Evidence synthesis through a degradation model applied to myocardial infarction

Lifetime Data Analysis

Volumn 19, Issue 1, 2012, Pages 1-18

  Commenges, Daniel ^a,b   Hejblum, Boris P ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ DE BORDEAUX   (France)

Author keywords

Causal inference; Causality; Coronary heart disease; Degradation model; Epidemiology; Evidence synthesis; Inverse Gaussian distribution; Myocardial infarction; Stochastic processes

Indexed keywords

ARTICLE; BIOLOGICAL MODEL; CORONARY ARTERY DISEASE; HEART INFARCTION; HUMAN; INCIDENCE; LIFE TABLE; MALE; RISK FACTOR; STATISTICAL MODEL;

CORONARY DISEASE; HUMANS; INCIDENCE; LIFE TABLES; MALE; MODELS, CARDIOVASCULAR; MODELS, STATISTICAL; MYOCARDIAL INFARCTION; RISK FACTORS;

EID: 84865168950     PISSN: 13807870     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10985-012-9227-3     Document Type: Article

References (34)

1. Aalen O, Borgan Ø, Gjessing H (2008) Survival and event history analysis: a process point of view. Springer, New York.
2. Aalen OO, Gjessing HkK (2001) Understanding the shape of the hazard rate: a process point of view. Stat Sci 16(1): 1-22.
3. Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc 26(2): 211-252.
4. Clayton D, Schifflers E (1987) Models for temporal variation in cancer rates. i: age-period and age-cohort models. Stat Med 6(4): 449-467.
5. Commenges D (2012) The stochastic system approach to causality with a view toward lifecourse epidemiology. Arxiv preprint arXiv: 12035728, 26 March 2012.
6. Commenges D, Gégout-Petit A (2009) A general dynamical statistical model with causal interpretation. J R Stat Soc 71(3): 719-736.
7. Doksum K, Normand S (1995) Gaussian models for degradation processes-part I: methods for the analysis of biomarker data. Lifetime Data Anal 1(2): 131-144.
8. Flint AJ, Hu FB, Glynn RJ, Caspard H, Manson JE, Willett WC, Rimm EB (2010) Excess weight and the risk of incident coronary heart disease among men and women. Obesity 18(2): 377-383.
9. Fosen J, Ferkingstad E, Borgan Ø, Aalen O (2006) Dynamic path analysis: a new approach to analyzing time-dependent covariates. Lifetime Data Anal 12(2): 143-167.
10. Fox J, Weisberg S (2010) An R Companion to applied regression, 2nd edn. Sage Publications, London.
11. Freedman D (2006) On the so-called huber sandwich estimator and robust standard errors. Am Stat 60(4): 299-302.
12. Gamborg M, Jensen G, Sørensen T, Andersen P (2011) Dynamic path analysis in life-course epidemiology. Am J Epidemiol 173(10): 1131.
13. Hansson G (2005) Inflammation, atherosclerosis, and coronary artery disease. New Engl J Med 352(16): 1685-1695.
14. Hashemi R, Jacqmin-Gadda H, Commenges D (2003) A latent process model for joint modeling of events and marker. Lifetime Data Anal 9(4): 331-343.
15. Jiang W, Turnbull B (2004) The indirect method: inference based on intermediate statistics: a synthesis and examples. Stat Sci 19(2): 239-263.
16. Kannel WB, Wilson PWF, Nam BH, D'Agostino RB (2002) Risk stratification of obesity as a coronary risk factor. Am J Cardiol 90(7): 697-701.
17. Kowalski C (1970) The performance of some rough tests for bivariate normality before and after coordinate transformations to normality. Technometrics 12: 517-544.
18. Lee MLT, Whitmore GA (2006) Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Stat Sci 21(4): 501-513.
19. Liang K, Zeger S (1986) Longitudinal data analysis using generalized linear models. Biometrika 73(1): 13.
20. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2): 431-441.
21. National Heart Lung and Blood Institute (2006) Incidence and prevalence: 2006 chart book on cardiovascular and lung diseases. National Heart, Lung, and Blood Institute, Bethesda.
22. Nicholls SJ (2009) Relationship between LDL, HDL, blood pressure and atheroma progression in the coronaries. Curr Opin Lipidol 20(6): 491-496.
23. Pennell M, Whitmore G, Lee M (2010) Bayesian random-effects threshold regression with application to survival data with nonproportional hazards. Biostatistics 11(1): 111-126.
24. Presanis AM, De Angelis D, Goubar A, Gill ON, Ades AE (2011) Bayesian evidence synthesis for a transmission dynamic model for HIV among men who have sex with men. Biostatistics 12(4): 666-681.
25. Raftery A, Givens G, Zeh J (1995) Inference from a deterministic population dynamics model for bowhead whales. J Am Stat Assoc 90: 402-416.
26. Ridker PM, Cushman M, Stampfer MJ, Tracy RP, Hennekens CH (1997) Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. New Engl J Med 336(14): 973-979.
27. Sæbø S, Almøy T, Aastveit A (2005) Disease resistance modelled as first-passage times of genetically dependent stochastic processes. J R Stat Soc 54(1): 273-285.
28. Schweder T, Hjort N (1996) Bayesian synthesis or likelihood synthesis-what does borel's paradox say?. Rep Int Whal Comm 46: 475-480.
29. van der Vaart A (2000) Asymptotic statistics, 3rd edn. Cambridge University Press, Cambridge.
30. van Houwelingen H, Arends L, Stijnen T (2004) Advanced methods in meta-analysis: multivariate approach and meta-regression. Tutor Biostat 2: 289.
31. Velilla S (1993) A note on the multivariate Box-Cox transformation to normality. Stat Probab Lett 17(4): 259-263.
32. Whitmore GA (1986) Normal-gamma mixtures of inverse Gaussian distributions. Scand J Stat 13(3): 211-220.
33. Wilson PWF, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97(18): 1837-1847.
34. Yeo IK, Johnson RA (2000) A new family of power transformations to improve normality or symmetry. Biometrika 87(4): 954-959.