TY - JOUR
T1 - Avoiding Systematic Bias in Orthopedics Research Through Informed Variable Selection
T2 - A Discussion of Confounders, Mediators, and Colliders
AU - Devick, Katrina L.
AU - Zaniletti, Isabella
AU - Larson, Dirk R.
AU - Lewallen, David G.
AU - Berry, Daniel J.
AU - Maradit Kremers, Hilal
N1 - Funding Information:
Funding: This work was funded by a grant from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grant P30AR76312 and the American Joint Replacement Research-Collaborative (AJRR-C). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Funding Information:
Funding: This work was funded by a grant from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grant P30AR76312 and the American Joint Replacement Research-Collaborative (AJRR-C). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/10
Y1 - 2022/10
N2 - There are 3 common variable types in orthopedic research—confounders, colliders, and mediators. All 3 types of variables are associated with both the exposure (eg, surgery type, implant type, body mass index) and outcome (eg, complications, revision surgery) but differ in their temporal ordering. To reduce systematic bias, the decision to include or exclude a variable in an analysis should be based on the variable's relationship with the exposure and outcome for each research question. In this article, we define 3 types of variables with case examples from orthopedic research. Please visit the following https://youtu.be/V-grpgB1ShQ for videos that explain the highlights of the article in practical terms.
AB - There are 3 common variable types in orthopedic research—confounders, colliders, and mediators. All 3 types of variables are associated with both the exposure (eg, surgery type, implant type, body mass index) and outcome (eg, complications, revision surgery) but differ in their temporal ordering. To reduce systematic bias, the decision to include or exclude a variable in an analysis should be based on the variable's relationship with the exposure and outcome for each research question. In this article, we define 3 types of variables with case examples from orthopedic research. Please visit the following https://youtu.be/V-grpgB1ShQ for videos that explain the highlights of the article in practical terms.
KW - confounding
KW - databases
KW - directed acyclic graph
KW - mediation analysis
KW - selection bias
KW - total joint arthroplasty
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U2 - 10.1016/j.arth.2022.05.027
DO - 10.1016/j.arth.2022.05.027
M3 - Article
C2 - 36162928
AN - SCOPUS:85138421175
SN - 0883-5403
VL - 37
SP - 1951
EP - 1955
JO - Journal of Arthroplasty
JF - Journal of Arthroplasty
IS - 10
ER -