Confounders, Mediators, or Colliders: What Types of Shared Covariates Does a Sibling Comparison Design Control For?

Epidemiology

Volumn 28, Issue 4, 2017, Pages 540-547

  Sjölander, Arvid ^a   Zetterqvist, Johan ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLINICAL STUDY; COVARIANCE; DIRECTED ACYCLIC GRAPH; ENVIRONMENTAL FACTOR; HEREDITY; PRIORITY JOURNAL; SIBLING COMPARISON DESIGN; STATISTICAL ANALYSIS; COMPARATIVE STUDY; EPIDEMIOLOGY; FEMALE; HUMAN; MALE; SIBLING; STATISTICAL BIAS; STATISTICAL MODEL;

BIAS; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; FEMALE; HUMANS; LIKELIHOOD FUNCTIONS; MALE; SIBLINGS;

EID: 85020756438     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0000000000000649     Document Type: Article

References (23)

1. Susser E, Eide MG, Begg M. Invited commentary: the use of sibship studies to detect familial confounding. Am J Epidemiol. 2010;172:537-539.
2. Donovan SJ, Susser E. Commentary: advent of sibling designs. Int J Epidemiol. 2011;40:345-349.
3. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratifcation bias. Epidemiology. 2003;14:300-306.
4. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615-625.
5. Cole SR, Platt RW, Schisterman EF, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010;39:417-420.
6. Robins JM, Greenland S. Identifability and exchangeability for direct and indirect effects. Epidemiology. 1992;3:143-155.
7. Pearl, J. Direct and indirect effects. Proceedings of the seventeenth conference on uncertainty in artifcial intelligence. Morgan Kaufmann Publishers Inc.; 2010: 411-420.
8. Cole SR, Hernán MA. Fallibility in estimating direct effects. Int J Epidemiol. 2002;31:163-165.
9. Chang Z, Lichtenstein P, D'Onofrio BM, et al. Maternal age at childbirth and risk for ADHD in offspring: a population-based cohort study. Int J Epidemiol. 2014;43:1815-1824.
10. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37-48.
11. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educat Psychol. 1974;66:688-701.
12. Robins JM. A new approach to causal inference in mortality studies with a sustained exposure period - application to control of the healthy worker survivor effect. Math Modell. 1986;7:1393-1512.
13. Andersen, E. Asymptotic properties of conditional maximum-likelihood estimators. J R Stat Soc Series B (Methodol). 1970;32:283-301.
14. Zetterqvist J, Vansteelandt S, Pawitan Y, Sjölander A. Doubly robust methods for handling confounding by cluster. Biostatistics. 2016;17:264-276.
15. Allison PD. Fixed Effects Regression Models. Los Angeles: SAGE Publications; 2009: 17-18.
16. Kalbfeisch J, Sprott D. Application of likelihood methods to models involving large numbers of parameters. J R Stat Soc Series B (Methodol). 1970;32:175-208.
17. Lancaster T. The incidental parameter problem since 1948. J Econom. 2000;95:391-413.
18. Seaman S, Pavlou M, Copas A. Review of methods for handling confounding by cluster and informative cluster size in clustered data. Stat Med. 2014;33:5371-5387.
19. Neuhaus J, McCulloch C. Separating between-and within-cluster covari-ate effects by using conditional and partitioning methods. J R Stat Soc Series B (Stat Methodol). 2006;68:859-872.
20. Sjölander A, Lichtenstein P, Larsson H, Pawitan Y. Between-within models for survival analysis. Stat Med. 2013;32:3067-3076.
21. Breslow N, Day N. Statistical Methods in Cancer Research. Vol. 1. The Analysis of Case-Control Studies. Number 32. Lyon: IARC/WHO; 1980.
22. Holt J, Prentice R. Survival analyses in twin studies and matched pair experiments. Biometrika. 1974;61:17-30.
23. Greenland S, Robins JM, Pearl J. Confounding and collapsibility in causal inference. Stat Sci. 1999;14:29-46.