Power and sample size calculations for mendelian randomization studies using one genetic instrument

International Journal of Epidemiology

Volumn 42, Issue 4, 2013, Pages 1157-1163

  Freeman, Guy ^a   Cowling, Benjamin J ^a   Mary Schooling, C ^a,b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

Mendelian randomization; Power; Sample size

Indexed keywords

GENETIC VARIATION; HERITABILITY; SAMPLING; THEORETICAL STUDY;

ARTICLE; GENETIC VARIABILITY; MENDELIAN RANDOMIZATION ANALYSIS; POWER ANALYSIS; PRIORITY JOURNAL; SAMPLE SIZE; SIMULATION;

MENDELIAN RANDOMIZATION; POWER; SAMPLE SIZE;

CAUSALITY; GENETIC VARIATION; GENOTYPE; HUMANS; MATHEMATICS; MENDELIAN RANDOMIZATION ANALYSIS; RESEARCH DESIGN; SAMPLE SIZE;

EID: 84884793216     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dyt110     Document Type: Article

References (24)

1. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc 1996;91:444-55.
2. Katan MB. Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet 1986;327:507-08.
3. Davey Smith G, Ebrahim S. 'Mendelian randomization': Can genetic epidemiology contribute to understanding environmental determinants of disease?. Int J Epidemiol 2003;32:1-22.
4. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med 2007;27:1133-63.
5. Wehby GL, Ohsfeldt RL, Murray JC. 'Mendelian randomization' equals instrumental variable analysis with genetic instruments. Stat Med 2008;27:2745-49.
6. Schooling CM, Freeman G, Cowling BJ. Mendelian randomization and estimation of treatment efficacy for chronic diseases. Am J Epidemiol 2013;177:1128-33.
7. Freiman JA, Chalmers TC, Smith H, Kuebler RR. The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. N Engl J Med 1978;299:690-94.
8. Pierce BL, Ahsan H, VanderWeele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol 2011;40:740-52.
9. Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 2007;16:309-30.
10. Didelez V, Meng S, Sheehan NA. Assumptions of IV methods for observational epidemiology. Stat Sci 2010; 25:22-40.
11. Wooldridge JM. Introductory Econometrics: A Modern Approach. 4th edn. Boston, MA: South-Western College Publishing, 2008.
12. Nelson CR, Startz R. The distribution of the instrumental variables estimator and its t-ratio when the instrument is a poor one. J Bus 1990;63:S125-40.
13. Martens EP, Pestman WR, Boer A de, Belitser SV, Klungel OH. Instrumental variables. Epidemiology 2006; 17:260-67.
14. 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. BMJ 2011;342:d548.
15. Keavney B, Danesh J, Parish S et al. Fibrinogen and coronary heart disease: test of causality by 'Mendelian randomization'. Int J Epidemiol 2006;35: 935-43.
16. Burgess S, Thompson SG. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables. Stat Med 2010;29: 1298-311.
17. Burgess S, Thompson SG. CRP CHD Genetics Collaboration. Methods for meta-analysis of individual participant data from Mendelian randomisation studies with binary outcomes. Stat Methods Med Res 2012; doi:10.1177/0962280212451882.
18. Glymour MM, Tchetgen EJ, Robins JM. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am J Epidemiol 2012;175:332-39.
19. Nelson CR, Startz R. Some further results on the exact small sample properties of the instrumental variable estimator. Econometrica 1990;58:967-76.
20. Bound J, Jaeger DA, Baker RM. Problems with instrumental variables estimation when the correlation between the instruments and the endogeneous explanatory variable is weak. J Am Stat Assoc 1995;90: 443-50.
21. Sheehan NA, Didelez V. Commentary: Can 'many weak' instruments ever be 'strong'?. Int J Epidemiol 2011;40: 752-54.
22. Palmer TM, Lawlor DA, Harbord RM et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res 2012;21: 223-42.
23. Burgess S, Thompson SG. Use of allele scores as instrumental variables for Mendelian randomization. 2012. http://www.phpc.cam.ac.uk/ceu/files/2012/11/scoreone 140612-1.pdf (27 May 2013, date last accessed).
24. DasGupta A. Probability for Statistics and Machine Learning. New York: Springer, 2011.