Submodel Approximation under Preconditioning Outcome Approach

Clinical prediction models have been widely acknowledged as an informative tool that provides evidence-based support for clinical decision making. However, such prediction models are often underused in clinical practice due to many reasons including the presence of missing information in a new patient. Motivated by a study to implement a prediction model (STRATIFY) into the clinical work flow of emergency department, we propose a novel submodel estimation approach to address real-time missing information issues. For prediction models such as STRATIFY that were developed using the "preconditioning outcome" approach, the proposed submodel coefficients are shown to be equivalent to the original prediction model coefficients plus a corrected factor corresponding to the orthogonal projection of the missing components in the preconditioning outcome onto the range space of non-missing information. Comprehensive simulations were conducted to assess the performance of the proposed estimation approach and compared with an existing "one-step-sweep" based approach using various performance measurements including C-index, negative and positive predicted value (NPV, PPV), calibration intercept and slope, Brier score and root mean squared predicted error (rMSPE). The proposed approach were applied to electronic health records (EHR) data from the Emergency Department at Vanderbilt University Medical Center to develop submodels for STRATIFY which will subsequently be embedded in the STRATIFY clinical decision support tool for real-time implementation.

clinical prediction modeling

submodel

real-world implementation

missing data

preconditioning 

Mid-Level

Knowledge

Women in Statistics and Data Science 2022