Selected Publications

We propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We performed numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compared two different model selection strategies based on 1) the Akaike and Bayesian Information Criteria and 2) machine-learning algorithms, and illustrated double-robust estimators’ performance in a real setting. In simulations with correctly specified models and near-positivity violations, all but the naïve estimators presented relatively good performance. However, the augmented inverse-probability treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine-learning algorithms. We applied these methods to estimate adjusted one-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus non-emergency cancer diagnosis in England, 2006–2013. The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms.
In AJE, 2017

Although smoking during pregnancy may lead to many adverse outcomes, numerous studies have reported a paradoxical inverse association between maternal cigarette smoking during pregnancy and preeclampsia. Using a counterfactual framework we aimed to explore the structure of this paradox as being a consequence of selection bias. Using a case–control study nested in the Icelandic Birth Registry (1309 women), we show how this selection bias can be explored and corrected for. Cases were defined as any case of pregnancy induced hypertension or preeclampsia occurring after 20 weeks’ gestation and controls as normotensive mothers who gave birth in the same year. First, we used directed acyclic graphs to illustrate the common bias structure. Second, we used classical logistic regression and mediation analytic methods for dichotomous outcomes to explore the structure of the bias. Lastly, we performed both deterministic and probabilistic sensitivity analysis to estimate the amount of bias due to an uncontrolled confounder and corrected for it. The biased effect of smoking was estimated to reduce the odds of preeclampsia by 28 % (OR 0.72, 95 %CI 0.52, 0.99) and after stratification by gestational age at delivery (<37 vs. ≥37 gestation weeks) by 75 % (OR 0.25, 95 %CI 0.10, 0.68). In a mediation analysis, the natural indirect effect showed and OR > 1, revealing the structure of the paradox. The bias-adjusted estimation of the smoking effect on preeclampsia showed an OR of 1.22 (95 %CI 0.41, 6.53). The smoking-preeclampsia paradox appears to be an example of (1) selection bias most likely caused by studying cases prevalent at birth rather than all incident cases from conception in a pregnancy cohort, (2) omitting important confounders associated with both smoking and preeclampsia (preventing the outcome to develop) and (3) controlling for a collider (gestation weeks at delivery). Future studies need to consider these aspects when studying and interpreting the association between smoking and pregnancy outcomes.
In EJEP, 2016

Recent Publications

. Targeted Maximum Likelihood Estimation: A tutorial. In SIM, 2018.


. Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk differences for lung cancer mortality by emergency presentation. In AJE, 2017.

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Recent Talks

Clinical Epidemiology in the Era of the Big Data Revolution: New Opportunities.
Nov 8, 2017 12:00 AM
Ensemble Learning Targeted Maximum Likelihood Estimation, at London.
Oct 15, 2017 12:00 AM


Ensemble Learning for Model Prediction in Cancer Epidemiology

To improve model selection and prediction in cancer epidemiology data adaptive ensemble learning methods based on the Super Learner as a method for variable selection via cross-validation are suitable. To selection of the optimal regression algorithm among all weighted combinations of a set of candidate machine learning algorithms the ensemble learning method improves model accuracy and prediction.

Targeted Maximum Likelihood Estimation: A Tutorial for Applied Researchers

TMLE is a semiparametric doubly-robust method for Causal Infernece that enhances correct model specification by allowing flexible estimation using non-parametric machine-learning methods and requires weaker assumptions than its competitors.


cvAUROC is a Stata program that implements k-fold cross-validation for the AUC for a binary outcome after fitting a logistic regression model. Evaluating the predictive performance (AUC) of a set of independent variables using all cases from the original analysis sample tends to result in an overly optimistic estimate of predictive performance. K-fold cross-validation can be used to generate a more realistic estimate of predictive performance.


I am a Distance Learning Module Organizer for the MSc in Epidemiology at the LSHTM and I teach in the following short courses:

  • EPM304 Advanced Statistical Methods in Epidemiology

  • Short Course: Survival Analysis in Cancer Epidemiology

  • Short Course: Introduction to Causal Inference and the Potential Outcomes Framework (In development)

Also, I am developing software for teaching and scientific interests: