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One intuitive method of addressing this challenge assigns different weights to different features, subsequently combining this information into a single score, named the compound covariate. Investigators commonly employ this score to assess whether an association exists between the compound covariate and clinical outcomes adjusted for baseline covariates. However, we found that some clinical papers concerned with such analysis report bias p-values based on flawed compound covariate in their training data set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We correct this flaw in the analysis and we also propose treating the compound score as a random covariate, to achieve more appropriate results and significantly improve study power for survival outcomes. With this proposed method, we thoroughly assess the performance of two commonly used estimated gene weights through simulation studies. When the sample size is 100, and censoring rates are 50%, 30%, and 10%, power is increased by 10.6%, 3.5%, and 0.4%, respectively, by treating the compound score as a random covariate rather than a fixed covariate. Finally, we assess our proposed method using two publicly available microarray data sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In this article, we correct this flaw in the analysis and the propose method, treating the compound score as a random covariate, can achieve more appropriate results and improve study power for survival outcomes.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1752-0509-6-s3-s11","type":"journal-article","created":{"date-parts":[[2013,6,24]],"date-time":"2013-06-24T14:19:20Z","timestamp":1372083560000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Statistical aspects of omics data analysis using the random compound covariate"],"prefix":"10.1186","volume":"6","author":[{"given":"Pei-Fang","family":"Su","sequence":"first","affiliation":[]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Heidi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Shyr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,12,17]]},"reference":[{"key":"996_CR1","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/415530a","volume":"415","author":"LJ van't Veer","year":"2002","unstructured":"van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend S: Gene expression profiling predicts clinical outcome of breast cancer. 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