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Titolo:
Assessing the sensitivity of regression results to unmeasured confounders in observational studies
Autore:
Lin, DY; Psaty, BM; Kronmal, RA;
Indirizzi:
Univ Washington, Dept Biostat, Seattle, WA 98195 USA Univ Washington Seattle WA USA 98195 Dept Biostat, Seattle, WA 98195 USA Univ Washington, Dept Med, Seattle, WA 98195 USA Univ Washington Seattle WA USA 98195 ton, Dept Med, Seattle, WA 98195 USA Univ Washington, Dept Epidemiol, Seattle, WA 98195 USA Univ Washington Seattle WA USA 98195 ept Epidemiol, Seattle, WA 98195 USA
Titolo Testata:
BIOMETRICS
fascicolo: 3, volume: 54, anno: 1998,
pagine: 948 - 963
SICI:
0006-341X(199809)54:3<948:ATSORR>2.0.ZU;2-Z
Fonte:
ISI
Lingua:
ENG
Soggetto:
CENSORED-DATA; RANK-TESTS; MODELS; SCORE;
Keywords:
case-central studies; causal inference; cohort studies; covariate adjustment; Cox regression; logistic regression; model misspecification; sensitivity analysis;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Life Sciences
Citazioni:
22
Recensione:
Indirizzi per estratti:
Indirizzo: Lin, DY Univ Washington, Dept Biostat, Box 357232, Seattle, WA 98195 USA Univ Washington Box 357232 Seattle WA USA 98195 ttle, WA 98195 USA
Citazione:
D.Y. Lin et al., "Assessing the sensitivity of regression results to unmeasured confounders in observational studies", BIOMETRICS, 54(3), 1998, pp. 948-963

Abstract

This paper presents a general approach for assessing the sensitivity of the point and interval estimates of the primary exposure effect in an observational study to the residual confounding effects of unmeasured variables after adjusting for measured covariates. The proposed method assumes that thetrue exposure effect can be represented in a regression model that includes the exposure indicator as well as the measured and unmeasured confounders. One can use the corresponding reduced model that omits the unmeasured confounder to make statistical inferences about the true exposure effect by specifying the distributions of the unmeasured confounder in the exposed and unexposed groups along with the effects of the unmeasured confounder on theoutcome variable. Under certain conditions, there exists a simple algebraic relationship between the true exposure effect in the full model and the apparent exposure effect in the reduced model. One can then estimate the true exposure effect by making a simple adjustment to the point and interval estimates of the apparent exposure effect obtained from standard software orpublished reports. The proposed method handles both binary response and censored survival time data, accommodates any study design, and allows the unmeasured confounder to be discrete or normally distributed. We describe applications to two major medical studies.

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Documento generato il 01/12/20 alle ore 10:24:13