Objective Implement random sample from a simulation population,to evaluate the The impact of samplesize and sample-process on several usual importance evaluate methods,observe the stability of those methods.Methods This study introduced existed importance methods,using PROC SURVEYSELECT procedure to sample a fixed population for 1000 times,generating 1000 same size sample,to evaluate the stability of relative importance methods.We sampled the population to generate datasets with different sample size to observe impact of sample-size on those methods.Results The sum of squared correlation coefficients' estimator is bigger than model R-square,squared standardized regression coefficients' sum is smaller.In contrary,sum of the Product Measure,Relative Weight and Dominance Analysis are extremely close to model R-square.When the sample size small than 1000,the estimator have obviously variation,but the variation decreased when the sample size rise up.Conclusion The dominance analysis has best stability,also has the best match of model R2 in those methods.

Objective To calculate the social economic burden of mental illness,and to analyze the economic impact of different patients with mental illness in Shandong Province.Methods Direct method was used to calculate the direct economic burden,and human capital method was employed to estimate the indirect economic burden.Results The social economic burden increased from 10.076 billion in 2005 to 31.277 billion in 2013,and the proportion of the social economic burden accounting for GDP of Shandong province was between 0.5 ％ ～ 0.7％.The economic burden caused by women,18-39 years old patients,rural patients and mood disorders was higher than that of men,more than 55 years-old patients,urban patients and other diagnoses,respectively.Conclusions The economic burden brought by mental illness was heavy and increasing during 2005-2013.The economic burden caused by different people was heterogeneous.

Objective To compare the difference of effect between principal component regression analysis and projection pursuit regression analysis when collinearity exists in data.Methods Evaluating the advantages and disadvantages of the two modeling methods by using the actual data on two aspects:the fitting effect and the predicting effect.Results The principal component regression model showed that the coefficient of determination was 0.8172,the mean of absolute relative error was 6.42％ and the mean square of prediction error was 0.61.The projection pursuit regression model,on the other hand,showed that the coefficient of determination ranged from 0.8851 to 0.9944,the mean of absolute relative error ranged from1.11％ to 4.81％ and the mean square of prediction error ranged from 0.03 to 0.38.Conclusion The analysis results based on the actual data with collinearity indicate that the projection pursuit regression analysis outperforms the principal component regression analysis both in fitting and predicting effect.

Objective To explore the trend of mortality and years of life lost due to Esophageal Cancer in residents in Tieling,so as to provide the basis data on preventing Esophageal cancer in Tieling.Methods The data of residents in Tieling dying of Esophageal cancer from 2007 to 2015 was collected and cleared up to calculate the evaluation indexes including the mortality rate,the average percentage change of mortality rate.GM(1,1) model was used to predict the future mortality.Results From 2007 to 2015,the Average Esophageal cancer Mortality Rate of in residents in Tieling was 5.26 per 100000 persons,and especially 1.95％ raised a year.The Mortality Rate would increase from 2016 to 2019.Conclusion Tieling Esophageal Cancer mortality rate is on the rise,especially for elder men more than 60.So that the proper prevention measures should be car ried and strengthened.

Objective To evaluate the performance of a prediction system built with LASSO regression model and Baidu search query data.Methods Based on a strategy using a combination of Bagging and multi-measure optimization method,this study proposed an ensemble LASSO regression model which had an obviously improved performance,and applied it to predict the epidemics of influenza in China.Results The results showed that the improved model had significantly smaller prediction error rates than that of the conventional LASSO regression model for influenza cases during the study period of 2011-2015.This study designed an open source R package,SparseLearner,which was conveniently used and further developed.Conclusion The combination of Bagging and multi-measure optimization method is an efficient strategy to improve the performance of LASSO regression model.The proposed ensemble LASSO regression model in this study can be applied for the prediction of infectious diseases epidemics.

Objective To explore the application of auto-regressive integrated moving average (ARIMA) product season model in predicting number of inpatient and hospitalized expense of malignant tumor,and to provide scientific basis for hospital business management.Methods We collected inpatient data of malignant tumor from January 2007 to December 2015 in one hospital for model fitting,and used monthly data 2016 to verify the effect of model prediction.We predicted the number of inpatient and hospitalized expense of malignant tumor in 2017.Results ARIMA (0,1,1) (1,1,0) 12 was the best model for number of inpatient and hospitalized expense of malignant tumor,with prediction fitting errors of 1.1％ and 1.47 ％,respectively.The number of inpatient and hospitalized expense of malignant tumor in 2017 were predicted to be 7631 and 0.336 billion.Conclusion ARIMA product season model can better applied in the predicting of hospital business management.

Objective To learn quality of life of acute upper respiratory tract infection patients and their influence factors.Methods We conducted questionnaire investigation on acute upper respiratory tract infection patients from five hospitals,respectively located in Wuhan,Hefei and Jinhua.The scale we used is EQ-5D.We calculated EQ-5D score through three different integration systems,which are from China,Korea and the UK,in order to compare difference among different countries' integration systems.Then econometric model was used to carry out regression analysis on factors affected EQ-5D score.Results 659 samples was included,with 319 from Wuhan,235 from Hefei and 105 from Jinhua.Among EQ-5D five dimensions,the first three dimensions do not have problems.However,81.94％ of the patients have problems in pain/discomfort and 47.8％ of them have problems in anxiety/depression.In regression analysis,location,severity level of disease and whether accompanied by chronic diseases or not have influence on EQ-5D score.Compared with utilities of patients who are suffered from other diseases,utilities of acute upper respiratory tract infection patients are lower than that of cerebral apoplexy,hypertension,coronary heart diseaseand diabetes patients;but are higher than that of Chronic lymphatic filariasis,chronic obstructive pneumonia and rheumatoid arthritis patients.Conclusion Chinese acute upper respiratory tract infection patients mainly have problems in the dimension of pain/discomfort and anxiety/depression.Compared to British integration system,Korean's is more applicable to Chinese population.Acute upper respiratory tract infection patients' utilities are lower than that of healthy population and chronic invalids with no obvious symptoms,and higher than that of chronic invalids with obvious symptoms.

Objective Research on variable substitution to non-linear regression forecast model precision's influence, and seek the modelling method that can improve the forecast precision. Methods Based on the data mining,the transform in space and the weighted processing combined method, make full use of information that the primary data provide. Results Given modelling method of combination forecast model based on the data mining. Conclusion Based on data mining's combination forecast model's modelling method can reduce the serious influence that the variable substitution brings and has fully used useful information in the primary data. It obviously improved the accuracy of the prediction model.

Objective To compare the efficiency of χ~2-Fisher's exact test which is one of the competitive null hypothesis approaches with SAM-GS which belongs toself-contained null hypothesis approaches. Methods The two methods were used to analyze a simulation experiment which contained five different scenarios. The results were compared with the simulated initialization,and assessing indexes were calculated to compare the efficiency. Results Under the same conditions,SAM-GS always have higher power than that of χ~2-fisher's exact test. However, the final inference is equivalent, namely if the difference between the two groups are smaller than 0.30,the two methods can not be better to identify differences between them. By contrary, when the differences between the two phenotypes are larger than 0.30, the two ways can both identify differences. Conclusion SAM-GS tends to have slightly higher power thanχ~2-Fisher' s exact test. The two methods can be used for screening enrichment gene sets of gene expression profile.χ~2-Fisher's exact test has the important advantage of being able to analyze multi-class phenotype.

Objective To explore the application of multilevel growth model in community intervention research. Methods The data a-bout blood pressure collected through questionnaire at baseline and 6 months, 12 months after intervention were analysed through multilevel growth model to evaluate the effectiveness of hypertension self-management. Results The blood pressures between the two groups were statistical significance at baseline,and the changing trends also were different. Age could affect diastolic blood pressure (DBP) changing after controlling the other factors. Conclusion Multilevel growth model can analyze the longitudinal data acquired from intervention research flexibly,and the result is more reliable.