Teaching

I am an Associate Professor of Biostatistics at the University of Granada (Department of Statistics and Operations Research) and a Distance Learning Module Organizer for the MSc in Epidemiology at the LSHTM.

📖 Book

Computational Causal Inference for Applied Researchers — A comprehensive open-access Quarto book (with Matthew J. Smith). 9 chapters covering potential outcomes, DAGs, regression adjustment, G-formula, propensity scores, AIPW, TMLE, longitudinal data, mediation, and sensitivity analysis. With ~65 reproducible R and Stata code examples and 71 references. Read online · GitHub

Courses

• EPM304 — Advanced Statistical Methods in Epidemiology (LSHTM)
• Short Course: Introduction to Survival Analysis in Cancer Epidemiology — GitHub repo
• Introduction to Causal Inference and the Potential Outcomes Framework — Course site
• Computational Causal Inference and Estimation using Stata (LSHTM) — Tutorial

Tutorials & Open-Access Materials

🎓 Featured: Conformal Statistical Inference

Comprehensive tutorials from theory to practice — marginal coverage guarantees, split conformal prediction (SCP), conformal quantile regression (CQR), full conformal, and Jackknife+.

• Comprehensive Guide (EN) — Interactive notebook covering theory and implementation
• Tutorial Slides (ES) — Structured lecture slides in Spanish (NEW)

Causal Inference & TMLE

• TMLE in practice with R — Step-by-step TMLE implementation with influence functions, fluctuation steps, and variance estimation
• Applied Computational Causal Inference using Stata — Doubly robust methods, AIPW, and TMLE in Stata using the eltmle command
• Las matemáticas detrás de TMLE (ES) — Mathematical derivation of TMLE in Spanish: influence functions, semiparametric theory
• Delta Method Tutorial — Variance estimation using the delta method for epidemiological estimands (R notebook)
• ELTMLE: One Simulation — Stata simulation comparing eltmle vs competitors (2017 SUGM)

Survival Analysis

• Introduction to Time-to-Event Analysis — Kaplan-Meier, log-rank tests, and Cox models (ULB workshop)
• Net Survival: Cohort Analysis — Standardization of net survival under the relative survival framework (Stata)
• Net Survival: STRS & Poisson Regression — Modeling net survival using STRS and Poisson regression (Stata)
• Net Survival: Flexible Parametric Modeling — Royston-Parmar flexible parametric survival models (Stata)
• Competing Risks Analysis — Cumulative incidence functions, cause-specific hazards, and Fine-Gray models (R)

Machine Learning

• Cross-Validation in Practice with R — K-fold CV, LOOCV, and bootstrap resampling for model selection

Interactive Shiny Apps

• Parametric Survival Distributions
• Colliders in Epidemiology
• Expected Date of Delivery
• Cancer Comorbidities Network

Stata Packages

• eltmle — Targeted Maximum Likelihood Estimation for the ATE with SuperLearner integration
• cvAUROC — Cross-validated AUC with bootstrap confidence intervals
• cmatch — Tabulation of matched pairs in 1:1 matched case-control studies

R Packages & Books

• Mathematical Statistics with R — An open-access textbook on mathematical statistics with R (Quarto)
• BioEstatR — R package for classical biostatistics applied to health sciences
• Bioestadistica Aplicada — Aplicaciones de bioestadistica para analisis de estudios epidemiologicos (ES)

Tools & Guides

• Claude Code en Windows — Guia paso a paso para instalar y usar Claude Code en Windows, orientada a estadisticos (ES)

📚 All tutorials: Browse all