Singlereplicate longitudinal data analysis in the presence of multiple instrumental measurement errors Abstract   In this paper, a novel method as a combination of the expectationmaximization (EM) algorithm and Variogram is proposed to decompose the longitudinal measurement errors in the absence of replications and the presence of multiple instrumental measurement errors. In the proposed method, multiple measurements are considered where the units are observed by several distinct instruments (gauges). The approach decouples the observed variance of the measurement model into the process and measurement system variances. In addition, it decomposes the variance of multiple instruments into the process and instrument variances. In the end, the proposed model is validated and tested based on simulated longitudinal data as well as a real case study related to the Framingham Heart study, measuring systolic blood pressure by multiple instruments. In addition, the robustness of the proposed method to the missing values, a common problem in longitudinal data, is demonstrated.

Multiple Predictively Equivalent Risk Models for Handling Missing Data at Time of Prediction: with an Application in Severe Hypoglycemia Risk Prediction for Type 2 Diabetes Abstract   The presence of missing data at the time of prediction limits the application of risk models in clinical and research settings. Common ways of handling missing data at the time of prediction include measuring the missing value and employing statistical methods. Measuring missing value incurs additional cost, whereas previously reported statistical methods results in reduced performance compared to when all variables are measured. To tackle these challenges, we introduce a new strategy, the MMTOP algorithm (Multiple models for Missing values at Time Of Prediction), which does not require measuring additional data elements or data imputation. Specifically, at model construction time, the MMTOP constructs multiple predictively equivalent risk models utilizing different risk factor sets. The collection of models are stored and to be queried at prediction time. To predict an individualxe2x80x99s risk in the presence of incomplete data, the MMTOP selects the risk model based on measurement availability for that individual from the collection of predictively equivalent models and makes the risk prediction with the selected model. We illustrate the MMTOP with severe hypoglycemia (SH) risk prediction based on data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. We identified 77 predictively equivalent models for SH with crossvalidated cindex of 0.77xc2xb10.03. These models are based on 77 distinct risk factor sets containing 1217 risk factors. In terms of handling missing data at the time of prediction, the MMTOP outperforms all four tested competitor methods and maintains consistent performance as the number of missing variables increase.