Bias attenuation results for dichotomization of a continuous confounder
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Bias attenuation results for dichotomization of a continuous confounder. / Gabriel, Erin E.; Peña, Jose M.; Sjölander, Arvid.
I: Journal of Causal Inference, Bind 10, Nr. 1, 2022, s. 515-526.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Bias attenuation results for dichotomization of a continuous confounder
AU - Gabriel, Erin E.
AU - Peña, Jose M.
AU - Sjölander, Arvid
N1 - Publisher Copyright: © 2022 the author(s), published by De Gruyter.
PY - 2022
Y1 - 2022
N2 - It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation. There are additional examples in the literature where adjusting for a dichotomized confounder can be more biased than not adjusting at all. The message is clear, do not dichotomize. What is unclear is if there are scenarios where adjusting for the dichotomized confounder always leads to lower bias than not adjusting. We propose several sets of conditions that characterize scenarios where one should always adjust for the dichotomized confounder to reduce bias. We then highlight scenarios where the decision to adjust should be made more cautiously. To our knowledge, this is the first formal presentation of conditions that give information about when one should and potentially should not adjust for a dichotomized confounder.
AB - It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation. There are additional examples in the literature where adjusting for a dichotomized confounder can be more biased than not adjusting at all. The message is clear, do not dichotomize. What is unclear is if there are scenarios where adjusting for the dichotomized confounder always leads to lower bias than not adjusting. We propose several sets of conditions that characterize scenarios where one should always adjust for the dichotomized confounder to reduce bias. We then highlight scenarios where the decision to adjust should be made more cautiously. To our knowledge, this is the first formal presentation of conditions that give information about when one should and potentially should not adjust for a dichotomized confounder.
KW - bias
KW - causal inference
KW - dichotomized confounder
U2 - 10.1515/jci-2022-0047
DO - 10.1515/jci-2022-0047
M3 - Journal article
AN - SCOPUS:85147151109
VL - 10
SP - 515
EP - 526
JO - Journal of Causal Inference
JF - Journal of Causal Inference
SN - 2193-3677
IS - 1
ER -
ID: 336824343