On the choice of parameterisation and priors for the Bayesian analyses of Mendelian randomisation studies

Statistics in Medicine

Volumn 31, Issue 14, 2012, Pages 1483-1501

  Jones, E M ^a   Thompson, J R ^a   Didelez, V ^b   Sheehan, N A ^a  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Bayesian analyses; Identifiability; Mendelian randomisation

Indexed keywords

ARTICLE; BAYES THEOREM; MENDELIAN RANDOMIZATION ANALYSIS; MONTE CARLO METHOD; PARAMETERIZATION; PROBABILITY; SIMULATION; STATISTICAL CONCEPTS; STATISTICAL DISTRIBUTION;

ADULT; BAYES THEOREM; CHILD; COMPUTER SIMULATION; HUMANS; LONGITUDINAL STUDIES; LUNG; MENDELIAN RANDOMIZATION ANALYSIS; MODELS, STATISTICAL; OBESITY; RANDOMIZED CONTROLLED TRIALS AS TOPIC; SAMPLE SIZE;

EID: 84863411310     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4499     Document Type: Article

References (30)

1. Dawid AP. Conditional independence in statistical models. Journal of the Royal Statistical Society: Series B 1979 41(1): 1-31.
2. Davey -Smith G, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology 2003 32(1): 1-22.
3. Didelez V, Sheehan NA. Mendelian randomization as an instrumental variable approach to causal inference. Statistics in Medicine 2007 16: 309-330.
4. Didelez V, Sheehan NA.Mendelian randomisation: why epidemiology needs a formal language for causality. In Causality and Probability in the Sciences, Russo F, Williamson J (eds), College Publications, London, 2007.
5. Lawlor D, Harbord R, Sterne JAC, Timpson N, Davey -Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine 2008 27: 1133-1163.
6. Bound J, Jaeger DA, Baker RM. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association 1995 90: 443-450.
7. Didelez V, Meng S, Sheehan NA. Assumptions of IV methods for observational epidemiology. Statistical Science 2010 25: 22-40.
8. Wald A. The fitting of straight lines if both variables are subject to error. Annals of Mathematical Statistics 1940 11(3): 284-300.
9. Poirer D J. Revising beliefs in nonidentified models. Econometric Theory 1998 14: 83-509.
10. Gustafson P. On model expansion, model contraction, identifiability and prior information: two illustrative scenarios involving mismeasured variables. Statistical Science 2005 20(2): 111-140.
11. Gustafson P. What are the limits of posterior distributions arising from nonidentified models, and why should we care? American Statistical Association 2009 104(488): 1682-1695.
12. Gelfand AE, Sahu SK. Identifiability, improper priors, and Gibbs sampling for generalized linear models. American Statistical Association 1999 94(445): 247-253.
13. Greenland S. Multiple-bias modelling for analysis of observational data. Journal of the Royal Statistical Society: Series A 2005 168(2): 267-306.
14. Gustafson P. Bayesian inference for partially identified models. The International Journal of Biostatistics 2010 6(2). Article 17.
15. Burgess S, Thompson SG. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables. Statistics in Medicine 2010 29(12): 1298-1311.
16. Pearl J. Causality, Cambridge University Press: Cambridge, 2000.
17. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologists dream? Epidemiology 2006 17(4): 360-372.
18. Lancaster T. Bayesian Econometrics Modelling, Blackwell publishing: MA, USA, 2004.
19. Pearl J. An Introduction to causal inference. The International Journal of Biostatistics 2010 6(2). Article 7.
20. Didelez V, Sheehan NA. Commentary: can 'many weak' instruments ever be 'strong'? International Journal of Epidemiology 2011 40(3): 752-754.
21. Thyagarajan B, Jacobs DR, Apostol GG, Smith LJ, Jensen RL, Crapo RO, Barr RG, Lewis CE, Williams OD. Longitudinal association of body mass index with lung function: the CARDIA Study. Respiratory Research 2008 9: 31.
22. Golding J. Children of the nineties. A longitudinal study of pregnancy and childhood based on the population of Avon (ALSPAC). West of England Medical Journal 1990 105(3): 80-2.
23. Frayling TM, Timpson NJ, Weedon MN, Zeggini E. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 2007 316: 889-894.
24. Timpson NJ, Harbord RM, Davey- Smith G, Zacho J, et al. Does greater adiposity increase blood pressure and hypertension risk? Mendelian randomization using the FTO/MC4R genotype. Hypertension 2009 54: 84-90.
25. Loos RJF, Lindgren CM, Li S, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature Genetics letters 2008 40(6): 768-775.
26. Gustafson P. Measurement error modelling with an approximate instrumental variable. Journal of the Royal Statistical Society: Series B 2007 69(5): 797-815.
27. Gustafson P, Greenland S. Interval estimation for messy observational data. Statistical Science 2009 24(3): 328-342.
28. Greenland S. Relaxation penalties and priors for plausible modeling of nonidentified bias sources. Statistical Science 2009 24(2): 195-210.
29. McKeigue PM et al. Bayesian methods for instrumental variable analysis with genetic instruments ('Mendelian randomisation'): example with urate transporter SLC2A9 as an instrumental variable for effect of urate levels on metabolic syndrome. International Journal of Epidemiology 2010 39(3): 907-918.
30. Goetghebeur E. Commentary: to cause or not to cause confusion vs transparency with Mendelian randomization. International Journal of Epidemiology 2010 39(3): 918-920.