Simulating the Risk of Stroke in China Using Markov Model Based on National Screening Data In recent years, the increasing incidence and prevalence of stroke has brought a heavy economic burden on families and society in China. The Ministry of Health of the Peoplesu0027 Republic of China initiated a national stroke screening and intervention program in 2011 for stroke prevention and control. In this paper, we build Markov models for Chinese aged 4059yearsold based on the stroke screening cohort data in 20132017 in order to study the 10year risk of stroke for patients with different chronic diseases and targeted prevention and control measures for them. The results show that the stroke risk of people with hypertension is higher than that of people with diabetes or dyslipidemia. Considering the high prevalence of hypertension in China, it is more important to control hypertension for stroke prevention and control. For patients with diabetes and dyslipidemia, interventions should be taken according to gender and age. The simulation results can provide decisionmaking basis at the national level for guiding key population to control chronic diseases for stroke prevention.

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.