Causal diagrams for encoding and evaluation of information bias. Export

@article{citeulike:4402288, abstract = {BACKGROUND: Epidemiologists and clinical researchers usually classify bias into three main categories: confounding, selection bias and information bias. Previous authors have described the first two categories in the logic and notation of causal diagrams, formally known as directed acyclic graphs (DAG). METHODS: I examine common types of information bias--disease-related and exposure-related--from the perspective of causal diagrams. RESULTS: Disease or exposure information bias always involves the use of an effect of the variable of interest - specifically, an effect of true disease status or an effect of true exposure status. The bias typically arises from a causal or an associational path of no interest to the researchers. In certain situations, it may be possible to prevent or remove some of the bias. CONCLUSIONS: Common types of information bias, just like confounding and selection bias, have a clear and helpful representation within the framework of causal diagrams.}, author = {Shahar, Eyal}, citeulike-article-id = {4402288}, citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1365-2753.2008.01031.x}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19366394}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19366394}, doi = {10.1111/j.1365-2753.2008.01031.x}, issn = {1365-2753}, journal = {Journal of evaluation in clinical practice}, keywords = {bias, dag}, month = {June}, number = {3}, pages = {436--440}, posted-at = {2009-04-25 03:44:08}, priority = {2}, title = {Causal diagrams for encoding and evaluation of information bias.}, url = {http://dx.doi.org/10.1111/j.1365-2753.2008.01031.x}, volume = {15}, year = {2009} }

TY - JOUR ID - citeulike:4402288 L3 - citeulike-article-id:4402288 N2 - BACKGROUND: Epidemiologists and clinical researchers usually classify bias into three main categories: confounding, selection bias and information bias. Previous authors have described the first two categories in the logic and notation of causal diagrams, formally known as directed acyclic graphs (DAG). METHODS: I examine common types of information bias--disease-related and exposure-related--from the perspective of causal diagrams. RESULTS: Disease or exposure information bias always involves the use of an effect of the variable of interest - specifically, an effect of true disease status or an effect of true exposure status. The bias typically arises from a causal or an associational path of no interest to the researchers. In certain situations, it may be possible to prevent or remove some of the bias. CONCLUSIONS: Common types of information bias, just like confounding and selection bias, have a clear and helpful representation within the framework of causal diagrams. IS - 3 JF - Journal of evaluation in clinical practice SN - 1365-2753 EP - 440 TI - Causal diagrams for encoding and evaluation of information bias. VL - 15 SP - 436 KW - bias KW - dag AU - Shahar, Eyal PY - 2009/06/01/ UR - http://dx.doi.org/10.1111/j.1365-2753.2008.01031.x ER -