On confounding, prediction and efficiency in the analysis of longitudinal and cross-sectional clustered data

Scandinavian Journal of Statistics

Volumn 34, Issue 3, 2007, Pages 478-498

  Vansteelandt, Stijn ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Causal diagrams; Causal inference; Interference between subjects; Inverse weighting; Marginal structural models; Semi parametric efficiency; SUTVA

Indexed keywords

EID: 34547977994     PISSN: 03036898     EISSN: 14679469     Source Type: Journal    
DOI: 10.1111/j.1467-9469.2006.00555.x     Document Type: Article

References (17)

1. Angrist, J. D., Imbens, G. W. Rubin, D. B. (1998). Identification of causal effects using instrumental variables (with discussion). J. Am. Stat. Assoc. 91, 444 455.
2. Bickel, P. J., Klaassen, C. A. J., Ritov, Y. Wellner, J. A. (1993). Efficient and adaptive estimation for semiparametric models. Springer-Verlag, New York.
3. Chamberlain, G. (1992). Comment: sequential moment restrictions in panel data, J. Bus. Econ. Stat. 10, 20 26.
4. Diggle, P. J., Heagerty, P., Liang, K. Y. Zeger, S. L. (2002). The analysis of longitudinal data, 2nd edn. Oxford University Press, Oxford.
5. van der Laan, M. J. Robins, J. M. (2002). Unified methods for censored longitudinal data and causality. Springer-Verlag, New York.
6. Liang, K.-Y. Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13 22.
7. Newey, W. K. (1990). Semiparametric efficiency bounds. J. Appl. Econom. 5, 99 135.
8. Pan, W., Louis, T. A. Connett, J. E. (2000). A note on marginal linear regression with correlated response data. Am. Stat. 54, 191 195.
9. Pearl, J. (1995). Causal diagrams for empirical research (with discussion). Biometrika 82, 669 710.
10. Pearl, J. (2000). Causality: models, reasoning, and inference. Cambridge University Press, Cambridge.
11. Pepe, M. S. Anderson, G. L. (1994). A cautionary note on inference for marginal regression-models with longitudinal data and general correlated response data. Commun. Stat. Simul. Comput. 23, 939 951.
12. Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Math. Model. Theor. Appl. 7, 1393 1512.
13. Robins, J. M. (1998). Marginal structural models. 1997 Proc. Am. Stat. Assoc. Sect. Bayesian Stat. Sci., 1 10.
14. Robins, J. M. (2001). Data, design and background knowledge in etiologic inference. Epidemiology 11, 313 320.
15. Robins, J. M., Hernán, M. A. Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550 560.
16. Rubin, D. B. (1978). Bayesian inference for causal effects: the role of randomization. Ann. Stat. 6, 34 58.
17. Schildcrout, J. S. Heagerty, P. J. (2005). Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency. Biostatistics 6, 633 652.