Estimation of direct causal effects

Epidemiology

Volumn 17, Issue 3, 2006, Pages 276-284

  Petersen, Maya L ^a   Sinisi, Sandra E ^a   Van Der Laan, Mark J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIRETROVIRUS AGENT; PROTEINASE INHIBITOR; VIRUS RNA;

AIR POLLUTANT; AIR POLLUTION; ARTICLE; CD4 LYMPHOCYTE COUNT; CLINICAL RESEARCH; ENVIRONMENTAL EXPOSURE; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; PRIORITY JOURNAL; STATISTICAL MODEL; VIRUS LOAD;

CLINICAL TRIALS; EFFECT MODIFIERS (EPIDEMIOLOGY); EPIDEMIOLOGIC STUDIES; HUMANS; MODELS, STATISTICAL; RESEARCH DESIGN; UNITED STATES;

EID: 33646779523     PISSN: 10443983     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.ede.0000208475.99429.2d     Document Type: Article

References (13)

1. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37-81.
2. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3:143-155.
3. Pearl J. Direct and indirect effects. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann; 2001:411-420.
4. Robins JM. Semantics of causal DAG models and the identification of direct and indirect effects. In: Hjort N, Green P, Richardson S, eds. Highly Structured Stochastic Systems. Oxford: Oxford University Press; 2003:70-81.
5. Cole SR, Hernan MA. Fallibility in estimating direct effects. Epidemiology. 2002;31:163-165.
6. Poole C, Kaufman JS. What does standard adjustment for down-stream mediators tell us about social effect pathways. Am J Epidemiol. 2000;151:s52.
7. Joffe MM, Colditz GA. Restriction as a method for reducing bias in the estimation of direct effects. Stat Med. 1998;17:2233-2249.
8. Kaufman S, Kaufman JS, Maclehose RF, et al. Improved estimation of controlled direct effects in the presence of unmeasured confounding of intermediate variables. Stat Med. 2005;24:1683-1702.
9. Rubin DB. Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics. 2004;31:161-170.
10. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550-560.
11. van der Laan MJ, Petersen ML. Estimation of direct and indirect causal effects in longitudinal studies. Technical report, University of California, Berkeley, Division of Biostatistics. August 23, 2004. Available at: http://www.bepress.com/ucbbiostat/paper155.
12. Phenix B, Angel J, Mandy F, et al. Decreased HIV-associated T-cell apoptosis by HIV protease inhibitors. AIDS Res Hum Retroviruses. 2000;16:559-567.
13. Muthumani K, Choo A, Hwang D, et al. Mechanism of HIV-1 viral protein R-induced apoptosis. Biochem Biophys Res Commun. 2003;304:583-592.