15_coverletter.Rmd

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title: "Cover Letter"
output: pdf_document
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Dear Editor,   

I am pleased to submit our manuscript titled "People-like-Me Methods with Mahalanobis distance: A Personalized Prediction of Longitudinal Growth in Children" for consideration of publication in Biometrics (Practice) Journal. All co-authors agree with this submission and have no conflicts of interest to report. 
 
In this paper, we employed a new personalized prediction method call People-Like-Me (PLM) with nonlinear longitudinal data. PLM consists of using  curves that are similar to the particular individual we wish to predict for to predict a future trajectory for the target individual. Matches are traditionally found by calculating Euclidean distances between the target trajectory and the potential matches at a single time point. In this paper, we build from previous work on PLM to i) use Mahalanobis distance as an alternative to the traditionally used Euclidean distance for calculating matches; and ii) compare the predictions from PLM using Euclidean and Mahalanobis distances to predictions derived from a linear mixed model (LMM).  We believe that our manuscript aligns with the mission of Biometrics Practice.
 
Thank you for considering our manuscript for publication.



Sincerely,
Elizabeth Juarez-Colunga
Associate Professor  
Department of Biostatistics and Informatics, Colorado School of Public Health 
Associate Director of Informatics, VA Eastern Colorado Geriatric Research Education and Clinical Center 
University of Colorado Anschutz Medical Campus