Estimating peer effects in longitudinal dyadic data using instrumental variables

Biometrics

Volumn 70, Issue 3, 2014, Pages 506-515

  O'Malley, A James ^a   Elwert, Felix ^b   Rosenquist, J Niels ^c   Zaslavsky, Alan M ^d   Christakis, Nicholas A ^e  

^a DARTMOUTH INSTITUTE FOR HEALTH POLICY AND CLINICAL PRACTICE   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

^e YALE UNIVERSITY   (United States)

Author keywords

Body mass index; Causality; Directed acyclic graphs; Dyad; Genes; Homophily; Instrumental variable; Longitudinal; Mendelian randomization; Peer effect; Social network; Two stage least squares

Indexed keywords

BIOMETRICS; DIRECTED GRAPHS; ECONOMIC AND SOCIAL EFFECTS;

ACYCLIC GRAPHS; BODY MASS; BODY-MASS INDEX; CAUSALITY; DIRECTED ACYCLIC GRAPH; DYAD; HOMOPHILY; INSTRUMENTAL VARIABLES; LONGITUDINAL; MASS INDEX; MENDELIAN RANDOMIZATION; PEER EFFECT; RANDOMISATION; SOCIAL NETWORK; TWO STAGE LEAST SQUARES;

GENE EXPRESSION;

BODY MASS; BODY SIZE; COMPUTER SIMULATION; FRIEND; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; LONGITUDINAL STUDY; OBESITY; PROCEDURES; SELECTION BIAS; SOCIAL NETWORK; STATISTICAL BIAS; STATISTICAL MODEL;

BIAS (EPIDEMIOLOGY); BODY MASS INDEX; BODY SIZE; COMPUTER SIMULATION; FRIENDS; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; OBESITY; SELECTION BIAS; SOCIAL NETWORKING;

EID: 84927696504     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12172     Document Type: Article

References (32)

1. Angrist, J., Imbens, G., and Rubin, D. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91, 444-455.
2. Brito, C. (2010). Instrument sets. In Heuristics, Probability and Causality: A Tribute to Judea Pearl, R. Dechter, H. Geffner, and J. Halpern (eds), 295-307. London: College Publications.
3. Brito, C. and Pearl, J. (2002). A new identification condition for recursive models with correlated errors. Structural Equation Modeling 9, 459-474.
4. Carrell, S. E., Fullerton, R. L., and West, J. E. (2009). Does your cohort matter? Estimating peer effects in college achievement. Journal of Labor Economics 27, 439-464.
5. Centola, D. (2010). The spread of behavior in an online social network. Science 329, 1194-1197.
6. Christakis, N. A. and Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine 357, 370-379.
7. Christakis, N. A. and Fowler, J. H. (2008). Dynamics of smoking behavior in a large social network. New England Journal of Medicine 358, 2249-2258.
8. Didelez, V. and Sheehan, N. (2007). Mendelian randomization as an instrumental variable approach to causal inference. Statistical Methods for Medical Research 16, 309-330.
9. Didelez, V., Meng, S., and Sheehan, N. A. (2010). Assumptions of IV methods for observational epidemiology. Statistical Science 25, 22-40.
10. Elwert, F. (2013). Graphical causal models. In Handbook of Causal Analysis for Social Research, S. Morgan (ed), 245-273. Dodrecht, Netherlands: Springer.
11. Elwert, F. and Christakis, N. A. (2008). Wives and ex-wives: A new test for homogamy bias in the widowhood effect. Demography 45, 851-873.
12. Fletcher, J. M. (2008). Social interactions and smoking: Evidence using multiple student cohorts, instrumental variables, and school fixed effects. Health Economics 19, 466-484.
13. Fowler, J. H. and Christakis, N. A. (2010). Cooperative behavior cascades in human social networks. PNAS: Proceedings of the National Academy of Sciences 107, 5334-5338.
14. Hernán, M. and Robins, J. (2006). Instruments for causal inference-An epidemiologist's dream? Epidemiology 17, 360-372.
15. Lasky-Su, J., Lyon, H. N., Emilsson, V., Heid, I. M., Molony, C., Raby, B. A., Lazarus, R., Klanderman, B., Soto-Quiros, M. E., Avila, L., Silverman, E. K., Thorleifsson, G., Thorsteinsdottir, U., Kronenberg, F., Vollmert, C., Illig, T., Fox, C. S., Levy, D., Laird, N., Ding, X., McQueen, M. B., Butler, J., Ardlie, K., Papoutsakis, C., Dedoussis, G., O'Donnell, C. J., Wichmann, H. E., Celedón, J. C., Schadt, E., Hirschhorn, J., Weiss, S. T., Stefansson, K., and Lange, C. (2008). On the replication of genetic associations: Timing can be everything. The American Journal of Human Genetics 82, 849-858.
16. O'Malley, A. J. and Christakis, N. A. (2011). Longitudinal analysis of large social networks: Estimating the effect of health traits on changes in friendship ties. Statistics in Medicine 30, 950-964.
17. Palmer, T. M., Lawlor, D. A., Harbord, R. M., Sheehan, N. A., Tobias, J. H., Timpson, N. J., Smith, G. D., and Sterne, J. A. C. (2012). Using multiple genetic variants as instrumental variables for modifiable risk factors. The International Journal of Biostatistics 21, 223-242.
18. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Mateo, CA: Morgan Kaufmann.
19. Pearl, J. (1995). Causal diagrams for empirical research. Biometrika 82, 669-710.
20. Pearl, J. (2009). Causality, 2nd edition. New York: Cambridge University Press.
21. Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., and Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38, 904-909.
22. Richardson, T. S. and Robins, J. M. (2013). Single world intervention graphs (swigs): A unification of the counterfactual and graphical approaches to causality. Working Paper Number 128, Center for Statistics and the Social Sciences, University of Washington, (146 pages), http://www.csss.washington.edu/Papers/wp128.pdf.
23. Rosenquist, J. N., Fowler, J. H., Murabito, J., and Christakis, N. A. (2010). The spread of alcohol consumption behavior in a large social network. Annals of Internal Medicine 152, 426-433.
24. Sacerdote, B. (2001). Peer effects with random assignment: results for Dartmouth roommates. Quarterly Journal of Economics 116, 681-704.
25. Shalizi, C. R. and Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research 40, 211-239.
26. Speliotes, E., Willer, C., Berndt, S., Monda, K., Thorleifsson, G., Jackson, A., et al. (2010). Association analyses of 249, 796 individuals reveal 18 new loci associated with body mass index. Nature Genetics 42, 937-948.
27. Stock, J., Wright, J., and Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economics Statistics 20, 518-527.
28. VanderWeele, T. J., Ogburn, E. L., and Tchetgen Tchetgen, E. J. (2012). Why and when flawed social network analyses still yield valid tests of no contagion. Statistics, Politics, and Policy 3, doi:10.1515/2151-7509.1050.
29. Vansteelandt, S., Bowden, J., Babanezhad, M., and Goetghebeur, E. (2011). On instrumental variables estimation of causal odds ratios. Statistical Science 26, 403-422.
30. Verma, T. and Pearl, J. (1988). Causal networks: Semantics and expressiveness. In Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence, Mountain View, CA: Association for Uncertainty in Artificial Intelligence, 352-359.
31. White, H. (1982). Instrumental variables regression with independent observations. Econometrica 50, 483-499.
32. Wing, R. R. and Jeffery, R. W. (1999). Benefits of recruiting participants with friends and increasing social support for weight loss and maintenance. Journal of Consulting and Clinical Psychology 67, 132-138.