Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables

By Iván Díaz, Alan E. Hubbard, Anna Decker, Mitchell J. Cohen. 2015

33 citations recorded by the source corpus.

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.

Subfields: Emergency Medicine, Statistics and Probability, Surgery

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Citations in the corpus

All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, methodology): “The label of “variable importance” measure has been broadly used to describe approaches for either inference (van der Laan, 2006; Dı́az et al., 2015; Williamson et al., 2017) or prediction.”
Handbook of Big Data (2016, methodology): “As discussed earlier, the list of variables could involve clinical or epidemiological data [6,45], and these tools also have important implications for testing for possible effect modification (e.”
All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, methodology): “The label of “variable importance” measure has been broadly used to describe approaches for either inference (van der Laan, 2006; Díaz et al., 2015; Williamson et al., 2017) or prediction.”
Causal mediation analysis for stochastic interventions (2019, unknown): “For example, they allow the seamless definition of causal effects for continuous exposures with an interpretation that is familiar to regular users of linear regression adjustment (Dı́az and van der Laan, 2012; Haneuse and Rotnitzky, 2013; Dı́az et al., 2015), and can be used in longitudinal studies”