Reproducible Code and Data Repository: Design-Aware Predictive and Causal Modeling of Cardiovascular Risk in Chronic Kidney Disease Using Penalized and Double Machine Learning Approaches

Description This repository contains the data and code necessary to reproduce the main results of the study. The paper proposes a unified framework integrating survey-weighted penalized prediction (LASSO), Double/Debiased Machine Learning (DML), Two-Stage Residual Inclusion (2SRI), and design-consistent bootstrap inference. Repository Structure /data ckd_survey_sim.csv simulation_metrics.csv metrics_all_sim.csv stability_sel_prob.csv/code SimulacionGeneral.R Empirical Study.R/docs README.docx Requirements R version 4.3 or higher with the following packages: survey, glmnet, dplyr, tidyr, ggplot2, haven, purrr, gridExtra Reproducibility Guide Step 1 – Simulation: source('code/SimulacionGeneral.R') Step 2 – Empirical analysis: source('code/Empirical Study.R') Data Availability Simulated datasets are included. The ENS 2016–2017 dataset is not redistributed and must be obtained from official sources (MINSAL). Methodological Notes Survey design uses the survey package. Penalized models use glmnet. DML uses cross-fitting at PSU level. 2SRI addresses endogeneity. Metrics include weighted AUC and Brier score. Disclaimer Simulated data are synthetic. ENS variable names may vary. Adjustments may be required. Contact Fernando Rojasfernando.rojas@uv.cl License Creative Commons Attribution (CC BY)