced techniques for modeling avian nest survival. Ecology 83 (2002) 3476
9. Doyle C.J., and Lim R.P. Sexual behavior and impregnation success of adult male mosquitofish following exposure to 17 beta-estradiol. Ecotoxicol. Environ. Safety 61 (2005) 392
10. Drèze V., Monod G., Cravedi J.P., Biagianti-Risbourg S., and Le Gac F. Effects of 4-nonylphenol on sex differentiation and puberty in mosquitofish (Gambusia holbrooki). Ecotoxicology 9 (2000) 93
11. Efron B., and Tibshirani R.J. An Introduction to the Bootstrap (1993), Chapman & Hall, New York
12. Ginot V., Gaba S., Beaudouin R., Aries F., and Monod H. Combined use of local and anova-based global sensitivity analyses for the investigation of a stochastic dynamic model: application to the case study of an individual-based model of a fish population. Ecol. Model. 193 (2006) 479
13. Kooperberg C., and Clarkson D.B. Hazard regression with interval-censored data. Biometrics 53 (1997) 1485
14. McCulloch C.E., and Searle S.R. Generalized, Linear and Mixed Models (2001), Wiley, New York
15. Mulvey M., Newman M.C., Chazal A., and Keklak M.M. Genetic and demographic responses of mosquitofish (Gambusia holbrooki girard 1859) populations stressed by mercury. Environ. Toxicol. Chem. 14 (1995) 1411
16. Robert C.P., and Casella G. Monte Carlo Statistical Methods (1999), Springer, New York
17. Rotella J.J., Dinsmore S.J., and Shaffer T.L. Modeling nest-survival data: a comparison of recently developed methods that can be implemented in mark and sas. Animal Biodiv. Conserv. 27 (2004) 187
18. Ruppert D., Wand M.P., and Carroll R.J. Semiparametric regression (2003), Cambridge University
19. Schwartz C.C., Haroldson M.A., and White G.C. Survival of cub and yearling grizzly bears in the Greater Yellowstone Ecosystem, 1983-2001. Wildlife Monogr. 161 (2006) 25
20. Shaffer T.L. A unified approach to analyzing nest success. The Auk 121 (2004) 526
21. Sparling Y.H., Younes N., Lachin J.M., and Bautista O.M. Parameteric survival models for interval-censored data with time-dependent covariates. Biostatistics 7 (2006) 599
22. Spiegelhalter D.J., Best N.G., Carlin B.P., and van der Linde A. Bayesian measures of model complexity and fit (with discussion). J. R. Stat. Soc. B 64 (2002) 583
23. S.E. Stephens, The influence of landscape characteristics on duck nesting success in the Missouri Coteau Region of North Dakota. Ph.D. thesis, Montana State University, 2003.
24. Toft G., Edwards T.M., Baatrup E., and Guillette L.J. Disturbed sexual characteristics in male mosquitofish (Gambusia holbrooki) from a lake contaminated with endocrine disruptors. Environ. Health Persp. 111 (2003) 695
25. Vonesh E.F., and Chinchilli V.M. Linear and Nonlinear Models for the Analysis of Repeated Measurements (1997), Dekker, New York
26. Wainwright T.C., Kope R.G., Botsford L.W., and Cech Jr. J.J. Implications of laboratory mosquitofish experiments for population development in rice fields. Proceedings of the California Mosquito and Vector Control Association 52 (1984) 110
27. Wei G.C.G., and Tanner M.A. A monte carlo implementation of the em algorithm and the poor man's data augmentation algorithms. J. Am. Stat. Assoc. 85 (1990) 699
28. Yip P.S.F., Lam K.F., Lau E.H.Y., Chau P.-H., and Tsang K.W. A comparison study of realtime fatality rates: severe acute respiratory syndrome in hong kong, singapore, taiwan, toronto and beijing, china. J. R. Stat. Soc. A 168 (2005) 233
29. Yip P.S.F., Lau E.H.Y., Lam K.F., and Huggins R.M. A chain multinomial model for estimating the real-time fatality rate of a disease, with an application to severe acute respiratory syndrome. American Journal of Epidemiology 161 (2005) 700