According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are establishe...According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are established. The relationship between lead-time and inventory cost is studied by Matlab software. It shows that the variety of lead-time has an important effect on medicine inventory systems. Numerical simulation and sensitivity analysis of two models are presented by Lingo software. Based on analysis, it is concluded that the two-echelon model with lead-time results in inventory cost savings, and keeps the quality of care as reflected in service levels when compared with the three-echelon network structure.展开更多
Objective:To investigate what extent lead-time bias is likely to affect endoscopic screening effectiveness for esophageal cancer in the high-risk area in China.Methods:A screening model based on the epidemiological ca...Objective:To investigate what extent lead-time bias is likely to affect endoscopic screening effectiveness for esophageal cancer in the high-risk area in China.Methods:A screening model based on the epidemiological cancer registry data,yielding a population-level incidence and mortality rates,was carried out to simulate study participants in the high-risk area in China,and investigate the effect of lead-time bias on endoscopic screening with control for length bias.Results:Of 100,000 participants,6,150(6.15%)were diagnosed with esophageal squamous dysplasia during the 20-year follow-up period.The estimated lead time ranged from 1.67 to 5.78 years,with a median time of 4.62 years[interquartile range(IQR):4.07-5.11 years]in the high-risk area in China.Lead-time bias exaggerated screening effectiveness severely,causing more than a 10%overestimation in 5-year cause-specific survival rate and around a 43%reduction in cause-specific hazard ratio.The magnitude of lead-time bias on endoscopic screening for esophageal cancer varied depending on the screening strategies,in which an inverted U-shaped and U-shaped effects were observed in the 5-year cause-specific survival rate and cause-specific hazard ratio respectively concerning a range of ages for primary screening.Conclusions:Lead-time bias,usually causing an overestimation of screening effectiveness,is an elementary and fundamental issue in cancer screening.Quantification and correction of lead-time bias are essential when evaluating the effectiveness of endoscopic screening in the high-risk area in China.展开更多
Climatic variability influences the hydrological cycle that subsequently affects the discharge in the stream. The variability in the climate can be represented by the ocean-atmospheric oscillations which provide the f...Climatic variability influences the hydrological cycle that subsequently affects the discharge in the stream. The variability in the climate can be represented by the ocean-atmospheric oscillations which provide the forecast opportunity for the streamflow. Prediction of future water availability accurately and reliably is a key step for successful water resource management in the arid regions. Four popular ocean-atmospheric indices were used in this study for annual streamflow volume prediction. They were Pacific Decadal Oscillation (PDO), El-Nino Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Multivariate Relevance Vector Machine (MVRVM), a data driven model based on Bayesian learning approach was used as a prediction model. The model was applied to four unimpaired stream gages in Utah that spatially covers the state from north to south. Different models were developed based on the combinations of oscillation indices in the input. A total of 60 years (1950-2009) of data were used for the analysis. The model was trained on 50 years of data (1950-1999) and tested on 10 years of data (2000-2009). The best combination of oscillation indices and the lead-time were identified for each gage which was used to develop the prediction model. The predicted flow had reasonable agreement with the actual annual flow volume. The sensitivity analysis shows that the PDO and ENSO have relatively stronger effect compared to other oscillation indices in Utah. The prediction results from the MVRVM were compared with the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) where MVRVM performed relatively better.展开更多
文摘According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are established. The relationship between lead-time and inventory cost is studied by Matlab software. It shows that the variety of lead-time has an important effect on medicine inventory systems. Numerical simulation and sensitivity analysis of two models are presented by Lingo software. Based on analysis, it is concluded that the two-echelon model with lead-time results in inventory cost savings, and keeps the quality of care as reflected in service levels when compared with the three-echelon network structure.
文摘Objective:To investigate what extent lead-time bias is likely to affect endoscopic screening effectiveness for esophageal cancer in the high-risk area in China.Methods:A screening model based on the epidemiological cancer registry data,yielding a population-level incidence and mortality rates,was carried out to simulate study participants in the high-risk area in China,and investigate the effect of lead-time bias on endoscopic screening with control for length bias.Results:Of 100,000 participants,6,150(6.15%)were diagnosed with esophageal squamous dysplasia during the 20-year follow-up period.The estimated lead time ranged from 1.67 to 5.78 years,with a median time of 4.62 years[interquartile range(IQR):4.07-5.11 years]in the high-risk area in China.Lead-time bias exaggerated screening effectiveness severely,causing more than a 10%overestimation in 5-year cause-specific survival rate and around a 43%reduction in cause-specific hazard ratio.The magnitude of lead-time bias on endoscopic screening for esophageal cancer varied depending on the screening strategies,in which an inverted U-shaped and U-shaped effects were observed in the 5-year cause-specific survival rate and cause-specific hazard ratio respectively concerning a range of ages for primary screening.Conclusions:Lead-time bias,usually causing an overestimation of screening effectiveness,is an elementary and fundamental issue in cancer screening.Quantification and correction of lead-time bias are essential when evaluating the effectiveness of endoscopic screening in the high-risk area in China.
文摘Climatic variability influences the hydrological cycle that subsequently affects the discharge in the stream. The variability in the climate can be represented by the ocean-atmospheric oscillations which provide the forecast opportunity for the streamflow. Prediction of future water availability accurately and reliably is a key step for successful water resource management in the arid regions. Four popular ocean-atmospheric indices were used in this study for annual streamflow volume prediction. They were Pacific Decadal Oscillation (PDO), El-Nino Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Multivariate Relevance Vector Machine (MVRVM), a data driven model based on Bayesian learning approach was used as a prediction model. The model was applied to four unimpaired stream gages in Utah that spatially covers the state from north to south. Different models were developed based on the combinations of oscillation indices in the input. A total of 60 years (1950-2009) of data were used for the analysis. The model was trained on 50 years of data (1950-1999) and tested on 10 years of data (2000-2009). The best combination of oscillation indices and the lead-time were identified for each gage which was used to develop the prediction model. The predicted flow had reasonable agreement with the actual annual flow volume. The sensitivity analysis shows that the PDO and ENSO have relatively stronger effect compared to other oscillation indices in Utah. The prediction results from the MVRVM were compared with the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) where MVRVM performed relatively better.
