This repository contains code to reproduce the results presented in the paper

Charu V, Liang JW, Mannalithara A, Kwong A, Tian, Lu, and Kim, RW. Benchmarking clinical risk prediction algorithms with ensemble machine learning: An illustration of the superlearner algorithm for the non-invasive diagnosis of liver fibrosis in non-alcoholic fatty liver disease. 2024.

• Data wrangling and visualization: tidyverse, gridExtra, gtable, reshape2, forcats, and cowplot
• Superlearners: Superlearner
• Performance metrics and survey weighting: cvAUC, pROC, survey, DescTools, and WeightedROC

• Nonalcoholic Steatohepatitis Clinical Research Network Cohort¹ (NASH-CRN; training set)
• Farnesoid X Receptor (FXR) Ligand Obeticholic Acid in NASH Treatment trial² (FLINT; testing set 1)
• National Health and Nutrition Examination Survey NAFLD Cohort³ (NHANES-NAFLD; testing set 2)

1. Harrison SA, Oliver D, Arnold HL, Gogia S, Neuschwander-Tetri BA. Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease. Gut. 2008;57(10):1441-1447. doi:10.1136/gut.2007.146019

2. Neuschwander-Tetri BA, Loomba R, Sanyal AJ, et al. Farnesoid X nuclear receptor ligand obeticholic acid for non-cirrhotic, non-alcoholic steatohepatitis (FLINT): a multicentre, randomised, placebo-controlled trial. Lancet. 2015;385(9972):956-965.

3. National Center for Health Statistics. About the National Health and Nutrition Examination Survey. Accessed October 17, 2021. https://www.cdc.gov/nchs/nhanes/about_nhanes.htm