High dimensional variable selection with clustered data: an application of random multivariate survival forests for detection of outlier medical device components. Issue 8 (24th May 2019)

• Record Type: Journal Article
• Authors: Cafri, Guy; Calhoun, Peter; Fan, Juanjuan
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 8(2019)
• Page Start: 1410
• Page End: 1422
• Publication Date: 2019-05-24
• Subjects: Medical devices -- multivariate -- random forest -- stratification -- survival; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1584198
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In many medical studies patients are nested or clustered within doctor. With many explanatory variables, variable selection with clustered data can be challenging. We propose a method for variable selection based on random forest that addresses clustered data through stratified binary splits. Our motivating example involves the detection orthopedic device components from a large pool of candidates, where each patient belongs to a surgeon. Simulations compare the performance of survival forests grown using the stratified logrank statistic to conventional and robust logrank statistics, as well as a method to select variables using a threshold value based on a variable's empirical null distribution. The stratified logrank test performs superior to conventional and robust methods when data are generated to have cluster-specific effects, and when cluster sizes are sufficiently large, perform comparably to the splitting alternatives in the absence of cluster-specific effects. Thresholding was effective at distinguishing between important and unimportant variables.