title: A joint latent class analysis for adjusting survival bias with application to a trauma transfusion study.
abstract: There is no clear classification rule to rapidly identify trauma patients who are severely hemorrhaging and may need substantial blood transfusions. Massive transfusion (MT), defined as the transfusion of at least 10 units of red blood cells within 24 h of hospital admission, has served as a conventional surrogate that has been used to develop early predictive algorithms and establish criteria for ordering an MT protocol from the blood bank. However, the conventional MT rule is a poor proxy, because it is likely to misclassify many severely hemorrhaging trauma patients as they could die before receiving the 10th red blood cells transfusion. In this article, we propose to use a latent class model to obtain a more accurate and complete metric in the presence of early death. Our new approach incorporates baseline patient information from the time of hospital admission, by combining respective models for survival time and usage of blood products transfused within the framework of latent class analysis. To account for statistical challenges, caused by induced dependent censoring inherent in 24-h sums of transfusions, we propose to estimate an improved standard via a pseudo-likelihood function using an expectation-maximization algorithm with the inverse weighting principle. We evaluated the performance of our new standard in simulation studies and compared with the conventional MT definition using actual patient data from the Prospective Observational Multicenter Major Trauma Transfusion study. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX, Houston, U.S.A.
authors.forename: Jing
authors.initials: J
authors.lastname: Ning
authors.affiliation: Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas Medical School at Houston, Houston, TX, U.S.A.|Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Sciences Center at Houston, Houston, TX, U.S.A.
authors.forename: Mohammad H
authors.initials: MH
authors.lastname: Rahbar
authors.affiliation: Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas Medical School at Houston, Houston, TX, U.S.A.
authors.forename: Sangbum
authors.initials: S
authors.lastname: Choi
authors.affiliation: Division of Biostatistics, School of Public Health, The University of Texas Health Sciences Center at Houston, Houston, TX, U.S.A.
authors.forename: Chuan
authors.initials: C
authors.lastname: Hong
authors.affiliation: Division of Biostatistics, School of Public Health, The University of Texas Health Sciences Center at Houston, Houston, TX, U.S.A.
authors.forename: Jin
authors.initials: J
authors.lastname: Piao
authors.affiliation: Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, The University of Texas Health Science Center at Houston, Houston, TX, U.S.A.
authors.forename: Deborah J
authors.initials: DJ
authors.lastname: del Junco
authors.affiliation: Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, The University of Texas Health Science Center at Houston, Houston, TX, U.S.A.
authors.forename: Erin E
authors.initials: EE
authors.lastname: Fox
authors.affiliation: Department of Biomedical Engineering, Wake Forest University, Winston-Salem, NC, U.S.A.
authors.forename: Elaheh
authors.initials: E
authors.lastname: Rahbar
authors.affiliation: Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, The University of Texas Health Science Center at Houston, Houston, TX, U.S.A.
authors.forename: John B
authors.initials: JB
authors.lastname: Holcomb
mesh_terms: D000465:Algorithms; D015982:Bias; D056808:Biostatistics / Q000379:methods; D001803:Blood Transfusion / Q000706:statistics & numerical data*; D003198:Computer Simulation; D006470:Hemorrhage / Q000209:etiology / Q000401:mortality / Q000628:therapy; D006801:Humans; D053208:Kaplan-Meier Estimate; D016013:Likelihood Functions; D016015:Logistic Models; D016019:Survival Analysis; D014947:Wounds and Injuries / Q000150:complications / Q000401:mortality / Q000628:therapy*
keywords: EM algorithm; induced dependent censoring; inverse weighting principle; latent class model; massive transfusion