Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables

• Published in: PloS one Vol. 10; no. 3; p. e0120031
• Main Authors: Díaz, Iván, Hubbard, Alan, Decker, Anna, Cohen, Mitchell
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.03.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Bioinformatics; Clinical decision making; Decision making; Euclidean space; Evidence-Based Medicine - methods; Humans; Intervention; Longitudinal Studies; Machine Learning; Mathematical models; Molecular biology; Physicians; Prediction models; Predictive Value of Tests; Problems; Prognosis; Proteins; Regression analysis; Statistical analysis; Statistical models; Trauma; Trauma Centers; Variables; Wounds and Injuries - mortality; Wounds and Injuries - therapy; New York; San Francisco California; California; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0120031

We present prediction and variable importance (VIM) methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient's physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed). Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner), which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO). In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC) for a receiver-operator curve (ROC). Thus, given that 1) our VIM applies to any model fitting procedure, 2) under assumptions has meaningful clinical (causal) interpretations and 3) has asymptotic (influence-curve) based robust inference, it provides a compelling alternative to existing methods for estimating variable importance in high-dimensional clinical (or other) data.