Accompanying data for "A Multilevel statistical process control chart framework for personalized patient monitoring: A simulation study using patients historical data"

Statistical Process Control (SPC) charts offer a potentially effective means of continuously monitoring patient time-series data to enable the early detection ofclinical abnormalities. A significant challenge in implementing SPC methods, such as the Exponentially Weighted Moving Average (EWMA), is obtaining sufficient phase-I data from individual patients. This data is crucial for accurately estimating control limits using the expected mean and standard deviation duringstable periods. In contrast to other fields where ample historical data is available, patient monitoring systems often lack enough observations to effectively set these limits. To address this issue, we propose a multilevel SPC approach that uses historical data from stable subjects. By fitting a multilevel model with arandom intercept, our method enables comparison of an individual’s longitudinal measurements against cross-sectional data from other stable patients. Themodel recursively estimates the expected mean within a calibration window, applying distinct control limit coefficients compared to phase-II monitoring period. Person-specific deviations, computed as residuals between actual and estimated values are then monitored via an EWMA control chart. Simulation studies usingsynthetic data modeled on left ventricular assist device (LVAD) patients were conducted to evaluate this approach. The model was evaluated across multiplesimulation scenarios, varying parameters such as sampling frequency, shift size and type, to assess its robustness under different operating conditions. Resultsshowed that longer calibration periods enhanced alignment with patient data, thereby increasing the true-positive rate and reducing false alarms. However, if the calibration window becomes excessively long, it may lead to overfitting, which could delay detection or result in an increased false-positive rate. Our multilevel SPC framework demonstrates potential to improve early detection of mean shifts in patient monitoring data, offering a refined approach to personalized healthcaremonitoring and data-driven clinical decision-making. This repository contains the simulated LVAD data used in our study on multilevel statistical process control for personalized patient monitoring. The simulations were generated using the CPAR (probability autoregressive) model across all parameter combinations. Data with shifts stored in data_with_shifts.zip file. Using the CPAR framework, we generated 25,600 distinct simulation scenarios (5 × 4 × 4 × 2 × 2 × 2 × 2 × 5 × 4 combinations of settings basedon the simulation nstudy). For each scenario, a cohort of 500 unique patients was created: 250 patients for monitoringand 250 patients from a stable cohort, randomly sampled to assess the impact of the simulation parameters. Each patient trajectory was generated uniquely for that specific scenario based on the CPAR model. Code can be accessed via HERE.