Graphical models for inference under outcome-dependent sampling

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Graphical models for inference under outcome-dependent sampling. / Didelez, V; Kreiner, S; Keiding, N.

I: Statistical Science, Bind 25, Nr. 3, 2010, s. 368-387.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Didelez, V, Kreiner, S & Keiding, N 2010, 'Graphical models for inference under outcome-dependent sampling', Statistical Science, bind 25, nr. 3, s. 368-387. https://doi.org/10.1214/10-STS340

APA

Didelez, V., Kreiner, S., & Keiding, N. (2010). Graphical models for inference under outcome-dependent sampling. Statistical Science, 25(3), 368-387. https://doi.org/10.1214/10-STS340

Vancouver

Didelez V, Kreiner S, Keiding N. Graphical models for inference under outcome-dependent sampling. Statistical Science. 2010;25(3):368-387. https://doi.org/10.1214/10-STS340

Author

Didelez, V ; Kreiner, S ; Keiding, N. / Graphical models for inference under outcome-dependent sampling. I: Statistical Science. 2010 ; Bind 25, Nr. 3. s. 368-387.

Bibtex

@article{9c37330031894780b87682db1475aebc,
title = "Graphical models for inference under outcome-dependent sampling",
abstract = "We consider situations where data have been collected such thatthe sampling depends on the outcome of interest and possibly further covariates,as for instance in case-control studies. Graphical models representassumptions about the conditional independencies among the variables. Byincluding a node for the sampling indicator, assumptions about samplingprocesses can be made explicit. We demonstrate how to read off such graphswhether consistent estimation of the association between exposure and outcomeis possible. Moreover, we give sufficient graphical conditions for testingand estimating the causal effect of exposure on outcome. The practicaluse is illustrated with a number of examples.",
author = "V Didelez and S Kreiner and N Keiding",
year = "2010",
doi = "10.1214/10-STS340",
language = "English",
volume = "25",
pages = "368--387",
journal = "Statistical Science",
issn = "0883-4237",
publisher = "Institute of Mathematical Statistics",
number = "3",

}

RIS

TY - JOUR

T1 - Graphical models for inference under outcome-dependent sampling

AU - Didelez, V

AU - Kreiner, S

AU - Keiding, N

PY - 2010

Y1 - 2010

N2 - We consider situations where data have been collected such thatthe sampling depends on the outcome of interest and possibly further covariates,as for instance in case-control studies. Graphical models representassumptions about the conditional independencies among the variables. Byincluding a node for the sampling indicator, assumptions about samplingprocesses can be made explicit. We demonstrate how to read off such graphswhether consistent estimation of the association between exposure and outcomeis possible. Moreover, we give sufficient graphical conditions for testingand estimating the causal effect of exposure on outcome. The practicaluse is illustrated with a number of examples.

AB - We consider situations where data have been collected such thatthe sampling depends on the outcome of interest and possibly further covariates,as for instance in case-control studies. Graphical models representassumptions about the conditional independencies among the variables. Byincluding a node for the sampling indicator, assumptions about samplingprocesses can be made explicit. We demonstrate how to read off such graphswhether consistent estimation of the association between exposure and outcomeis possible. Moreover, we give sufficient graphical conditions for testingand estimating the causal effect of exposure on outcome. The practicaluse is illustrated with a number of examples.

U2 - 10.1214/10-STS340

DO - 10.1214/10-STS340

M3 - Journal article

VL - 25

SP - 368

EP - 387

JO - Statistical Science

JF - Statistical Science

SN - 0883-4237

IS - 3

ER -

ID: 33244176