Research projects, software packages, and tutorials
Comprehensive tutorial on conformal inference covering split conformal prediction (SCP), conformal quantile regression (CQR), full conformal, and Jackknife+. Includes interactive notebooks in English and structured lecture slides in Spanish with full implementation details.
Approximate statistical inference via determination of the asymptotic distribution of a statistic is routinely used for inference in applied medical statistics (e.g. to estimate the standard error of the marginal or conditional risk ratio). One method for variance estimation is the classical Delta-method but there is a knowledge gap as this method is not routinely included in training for applied medical statistics and its uses are not widely understood.
Effect modification and collapsibility when estimating the effect of public health interventions: A Monte-Carlo Simulation comparing classical multivariable regression adjustment versus the G-Formula based on a cancer epidemiology illustration: The American Journal of Public Health series Evaluating Public Health Interventions offers excellent practical guidance to researchers in public health. In the 8 part of the series, a valuable introduction to effect estimation of time-invariant public health interventions was given. The authors of this article suggested that in terms of bias and efficiency there is no advantage of using modern causal inference methods over classical multivariable regression modeling. However, this statement is not always true. Most importantly, both effect modification and collapsibility are important concepts when assessing the validity of using regression for causal effect estimation ( https://github.com/migariane/hetmor/blob/master/README.md)
A collider for a certain pair of variables (outcome and exposure) is a third variable that is influenced by both of them. Controlling for, or conditioning the analysis on (i.e., stratification or regression) a collider, can introduce a spurious association between its causes (exposure and outcome) potentially explaining why the medical literature is full of paradoxical findings [6]. In DAG terminology, a collider is the variable in the middle of an inverted fork (i.e., variable W in A -> W <- Y). While this methodological note will not close the vexing gap between correlation and causation, but it will contribute to the increasing awareness and the general understanding of colliders among applied epidemiologists and medical researchers.
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.
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.
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.
In this tutorial the computational we show the implementation of different causal inference estimators from a historical perspective where different estimators were developed to overcome the limitations of the previous one. Furthermore, we also briefly introduce the potential outcomes framework, illustrate the use of different methods using an illustration from the health care setting, and most importantly, we provide reproducible and commented code in Stata, R and Python for researchers to apply in their own observational study. Available at https://arxiv.org/abs/2012.09920