TY - GEN
T1 - Selection Bias from Data Processing in N3C
AU - N3C Consortium
AU - Haghighathoseini, Atefehsadat
AU - Qodrati, Mohammad
AU - Min, Hua
AU - Leslie, Timothy
AU - Frankenfeld, Cara
AU - Menon, Nirup M.
AU - Wojtusiak, Janusz
AU - Wilcox, Adam B.
AU - Lee, Adam M.
AU - Graves, Alexis
AU - Anzalone, Alfred
AU - Manna, Amin
AU - Saha, Amit
AU - Olex, Amy
AU - Zhou, Andrea
AU - Williams, Andrew E.
AU - Southerland, Andrew
AU - Girvin, Andrew T.
AU - Walden, Anita
AU - Sharathkumar, Anjali A.
AU - Amor, Benjamin
AU - Bates, Benjamin
AU - Hendricks, Brian
AU - Patel, Brijesh
AU - Alexander, Caleb
AU - Bramante, Carolyn
AU - Ward-Caviness, Cavin
AU - Madlock-Brown, Charisse
AU - Suver, Christine
AU - Chute, Christopher
AU - Dillon, Christopher
AU - Wu, Chunlei
AU - Schmitt, Clare
AU - Takemoto, Cliff
AU - Housman, Dan
AU - Gabriel, Davera
AU - Eichmann, David A.
AU - Mazzotti, Diego
AU - Brown, Don
AU - Boudreau, Eilis
AU - Hill, Elaine
AU - Zampino, Elizabeth
AU - Marti, Emily Carlson
AU - Pfaff, Emily R.
AU - French, Evan
AU - Koraishy, Farrukh M.
AU - Mariona, Federico
AU - Prior, Fred
AU - Sokos, George
AU - Subbian, Vignesh
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study investigates potential selection bias in outcome prediction within the National COVID Cohort Collaborative (N3C) resulting from arbitrarily made decisions. In the processing of health data, decisions regarding cohort criteria and variable selection are often arbitrarily made, potentially introducing selection bias. This work explores if such decisions affect results of data analysis and potential conclusions of research studies. An experiment is conducted in which four arbitrary decisions are made. Results demonstrate significant differences in the obtained datasets and indicate a high potential for bias based on inclusion or exclusion decisions. The findings contribute to informed healthcare policies, better decision-making, and improved patient outcomes, emphasizing the necessity for testing assumptions and decisions in ongoing research that uses clinical data.
AB - This study investigates potential selection bias in outcome prediction within the National COVID Cohort Collaborative (N3C) resulting from arbitrarily made decisions. In the processing of health data, decisions regarding cohort criteria and variable selection are often arbitrarily made, potentially introducing selection bias. This work explores if such decisions affect results of data analysis and potential conclusions of research studies. An experiment is conducted in which four arbitrary decisions are made. Results demonstrate significant differences in the obtained datasets and indicate a high potential for bias based on inclusion or exclusion decisions. The findings contribute to informed healthcare policies, better decision-making, and improved patient outcomes, emphasizing the necessity for testing assumptions and decisions in ongoing research that uses clinical data.
KW - Data Processing
KW - National COVID Cohort Collaborative (N3C)
KW - Prediction
KW - Selection Bias
UR - http://www.scopus.com/inward/record.url?scp=85203704221&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85203704221&partnerID=8YFLogxK
U2 - 10.1109/ICHI61247.2024.00038
DO - 10.1109/ICHI61247.2024.00038
M3 - Conference contribution
AN - SCOPUS:85203704221
T3 - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
SP - 234
EP - 241
BT - Proceedings - 2024 IEEE 12th International Conference on Healthcare Informatics, ICHI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Healthcare Informatics, ICHI 2024
Y2 - 3 June 2024 through 6 June 2024
ER -