Time-dependent prediction and evaluation of variable importance using superlearning in high-dimensional clinical data.

Background: Prediction of outcome after injury is fraught with uncertainty and statistically beset by misspecified models. Single-time point regression only gives prediction and inference at one time, of dubious value for continuous prediction of ongoing bleeding. New statistical machine learning techniques such as SuperLearner (SL) exist to make superior prediction at iterative time points while evaluating the changing relative importance of each measured variable on an outcome.

Population intervention causal effects based on stochastic interventions.

Estimating the causal effect of an intervention on a population typically involves defining parameters in a nonparametric structural equation model (Pearl, 2000, Causality: Models, Reasoning, and Inference) in which the treatment or exposure is deterministically assigned in a static or dynamic way. We define a new causal parameter that takes into account the fact that intervention policies can result in stochastically assigned exposures. The statistical parameter that identifies the causal parameter of interest is established.

Super learner based conditional density estimation with application to marginal structural models.

In this paper, we present a histogram-like estimator of a conditional density that uses cross-validation to estimate the histogram probabilities, as well as the optimal number and position of the bins. This estimator is an alternative to kernel density estimators when the dimension of the covariate vector is large. We demonstrate its applicability to estimation of Marginal Structural Model (MSM) parameters in which an initial estimator of the exposure mechanism is needed.