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Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application, at Oviedo, Spain, Friday, September 6, 2019:
xxvii_see19_LUQUE_colider.pdf
Classical epidemiology has focused on the control of confounding, but it is only recently that epidemiologists have started to focus on the bias produced by colliders. A collider for a certain pair of variables (e.g. an outcome Y and an exposure A) is a third variable (C) that is caused by both. In a directed acyclic graph (DAG), a collider is the variable in the middle of an inverted fork (i.e. the variable C in A → C ← Y). Controlling for, or conditioning an analysis on a collider (i.e. through stratification or regression) can introduce a spurious association between its causes. This... Read more about Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application
Ensemble Learning Targeted Maximum Likelihood Estimation for Stata Users (invited talk), at Pompeu Fabra University, Barcelona, Spain (Spanish Stata Users Meeting, 2018), Wednesday, October 24, 2018:
malf_eltmle_sum_bcn_2018.pdf

eltmle is a Stata program implementing the targeted maximum likelihood estimation (TMLE) for the ATE for a binary or continuous outcome and binary treatment. eltmle includes the use of a super-learner called from the SuperLearner package v.2.0-21 (Polley E., et al. 2011). Modern Epidemiology has been able to identify significant limitations of classic epidemiological methods, like outcome regression analysis, when estimating causal quantities such as the average treatment effect (ATE) for observational data. For...

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Cross-validated Area Under the ROC curve for Stata users: cvauroc (invited talk), at Pompeu Fabra University, Barcelona, Spain (Spanish Stata Users Meeting, 2018), Wednesday, October 24, 2018:
ma-luque-stata-ugm-bcn-auroc-18.pdf

Receiver operating characteristic (ROC) analysis is used for comparing predictive models, both in model selection and model evaluation. This method is often applied in clinical medicine and social science to assess the trade-off between model sensitivity and specificity. After fitting a binary logistic regression model with a set of independent variables, the predictive performance of this set of variables - as assessed by the area under the curve (AUC) from a ROC curve - must be estimated for a sample (the 'test' sample) that is independent of the sample used...

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Pattern of comorbidities among Colorectal Cancer Patients and impact on treatment and short-term survival, at Copenhange, Denmark (European Network of Cancer Registries: Conference), Friday, September 28, 2018:
maluque_encr18_comcor.pdf

 

Background: Colorectal cancer (CRC) is the most frequently diagnosed cancer in Spain in both sexes with 41,441 new cases in 2015. There is little evidence regarding the pattern and impact of comorbidities on time from cancer diagnosis to surgical treatment and short-term mortality among CRC patients in Spain.

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Clinical Epidemiology in the Era of the Big Data Revolution: New Opportunities (invited talk), at NDMORS, Medical Science Division, Oxford University, Oxford, UK, Thursday, November 2, 2017:
oxfordtalk.pdf
Science is moving towards a new data-driven paradigm. The big data revolution offers new opportunities for the development of Patient Centered Epidemiologic Methods (PCEM) under the Comparatives Effectiveness Research (CER) framework for clinical and applied epidemiologists and statisticians.
Ensemble Learning Targeted Maximum Likelihood Estimation , at London, Friday, September 8, 2017:
ma-luque_sugm_london_2017_eltmle.pdf

UK 2017 Stata Users Group Meeting:

When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G‐formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double‐robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation...

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