Analysis of the relationship between longitudinal gene expressions and ordered categorical event data

Natasa Rajicic, Dianne M. Finkelstein, David A. Schoenfeld, Henry V. Baker, Ulysses Balis, Paul Bankey, Timothy R. Billiar, Bernard H. Brownstein, Steven E. Calvano, David G. Camp, George Casella, Irshad H. Chaudry, J. Perren Cobb, Joseph Cuschieri, Ronald W. Davis, Asit K. De, Celeste C. Finnerty, Bradley Freeman, Richard L. Gamelli, Nicole S. GibranBrian G. Harbrecht, Douglas L. Hayden, Laura Hennessy, David N. Herndon, Jureta W. Horton, Marc G. Jeschke, Jeffrey Johnson, Matthew B. Klein, James A. Lederer, Stephen F. Lowry, Ronald V. Maier, John A. Mannick, Philip H. Mason, Grace P. McDonald-Smith, Carol L. Miller-Graziano, Michael N. Mindrinos, Joseph P. Minei, Lyle L. Moldawer, Ernest E. Moore, Avery B. Nathens, Grant E. O'Keefe, Laurence G. Rahme, Daniel G. Remick, Michael B. Shapiro, Geoffrey M. Silver, Richard D. Smith, John Storey, Robert Tibshirani, Ronald G. Tompkins, Mehmet Toner, H. Shaw Warren, Michael A. West, Wenzhong Xiao

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The NIH project 'Inflammatory and Host Response to Injury' (Glue) is being conducted to study the changes in the body over time in response to trauma and burn. Patients are monitored for changes in their clinical status, such as the onset of and recovery from organ failure. Blood samples are drawn over the first days and weeks after the injury to obtain gene expression levels over time. Our goal was to develop a method of selecting genes that differentially expressed in patients who either improved or experienced organ failure. For this, we needed a test for the association between longitudinal gene expressions and the time to the occurrence of ordered categorical outcomes indicating recovery, stable disease, and organ failure. We propose a test for which the relationship between the gene expression and the events is modeled using the cumulative proportional odds model that is a generalization of the pooling repeated observation method. Given the high-dimensionality of the microarray data, it was necessary to control for the multiplicity of the testing. To control for the false discovery rate (FDR), we applied both a permutational approach as well as Efron's empirical estimation method. We explore our method through simulations and provide the analysis of the multi-center, longitudinal study of immune response to inflammation and trauma (

Original languageEnglish
Pages (from-to)2817-2832
Number of pages16
JournalStatistics in Medicine
Issue number22
StatePublished - 30 Sep 2009


  • Microarray analysis
  • Multiple testing
  • Ordinal data
  • Permutation test
  • Score test


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