Missing data methods in mendelian randomization studies with multiple instruments

American Journal of Epidemiology

Volumn 174, Issue 9, 2011, Pages 1069-1076

  Burgess, Stephen ^a   Seaman, Shaun ^a   Lawlor, Debbie A ^b   Casas, Juan P ^c   Thompson, Simon G ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

^c LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

Author keywords

Bayesian methods; causal inference; imputation; instrumental variables; Mendelian randomization analysis; missing data

Indexed keywords

C REACTIVE PROTEIN; FIBRINOGEN;

BAYESIAN ANALYSIS; DATA SET; INSTRUMENTATION; NUMERICAL MODEL; POLYMORPHISM; PROTEIN;

ACCURACY; ARTICLE; BAYES THEOREM; CONTROLLED STUDY; HAPLOTYPE; ISCHEMIC HEART DISEASE; MENDELIAN RANDOMIZATION ANALYSIS; SAMPLE SIZE; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; STRUCTURAL EQUATION MODELING;

BAYES THEOREM; C-REACTIVE PROTEIN; CORONARY DISEASE; FEMALE; FIBRINOGEN; GENETIC ASSOCIATION STUDIES; HAPLOTYPES; HUMANS; MENDELIAN RANDOMIZATION ANALYSIS; MODELS, GENETIC; MULTIVARIATE ANALYSIS;

EID: 80054932205     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwr235     Document Type: Article

References (32)

1. Davey Smith G, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1-22. (Pubitemid 36503465)
2. Davey Smith G, Ebrahim S. What can Mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ. 2005;330(7499):1076- 1079. (Pubitemid 40646565)
3. Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133-1163. (Pubitemid 351395448)
4. Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16(4):309-330. (Pubitemid 47215256)
5. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722-729. (Pubitemid 30612241)
6. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology. 2006;17(4):360-372. (Pubitemid 44305508)
7. Davey Smith G, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004; 33(1):30-42. (Pubitemid 38528650)
8. Verduijn M, Siegerink B, Jager KJ, et al. Mendelian randomization: use of genetics to enable causal inference in observational studies. Nephrol Dial Transplant. 2010;25(5): 1394-1398.
9. Lawlor DA, Harbord RM, Timpson NJ, et al. The association of C-reactive protein and CRP genotype with coronary heart disease: findings from five studies with 4,610 cases amongst 18,637 participants [electronic article]. PLoS One. 2008;3(8):e3011. (doi:10.1371/journal.pone.0003011).
10. Smith GD. Randomised by (your) god: robust inference from an observational study design. J Epidemiol Community Health. 2006;60(5):382-388.
11. Pierce BL, Ahsan H, VanderWeele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40(3): 740-752.
12. Burgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755-764.
13. Palmer TM, Lawlor DA, Harbord RM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors [published online ahead of print January 7, 2011]. Stat Methods Med Res. (doi:10.1177/0962280210394459).
14. Little R, Rubin D. Statistical Analysis With Missing Data. 2nd ed. New York, NY: Wiley; 2002.
15. Staiger D, Stock J. Instrumental variables regression with weak instruments. Econometrica. 1997;65(3):557-586. (Pubitemid 127466159)
16. Stock J, Wright J, Yogo M. A survey of weak instruments and weak identification in generalized method of moments. J Bus Econ Stat. 2002;20(4):518-529.
17. Zacho J, Tybjaerg-Hansen A, Jensen JS, et al. Genetically elevated C-reactive protein and ischemic vascular disease. N Engl J Med. 2008;359(18):1897-1908.
18. C Reactive Protein Coronary Heart Disease Genetics Collaboration (CCGC). Association between C reactive protein and coronary heart disease: Mendelian randomisation analysis based on individual participant data [electronic article]. BMJ. 2011;342:d548. (doi:10.1136/bmj.d548).
19. Burgess S, Thompson SG. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables. Stat Med. 2010;29(12):1298-1311.
20. Stock JH, Yogo M. Testing for weak instruments in linear IV regression. In: NBER Working Paper Series. Vol t0284. Cambridge, MA: National Bureau of Economic Research, Inc; 2002.
21. Palmer TM, Thompson JR, Tobin MD, et al. Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses. Int J Epidemiol. 2008;37(5): 1161-1168.
22. Spiegelhalter D, Thomas A, Best N, et al. WinBUGS Version 1.4 User Manual. Cambridge, United Kingdom: MRC Biostatistics Unit; 2003.
23. Gelman A, Rubin D. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7(4):457-472.
24. Browning SR. Multilocus association mapping using variable-length Markov chains. Am J Hum Genet. 2006; 78(6):903-913. (Pubitemid 43787683)
25. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81(5):1084-1097. (Pubitemid 47580259)
26. Lunn D, Best N, Spiegelhalter D, et al. CombiningMCMC with 'sequential' PKPD modelling. J Pharmacokinet Pharmacodyn. 2009;36(1):19-38.
27. Meng X. Multiple-imputation inferences with uncongenial sources of input. Stat Sci. 1994;9(4):538-558.
28. Lunn DJ, Whittaker JC, Best NA. Bayesian toolkit for genetic association studies. Genet Epidemiol. 2006;30(3):231-247.
29. CRP CHD Genetics Collaboration. Collaborative pooled analysis of data on C-reactive protein gene variants and coronary disease: judging causality byMendelian randomisation. Eur J Epidemiol. 2008;23(8):531-540.
30. Fu W, Wang Y, Wang Y, et al. Missing call bias in highthroughput genotyping [electronic article]. BMC Genomics. 2009;10:106. (doi:10.1186/1471- 2164-10-106).
31. Scheet P, Stephens M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet. 2006;78(4):629-644.
32. Burgess S. WinBUGS Code for Imputation of Missing Data in Mendelian Randomization Studies. Technical report 2011/1. Cambridge, United Kingdom: MRC Biostatistics Unit; 2011.