Application of the Joint Latent Class Model in the Reversion of Mild Cognitive Impairment to Normal Cognition / 中国卫生统计

Objective The joint latent class model(JLCM)was applied to study the reversion of mild cognitive impairment(MCI)to normal cognition(NC),providing a methodology reference for the longitudinal study of chronic diseases.Methods The JLCM model consisted of three sub-models:the latent class sub-model adopted multi-class logistic regression to estimate the latent classes;the longitudinal process sub-model adopted linear mixed model to describe the longitudinal cognitive measurement;and the survival process sub-model adopted proportional risk model to fit the cognitive process.Goodness of fit was based on information criterion,posterior classification,comparison between predicted value and observed value and conditional independence assumption.Results The MCI population was divided into two latent classes based on the JLCM model,including 783(92.88%)and 60(7.12%),respectively.The latent class sub-model showed that gender,marital status,APOE4 and FAQ had statistical significance for the determination of latent classes.The longitudinal process sub-model showed male(β=-0.685,95%CI:-1.144,-0.226),being single(β=0.743,95%CI:0.200,1.286),not carrying APOE4(β=-1.201,95%CI:-1.636,-0.766)and without functional impairment(β=-1.868,95%CI:-2.095,-1.641)had higher MMSE scores.The survival process sub-model showed that high education level(HR=1.264,95%CI:1.134,1.395),being single(HR=1.593,95%CI:1.286,1.899)and not carrying APOE4(HR=0.453,95%CI:0.043,0.862)were protective factors for reversion to NC in MCI patients.Conclusion JLCM model has good application value in the study of chronic diseases such as cognitive impairment.

New Price Conversion Method of Anti-cancer Drugs under the Buy-and-gift Strategy Based on Survival Data / 中国药房

OBJECTIVE：To provid e a more accurate calculation method for the determination of drug negotiation base price ， pharmacoeconomic calculation and budget impact analysis and calculation in the process of medical insurance access with buy-and-gift strategy. METHODS ：By the model method and literature research ，understanding the existing price conversion methods of the anti-cancer drugs that currently implement the buy-and-gift strategy ，a new method of drug price conversion was explored on the basis of the survival data of patients in different disease states ，and the core idea and calculation process of the algorithm were analyzed by an example. RESULTS ：The new algorithm was combined with the survival data of patients under different disease states. Its calculation process mainly included obtaining the actual duration of medication use per unit cycle and the theoretical amount of medication ，determining the aid model for anti-cancer drugs under buy-and-gift strategy ，converting the actual price. The simulation calculation was carried out under the one-step drug donation mode ，periodic drug donation mode and preferential installment drug donation mode. CONCLUSIONS ：The conversion method of anti-cancer drug price under buy-and-gift strategy based on survival data makes up for the shortcoming that the existing calculation methods are difficult to reflect the actual price of anti-cancer drugs ，and provides a new calculation method for calculating the actual reference price of anti-cancer drugs for medical insurance access.

How to use χ2 test correctly——the log-rank test and the implementation of SAS software / 四川精神卫生

The purpose of this article was to introduce the log-rank test and the SAS implementation. There were many different expression forms for the test statistics of the log-rank tests. Among them， there were two most common expression forms： the first was similar to "Pearson΄s goodness-of-fit χ2 test statistic"； the second was similar to "the test statistic of the odds ratio of high-dimensional table data， that was， the Breslow-Day΄s χ2 test statistic". The log-rank test statistic had two distribution types， one was the χ2 distribution， the other was the standard normal distribution. In the process of constructing log-rank test statistics， there were four contents that needed to be paid attention to： ① the sequential multiple four-fold tables formed by stratification or partition after sorting according to the individual "survival time"； ② it was necessary to distinguish whether the "survival time of each individual" was the complete data or the censored data； ③ only the calculation of the "theoretical or expected frequency" on a specific grid ［for example， （1，1） grid］ in each four-fold table data； ④ the method of calculating the theoretical frequency was different from the one in the independent test of the data of the four-fold table. Based on two examples with the different data structures， the paper realized the log-rank tests with the help of the SAS software.

How to use χ2 test correctly——the likelihood ratio test and the six nonparametric tests of the survival data and the implementation of the SAS software / 四川精神卫生

The purpose of this article was to introduce the likelihood ratio test， six nonparametric tests， and the SAS implementation of the survival data. Based on the assumption that the survival data had the exponential distribution， the likelihood ratio test method was derived， the main difference between six nonparametric test methods was that they had different weight functions. Under the conditions of non-stratification and stratification， the seven survival data hypothesis testing methods mentioned above could be used， and their common point was that their test statistics all followed the χ2 distribution. Through two examples and by means of the SAS software， the article realized the various hypothesis tests for two or more groups of survival data， outputed and explained SAS calculation results， and made statistical and professional conclusions.

How to use χ2 test correctly——the three kinds of special tests of the survival data and the implementation of the SAS software / 四川精神卫生

The purpose of the paper was to introduce the three special tests of the survival data and the SAS implementation. Specifically， it was the multiple comparisons， the trend test and the covariate test of the survival data. The multiple comparisons involved two situations： "the pairwise comparison" and "the comparison with control group". In the trend test， it involved two algorithms： "the log-rank test" and "the Wilcoxon test". In the covariate test， it involved "the single covariate test method" and "the multi-covariate test method of adding one covariate step by step". With the help of the SAS software and based on an example， this article implemented the three special tests mentioned above， explained the output results， and made statistical and professional conclusions.

Modelo de Cox para análise da sobrevida das empresas de plano de saúde no Brasil / Cox model for survival analysis of health insurance companies in Brazil

Objetivo: No Brasil, estudos sobre o tempo de vida das operadoras de planos de saúde são escassos. Assim, o artigo tem o objetivo de investigar fatores econômicos que explicam a sobrevida de empresas de plano de saúde. Métodos: Foi utilizada a técnica estatística denominada análise de sobrevivência, por meio do modelo semiparamétrico de Cox. Os dados foram obtidos no site da ANS (agência reguladora do setor) e referem-se a 929 operadoras de todas as regiões do país, em 2011-2018. As seguintes variáveis foram analisadas: Beneficiário (número médio de beneficiário por operadora), Porte (Porte 1: até 20 mil beneficiários, Porte 2: entre 20 mil e 100 mil beneficiários, Porte 3: acima de 100 mil beneficiários), Tempo no Mercado (quantidade de trimestres que a operadora permaneceu no mercado), Receita/Despesa (Muito baixa, Baixa, Alta e Muito alta), Lucro (Operadoras que lucraram e Operadoras que não lucraram), Tipo de Gestão (Gestão sem fins lucrativos e Gestão empresarial) e por último tem-se a variável (dependente) Falência, que indica se a operadora solicitou o encerramento das suas atividades. Resultados: Observou-se uma taxa de mortalidade para operadoras de pequeno porte maior comparativamente às demais, com probabilidade de não sobrevivência no mercado duas vezes menor em relação às empresas de médio porte e três vezes menor se comparada às de grande porte. Conclusões: Empresas de pequeno porte encontram-se em grande desvantagem no panorama brasileiro de operadoras de planos de saúde, qualquer que seja seu tempo de vida no mercado.

Objective: In Brazil, studies on the survival time of health insurance providers are scarce. Thus, this article aims to investigate economic factors that explain the survival of these companies. Methods: The statistical technique survival analysis was used (Cox semi-parametric model). Data were obtained from the ANS (regulatory agency) website and refer to 929 operators from all regions of the country, 2011-2018. The following variables were analyzed: Beneficiaries (average number of beneficiaries per provider), Size (Size 1: Up to 20 thousand beneficiaries, Size 2: Between 20 thousand and 100 thousand beneficiaries, Size 3: Above 100 thousand beneficiaries) Time in market (Number of quarters that the provider remained in the market), Revenue/Expenses (Very Low, Low, High and Very High), Profit (Providers that profited and providers that did not profit), Type of Management (Nonprofit Management and For profit/Business Management) and lastly the (dependent) Bankruptcy variable, which indicates if the operator requested the ending of its activities. Results: A higher mortality rate was observed for small providers compared to the others, with a probability of non-survival in the market two times smaller in relation to medium-sized companies and three times lower than the large ones. Conclusions: Small businesses are at a great disadvantage in the Brazilian panorama of health insurance providers, regardless of their life time in the market.

Introduction of joint model and its applications in medical research / 中华流行病学杂志

In medical follow-up studies, longitudinal data and survival data are often accompanied and associated with each other, thus respective analysis of longitudinal and survival data might lead to biased results. Joint model can correct deviations, improve the efficiency of parameter estimation and provide effective inferences by simultaneously processing longitudinal and survival data. It is a popular method in medical research. Joint model has made much progress, whereas the literature about the joint model and its application is limited in China. This paper summarizes the main idea, basic framework, parameter estimation methods of random effect joint model and introduces the analysis on AIDS data set based on the R software package 'JM’ to clarify the advantages of the joint model in processing medical follow-up data and promote the use of the joint model in clinical research.

Analysis of the clinical characteristics and its prognostic factors for hilar cholangiocarcinoma with exaire-sis / 实用肿瘤学杂志

Objective To investigate the clinical characteristics and its prognostic factors for hilar cholangiocarcinoma with exairesis .Methods The clinical data of 58 cases of hilar cholangiocarcinoma were ret-rospectively analyzed .The Kaplan-Meier method was used to estimate the overall survival and disease specific survival rates for these patients .And the factors that may influence the prognosis and survival of patients were an -alyzed using univariate(log-rank test)and multivariate Cox proportional hazard models .Results The median survival time was 22 months for all patients .The 1-,3-and 5-year survival rates were 76%,40%and 21%, respectively.Univariate analysis showed that preoperative albumin (P=0.002),intraoperative blood loss (P=0.039),surgical method(P =0.006),histologic differentiation(P =0.001),portal vein encroached(P =0.014),surgical margin(P=0.020)were correlated factors for postoperative survival duration .Multivariate analy-sis by Cox Proportional Hazard Model showed that surgical method (P=0.022),histologic differentiation(P=0.020)were independent prognostic factors for patients with Hilar cholangiocarcinoma excised .Conclusion Low albumin leve,intraoperative blood loss more than 500 mL,low degree of tumor differentiation,portal vein en-croached ,radical surgery ,positive surgical margin are risk factors for total survival of Hilar cholangiocarcinoma .