USF-LVHN SELECT
Multidimensional reduction of multicentric cohort heterogeneity: An alternative method to increase statistical power and robustness.
Publication/Presentation Date
11-1-2016
Abstract
Modern clinical research takes advantage of multicentric cohorts to increase sample size and gain in statistical power. However, combining individuals from different recruitment centers provides heterogeneity in the dataset that needs to be accounted for to obtain robust results. Sophisticated statistical multivariate models adjusting for center effect can be implemented, but they can become unstable and can be complex to interpret with the increasing number of covariates to consider. Here, we present a multidimensional reduction technique to identify heterogeneity in a French multicentric cohort of hematopoietic stem cell transplantations and characterize a homogeneous subgroup prior to performing simple statistical univariate analyses. The exclusion of outliers allowed the identification of two genetic factors associated with post-transplantation overall survival. We therefore provide proof-of-concept that a sample size reduction method can efficiently account for heterogeneity and center effect in multicentric cohorts while increasing statistical power and robustness for discovery of new association signals.
Volume
77
Issue
11
First Page
1024
Last Page
1029
ISSN
1879-1166
Published In/Presented At
Le Gall, C., Laurent, J., Vince, N., Lizee, A., Agrawal, A., Walencik, A., Rettman, P., Gagne, K., Retiere, C., Hollenbach, J., Cesbron, A., Limou, S., & Gourraud, P. A. (2016). Multidimensional reduction of multicentric cohort heterogeneity: An alternative method to increase statistical power and robustness. Human immunology, 77(11), 1024–1029. https://doi.org/10.1016/j.humimm.2016.05.013
Disciplines
Medical Education | Medicine and Health Sciences
PubMedID
27262455
Department(s)
USF-LVHN SELECT Program, USF-LVHN SELECT Program Students
Document Type
Article