Sensitivity analysis of unmeasured confounding for the causal risk ratio by applying marginal structural models

Communications in Statistics - Theory and Methods

Volumn 39, Issue 1, 2010, Pages 65-76

  Chiba, Yasutaka ^a  

^a KINKI UNIVERSITY SCHOOL OF MEDICINE   (Japan)

Author keywords

Cohort study; Potential outcome; Propensity score; Repeated measure; Time dependent confounding

Indexed keywords

COHORT STUDIES; POTENTIAL OUTCOMES; PROPENSITY SCORE; REPEATED MEASURES; TIME-DEPENDENT;

MODEL STRUCTURES;

SENSITIVITY ANALYSIS;

EID: 77951171579     PISSN: 03610926     EISSN: 1532415X     Source Type: Journal    
DOI: 10.1080/03610920802677224     Document Type: Article

References (24)

1. Arah, O. A., Chiba, Y., Greenland, S. (2008). Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders. Ann. Epidemiol. 18:637-646.
2. Berk, R. (2003). Regression Analysis: A Constructive Critique. Thousand Oaks, CA: Sage Publications.
3. Breslow, N. E., Day, N. E. (1987). Statistical Methods in Cancer Research. Vol II: The Design and Analysis of Cohort Studies. New York: Oxford University Press.
4. Brumback, B. A., Hernán, M. A., Haneuse, S. J. P. A., Robins, J. M. (2004). Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures. Statist. Med. 23:749-767.
5. Copas, J. B. (1973). Randomization models for matched and unmatched 2 ×2 tables. Biometrika 60:267-276.
6. Flanders, W. D., Khoury, M. J. (1990). Indirect assessment of confounding: graphic description and limits on effect of adjusting for covariates. Epidemiology 1:239-246.
7. Greenland, S. (2003). The impact of prior distributions for uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia. J. Amer. Statist. Assoc. 98:47-54.
8. Greenland, S. (2005). Multiple-bias modeling for analysis of observational data (with discussion). J. Roy. Statist. Soc. Ser. A 168:267-308.
9. Hernán, M. A., Brumback, B., Robins, J. M. (2000). Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11:561-570.
10. Hernán, M. A., Brumback, B., Robins, J. M. (2002). Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures. Statist. Med. 21:1689-1709.
11. Jewell, N. P. (2003). Statistics for Epidemiology. Boca Raton, FL: Chapman and Hall/CRC press.
12. Joffe, M. M., Rosenbaum, P. R. (1999). Propensity scores. Amer. J. Epidemiol. 150:327-333.
13. Kitagawa, E. M. (1955). Components of a difference between two rates. J. Amer. Statist. Assoc. 50:1168-1194.
14. Ko, H., Hogan, J. W., Mayer, K. H. (2003). Estimating causal treatment effects from longitudinal HIV natural history studies using marginal structural models. Biometrics 59:152-162.
15. Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.
16. Robins, J. M. (1999). Association, causation, and marginal structural models. Synthese 121:151-179.
17. Robins, J. M., Rotnitzky, A. Scharfstein, D. O. (1999). Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In: Halloran, M. E., Berry, D., eds. Statistical Models in Epidemiology, the Environment and Clinical Trials. New York: Springer-Verlag, 1-94.
18. Rosenbaum, P. R., Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70:41-55.
19. Rosenman, R. R., Brand, R. J., Jenkins, D., Friedman, M., Straus, R., Wurm, M. (1975). Coronary heart disease in the Western Collaborative Group Study: final follow-up experience of 8 1/2 years. J. Amer. Med. Assoc. 233:872-877.
20. Rothman, K. J., Greenland, S. (1998). Modern Epidemiology. 2nd ed. Philadelphia: Lippincott.
21. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66:688-701.
22. SAS Institute. (1999). SAS/STAT® User's Guide: Version 8. Volume 2. Cary, NC: SAS Institute Inc.
23. Steenland, K., Greenland, S. (2004). Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer. Amer. J. Epidemiol. 160:384-392.
24. Yanagawa, T. (1984). Case-control studies: assessing the effect of a confounding factor. Biometrika 71:191-194.