Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models

Biometrics

Volumn 71, Issue 3, 2015, Pages 654-665

  Wang, Chi ^a,b   Dominici, Francesca ^c   Parmigiani, Giovanni ^c,d   Zigler, Corwin Matthew ^c  

^a UNIVERSITY OF KENTUCKY   (United States)

^b UNIVERSITY OF KENTUCKY   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^d DANA FARBER CANCER INSTITUTE   (United States)

Author keywords

Average causal effect; Bayesian adjustment for confounding; Confounder selection; Treatment effect heterogeneity

Indexed keywords

BAYESIAN NETWORKS; BIOMETRICS; HEALTH INSURANCE; UNCERTAINTY ANALYSIS;

AVERAGE CAUSAL EFFECT; BAYESIAN; BAYESIAN ADJUSTMENT FOR CONFOUNDING; CONFOUNDER; CONFOUNDER SELECTION; COVARIATES; GENERALIZED LINEAR MODEL; TREATMENT EFFECT HETEROGENEITY; TREATMENT EFFECTS; UNCERTAINTY;

DIAGNOSIS;

BAYESIAN ANALYSIS; BIOMETRY; BOOTSTRAPPING; COVARIANCE ANALYSIS; HETEROGENEITY; LINEARITY; MODEL TEST; NERVOUS SYSTEM DISORDER; TUMOR; UNCERTAINTY ANALYSIS;

UNITED STATES;

ARTIFACT; BAYES THEOREM; CAUSALITY; COMPUTER SIMULATION; EPIDEMIOLOGY; HUMAN; OUTCOME ASSESSMENT; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ARTIFACTS; BAYES THEOREM; CAUSALITY; COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; HUMANS; LINEAR MODELS; OUTCOME ASSESSMENT (HEALTH CARE); REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84941748969     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12315     Document Type: Article

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