메뉴 건너뛰기




Volumn 68, Issue 3, 2012, Pages 661-671

Bayesian Effect Estimation Accounting for Adjustment Uncertainty

Author keywords

Adjustment uncertainty; Bayesian model averaging; Exposure effects; Treatment effects

Indexed keywords

AIR POLLUTION; UNCERTAINTY ANALYSIS;

EID: 84866768287     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2011.01731.x     Document Type: Article
Times cited : (84)

References (30)
  • 1
    • 33644851650 scopus 로고    scopus 로고
    • Doubly robust estimation in missing data and causal inference models
    • Bang, H. and Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962-973.
    • (2005) Biometrics , vol.61 , pp. 962-973
    • Bang, H.1    Robins, J.M.2
  • 5
    • 0034541856 scopus 로고    scopus 로고
    • Model uncertainty and health effects studies for particulate matter
    • Clyde, M. (2000). Model uncertainty and health effects studies for particulate matter. Environmetrics 11, 745-763.
    • (2000) Environmetrics , vol.11 , pp. 745-763
    • Clyde, M.1
  • 6
    • 59349092705 scopus 로고    scopus 로고
    • Compatibility of prior specifications across linear models
    • Consonni, G. and Veronese, P. (2008). Compatibility of prior specifications across linear models. Statistical Science 23, 332-353.
    • (2008) Statistical Science , vol.23 , pp. 332-353
    • Consonni, G.1    Veronese, P.2
  • 7
    • 50949134553 scopus 로고    scopus 로고
    • Adjustment uncertainty in effect estimation
    • Crainiceanu, C. M., Dominici, F., and Parmigiani, G. (2008). Adjustment uncertainty in effect estimation. Biometrika 95, 635-651.
    • (2008) Biometrika , vol.95 , pp. 635-651
    • Crainiceanu, C.M.1    Dominici, F.2    Parmigiani, G.3
  • 8
    • 0034354440 scopus 로고    scopus 로고
    • Combining evidence on air pollution and daily mortality from the twenty largest U.S. cities: A hierarchical modeling strategy (with discussion)
    • Dominici, F., Samet, J. M., and Zeger, S. L. (2000). Combining evidence on air pollution and daily mortality from the twenty largest U.S. cities: A hierarchical modeling strategy (with discussion). Journal of the Royal Statistical Society, Series A: Statistics in Society 163, 263-302.
    • (2000) Journal of the Royal Statistical Society, Series A: Statistics in Society , vol.163 , pp. 263-302
    • Dominici, F.1    Samet, J.M.2    Zeger, S.L.3
  • 11
    • 40049100642 scopus 로고    scopus 로고
    • Variable selection versus shrinkage in the control of multiple confounders
    • Greenland, S. (2008). Variable selection versus shrinkage in the control of multiple confounders. American Journal of Epidemiology 167, 523-529.
    • (2008) American Journal of Epidemiology , vol.167 , pp. 523-529
    • Greenland, S.1
  • 12
    • 77953359190 scopus 로고    scopus 로고
    • Model uncertainty and variable selection in bayesian lasso regression
    • Hans, C. (2010). Model uncertainty and variable selection in bayesian lasso regression. Statistics and Computing 20, 221-229.
    • (2010) Statistics and Computing , vol.20 , pp. 221-229
    • Hans, C.1
  • 14
    • 34250780091 scopus 로고    scopus 로고
    • Trends in air pollution and mortality: An approach to the assessment of unmeasured confounding
    • Janes, H., Dominici, F., and Zeger, S. L. (2007). Trends in air pollution and mortality: An approach to the assessment of unmeasured confounding. Epidemiology 18, 416-423.
    • (2007) Epidemiology , vol.18 , pp. 416-423
    • Janes, H.1    Dominici, F.2    Zeger, S.L.3
  • 16
    • 0347566334 scopus 로고    scopus 로고
    • Measuring the health effects of air pollution: To what extent can we really say that people are dying of bad air
    • Koop, G. and Tole, L. (2004). Measuring the health effects of air pollution: To what extent can we really say that people are dying of bad air. Journal of Environmental Economics and Management 47, 30-54.
    • (2004) Journal of Environmental Economics and Management , vol.47 , pp. 30-54
    • Koop, G.1    Tole, L.2
  • 17
    • 21844520724 scopus 로고
    • Bayesian graphical models for discrete data
    • Madigan, D. and York, J. (1995). Bayesian graphical models for discrete data. International Statistical Review 63, 215-232.
    • (1995) International Statistical Review , vol.63 , pp. 215-232
    • Madigan, D.1    York, J.2
  • 18
    • 61749103209 scopus 로고    scopus 로고
    • Bayesian propensity score analysis for observational data
    • McCandless, L. C., Gustafson, P., and Austin, P. C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine 28, 94-112.
    • (2009) Statistics in Medicine , vol.28 , pp. 94-112
    • McCandless, L.C.1    Gustafson, P.2    Austin, P.C.3
  • 19
    • 0024592074 scopus 로고
    • The impact of confounder selection criteria on effect estimation
    • Mickey, R. M. and Greenland, S. (1989). The impact of confounder selection criteria on effect estimation. American Journal of Epidemiology 129, 125-137.
    • (1989) American Journal of Epidemiology , vol.129 , pp. 125-137
    • Mickey, R.M.1    Greenland, S.2
  • 22
    • 0001201909 scopus 로고
    • Bayesian model selection in social research
    • Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology 25, 111-163.
    • (1995) Sociological Methodology , vol.25 , pp. 111-163
    • Raftery, A.E.1
  • 24
    • 0026708512 scopus 로고
    • Estimating exposure effects by modelling the expectation of exposure conditional on confounders
    • Robins, J. M., Mark, S. D., and Newey, W. K. (1992). Estimating exposure effects by modelling the expectation of exposure conditional on confounders. Biometrics 48, 479-495.
    • (1992) Biometrics , vol.48 , pp. 479-495
    • Robins, J.M.1    Mark, S.D.2    Newey, W.K.3
  • 25
    • 77951622706 scopus 로고
    • The central role of the propensity score in observational studies for causal effects
    • Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
    • (1983) Biometrika , vol.70 , pp. 41-55
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 26
    • 0030862072 scopus 로고    scopus 로고
    • Estimating causal effects from large data sets using propensity scores
    • Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine 127, 757-763.
    • (1997) Annals of Internal Medicine , vol.127 , pp. 757-763
    • Rubin, D.B.1
  • 28
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6, 461-464.
    • (1978) The Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 29
    • 0035889492 scopus 로고    scopus 로고
    • Variable selection and Bayesian model averaging in case-control studies
    • Viallefont, V., Raftery, A. E., and Richardson, S. (2001). Variable selection and Bayesian model averaging in case-control studies. Statistics in Medicine 20, 3215-3230.
    • (2001) Statistics in Medicine , vol.20 , pp. 3215-3230
    • Viallefont, V.1    Raftery, A.E.2    Richardson, S.3
  • 30
    • 19544362938 scopus 로고    scopus 로고
    • Bayesian model averaging: Development of an improved multi-class, gene selection and classification tool for microarray data
    • Yeung, K. Y., Bumgarner, R. E., and Raftery, A. E. (2005). Bayesian model averaging: Development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 21, 2394-2402.
    • (2005) Bioinformatics , vol.21 , pp. 2394-2402
    • Yeung, K.Y.1    Bumgarner, R.E.2    Raftery, A.E.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.