Log-normal censored regression model detecting prognostic factors in gastric cancer:A study of 3018 cases

AIM:To investigate the efficiency of Cox proportional hazard model in detecting prognostic factors for gastric cancer.METHODS:We used the log-normal regression model to evaluate prognostic factors in gastric cancer and compared it with the Cox model.Three thousand and eighteen gastric cancer patients who received a gastrectomy between 1980 and 2004 were retrospectively evaluated.Clinic-pathological factors were included in a log-normal model as well as Cox model.The akaike information criterion (AIC) was employed to compare the efficiency of both models.Univariate analysis indicated that age at diagnosis,past history,cancer location,distant metastasis status,surgical curative degree,combined other organ resection,Borrmann type,Lauren's classification,pT stage,total dissected nodes and pN stage were prognostic factors in both log-normal and Cox models.RESULTS:In the final multivariate model,age at diagnosis,past history,surgical curative degree,Borrmann type,Lauren's classification,pT stage,and pN stage were significant prognostic factors in both log-normal and Cox models.However,cancer location,distant metastasis status,and histology types were found to be significant prognostic factors in log-normal results alone.According to AIC,the log-normal model performed better than the Cox proportional hazard model (AIC value:2534.72 vs 1693.56).CONCLUSION:It is suggested that the log-normal regression model can be a useful statistical model to evaluate prognostic factors instead of the Cox proportional hazard model.

A Cox Proportional Hazard Model Approach to Age at First Sexual Intercourse in Nigeria

Early age at first sexual intercourse comes with many negative sexual outcomes namely: having unprotected sex on first sexual intercourse, condom misuse, high rate of sexually transmitted infections (STIs), teenage pregnancy, increased number of sexual partners, etc. In this paper, we considered some socio-demographic and cultural factors and their relationship with age at first sexual intercourse so as to reduce the numerous negative sexual outcomes of early age at first sexual intercourse using the 2018 Nigerian Demographic and Health Survey data. The analysis was made using the Cox proportional hazard model and the Kaplan-Meier plot. The result shows that some respondents started having their first sexual intercourse at the age of 8 years and about 54.4% of the respondents had their first sexual intercourse before age 17 years. The median age of first sexual intercourse is 16 years which implies that about 50% of the respondents had their first sexual intercourse on or before their 16th birthday. Education, religion, region and residence significantly affects the age of first sexual intercourse while circumcision has no significant effect.

Short-term and long-term risk factors in gastric cance

While in chronic diseases, such as diabetes, mortalityrates slowly increases with age, in oncological seriesmortality usually changes dramatically during thefollow-up, often in an unpredictable pattern. Forinstance, in gastric cancer mortality peaks in thefirst two years of follow-up and declines thereafter.Also several risk factors, such as TNM stage, largelyaffect mortality in the first years after surgery, whileafterward their effect tends to fade. Temporal trendsin mortality were compared between a gastric cancerseries and a cohort of type 2 diabetic patients. Forthis purpose, 937 patients, undergoing curativegastrectomy with D1/D2/D3 lymphadenectomy forgastric cancer in three GIRCG （Gruppo Italiano RicercaCancro Gastrico = Italian Research Group for GastricCancer） centers, were compared with 7148 type 2diabetic patients from the Verona Diabetes Study. Inthe early/advanced gastric cancer series, mortality fromrecurrence peaked to 200 deaths per 1000 personyears1 year after gastrectomy and then declined,becoming lower than 40 deaths per 1000 person-yearsafter 5 years and lower than 20 deaths after 8 years.Mortality peak occurred earlier in more advanced Tand N tiers. At variance, in the Verona diabetic cohort overall mortality slowly increased during a 10-yearfollow-up, with ageing of the type 2 diabetic patients.Seasonal oscillations were also recorded, mortalitybeing higher during winter than during summer. Alsothe most important prognostic factors presented adifferent temporal pattern in the two diseases： whilethe prognostic significance of T and N stage markedlydecrease over time, differences in survival amongpatients treated with diet, oral hypoglycemic drugsor insulin were consistent throughout the follow-up.Time variations in prognostic significance of main riskfactors, their impact on survival analysis and possiblesolutions were evaluated in another GIRCG series of568 patients with advanced gastric cancer, undergoingcurative gastrectomy with D2/D3 lymphadenectomy.Survival curves in the two different histotypes （intestinaland mixed/diffuse） were superimposed in the first threeyears of follow-up and diverged thereafter. Likewise,survival curves as a function of site （fundus vs body/antrum） started to diverge after the first year. On thecontrary, survival curves differed among age classesfrom the very beginning, due to different post-operativemortality, which increased from 0.5% in patients aged65-74 years to 9.9% in patients aged 75-91 years;this discrepancy later disappeared. Accordingly, theproportional hazards assumption of the Cox modelwas violated, as regards age, site and histology. Tocope with this problem, multivariable survival analysiswas performed by separately considering either thefirst two years of follow-up or subsequent years.Histology and site were significant predictors only aftertwo years, while T and N, although significant bothin the short-term and in the long-term, became lessimportant in the second part of follow-up. Increasingage was associated with higher mortality in the firsttwo years, but not thereafter. Splitting survival timewhen performing survival analysis allows to distinguishbetween short-term and long-term risk factors.Alternative statistical solutions could be to excludepost-operative mortality, to introduce in the modeltime-dependent covariates or to stratify on variablesviolating proportionality assumption.

Exploring the Impact of Factors Affecting the Lifespan of HIVs/AIDS Patient’s Survival: An Investigation Using Advanced Statistical Techniques

This study investigates the impact of various factors on the lifespan and diagnostic time of HIV/AIDS patients using advanced statistical techniques. The Power Chris-Jerry (PCJ) distribution is applied to model CD4 counts of patients, and the goodness-of-fit test confirms a strong fit with a p-value of 0.6196. The PCJ distribution is found to be the best fit based on information criteria (AIC and BIC) with the smallest negative log-likelihood, AIC, and BIC values. The study uses datasets from St. Luke hospital Uyo, Nigeria, containing HIV/AIDS diagnosis date, age, CD4 count, gender, and opportunistic infection dates. Multiple linear regression is employed to analyze the relationship between these variables and HIV/AIDS diagnostic time. The results indicate that age, CD4 count, and opportunistic infection significantly impact the diagnostic time, while gender shows a nonsignificant relationship. The F-test confirms the model's overall significance, indicating the factors are good predictors of HIV/AIDS diagnostic time. The R-squared value of approximately 72% suggests that administering antiretroviral therapy (ART) can improve diagnostic time by suppressing the virus and protecting the immune system. Cox proportional hazard modeling is used to examine the effects of predictor variables on patient survival time. Age and CD4 count are not significant factors in the hazard of HIV/AIDS diagnostic time, while opportunistic infection is a significant predictor with a decreasing effect on the hazard rate. Gender shows a strong but nonsignificant relationship with decreased risk of death. To address the violation of the assumption of proportional hazard, the study employs an assumption-free alternative, Aalen’s model. In the Aalen model, all predictor variables except age and gender are statistically significant in relation to HIV/AIDS diagnostic time. The findings provide valuable insights into the factors influencing diagnostic time and survival of HIV/AIDS patients, which can inform interventions aimed at reducing transmission and improving early diagnosis and treatment. The Power Chris-Jerry distribution proves to be a suitable fit for modeling CD4 counts, while multiple linear regression and survival analysis techniques provide insights into the relationships between predictor variables and diagnostic time. These results contribute to the understanding of HIV/AIDS patient outcomes and can guide public health interventions to enhance early detection, treatment, and care.

Exploring the Impact of Factors Affecting the Lifespan of HIVs/AIDS Patient’s Survival: An Investigation Using Advanced Statistical Techniques

This study investigates the impact of various factors on the lifespan and diagnostic time of HIV/AIDS patients using advanced statistical techniques. The Power Chris-Jerry (PCJ) distribution is applied to model CD4 counts of patients, and the goodness-of-fit test confirms a strong fit with a p-value of 0.6196. The PCJ distribution is found to be the best fit based on information criteria (AIC and BIC) with the smallest negative log-likelihood, AIC, and BIC values. The study uses datasets from St. Luke hospital Uyo, Nigeria, containing HIV/AIDS diagnosis date, age, CD4 count, gender, and opportunistic infection dates. Multiple linear regression is employed to analyze the relationship between these variables and HIV/AIDS diagnostic time. The results indicate that age, CD4 count, and opportunistic infection significantly impact the diagnostic time, while gender shows a nonsignificant relationship. The F-test confirms the model's overall significance, indicating the factors are good predictors of HIV/AIDS diagnostic time. The R-squared value of approximately 72% suggests that administering antiretroviral therapy (ART) can improve diagnostic time by suppressing the virus and protecting the immune system. Cox proportional hazard modeling is used to examine the effects of predictor variables on patient survival time. Age and CD4 count are not significant factors in the hazard of HIV/AIDS diagnostic time, while opportunistic infection is a significant predictor with a decreasing effect on the hazard rate. Gender shows a strong but nonsignificant relationship with decreased risk of death. To address the violation of the assumption of proportional hazard, the study employs an assumption-free alternative, Aalen’s model. In the Aalen model, all predictor variables except age and gender are statistically significant in relation to HIV/AIDS diagnostic time. The findings provide valuable insights into the factors influencing diagnostic time and survival of HIV/AIDS patients, which can inform interventions aimed at reducing transmission and improving early diagnosis and treatment. The Power Chris-Jerry distribution proves to be a suitable fit for modeling CD4 counts, while multiple linear regression and survival analysis techniques provide insights into the relationships between predictor variables and diagnostic time. These results contribute to the understanding of HIV/AIDS patient outcomes and can guide public health interventions to enhance early detection, treatment, and care.

Partial proportional odds model for analyzing pedestrian crashes,threshold heterogeneity by scale and proportional odds factor

Despite low traffic in Wyoming,pedestrian crash severity accounts for a high number of fatalities in the state.Thus this study was conducted to highlights factors contributing to those crashes.The results highlighted that drivers under influence,type of vehicle,location of crashes,estimated speed of vehicles,driving over the recommended speed are some of factors contributing to the severity of crashes.In this study,we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values.However,it has been argued that this assumption might be unrealistic,especially at the presence of extreme values.Thus,the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes,or proportional odds effects.In addition,we accounted for the spread rate,or scale,of the model’s latent distribution of pedestrian crashes.The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model.Comparisons were also made across standard normal,simple partial ordinal model,and partial ordinal accounting for scale heterogeneity.In addition,various potential threshold structures such as symmetric and flexible were considered,but similar goodness of fits were observed across all those models.Extensive discussion has been made regarding the formulation of the implemented methodology,and its implications.

Prediction models for development of hepatocellular carcinoma in chronic hepatitis B patients

Chronic hepatitis B(CHB)-related hepatocellular carcinoma(HCC)is a major health problem in Asian-Pacific regions.Antiviral therapy reduces,but does not completely prevent,HCC development.Thus,there is a need for accurate risk prediction to assist prognostication and decisions on the need for antiviral therapy and HCC surveillance.A few risk scores have been developed to predict the occurrence of HCC in CHB patients.Initially,the scores were derived from untreated CHB patients.With the development and extensive clinical application of nucleos(t)ide analog(s)(NA),the number of risk scores based on treated CHB patients has increased gradually.The components included in risk scores may be categorized into host factors and hepatitis B virus factors.Hepatitis activities,hepatitis B virus factors,and even liver fibrosis or cirrhosis are relatively controlled by antiviral therapy.Therefore,variables that are more dynamic during antiviral therapy have since been included in risk scores.However,host factors are more difficult to modify.Most existing scores derived from Asian populations have been confirmed to be accurate in predicting HCC development in CHB patients from Asia,while these scores have not offered excellent predictability in Caucasian patients.These findings support that more relevant variables should be considered to provide individualized predictions that are easily applied to CHB patients of different ethnicities.CHB patients should receive different intensities of HCC surveillance according to their risk category.

糖尿病肾脏疾病预后影响因素的COX比例风险回归模型分析

目的基于COX比例风险回归模型分析糖尿病肾脏疾病(diabetic kidney disease,DKD)预后的影响因素,为临床早期评估和防治DKD提供科学的依据。方法收集多中心(河南中医药大学第一附属医院、河南中医药大学第三附属医院、河南省中医院及郑州市人民医院)2010—2021年就诊治疗的DKD患者资料,包括一般资料、实验室指标、中医辨证分型及中医药治疗,以肾小球滤过率(eGFR)下降≥50%、血肌酐(Scr)翻倍、DKD 5期、死亡为结局指标,随访其发生时间及情况,利用COX比例风险回归模型分析筛选DKD预后影响因素。结果本研究最终纳入1947例患者,随访时间为4.65(4.557~4.746)年,发生复合终点事件305例(15.67%)。单因素COX回归分析结果表明,不同年龄、DKD病程≥4年、饮酒史、吸烟史、高血压、冠心病、脑血管病、糖尿病足、糖尿病眼病、糖化血红蛋白(HbA1c)、血尿酸(SUA)、Scr、24 h尿蛋白定量(24 h-UTP)、eGFR、湿热血瘀证、脾肾气虚兼湿聚血瘀证、脾肾阳虚兼血瘀证、中药熏洗、服用中药患者DKD预后情况比较,差异有统计学意义(P<0.05)。多因素COX回归分析结果表明,年龄[HR(95%CI)=2.52(1.92,3.30)]、DKD病程[HR(95%CI)=1.62(1.09,2.42)]、吸烟史[HR(95%CI)=1.61(1.13,2.29)]、饮酒史[HR(95%CI)=1.41(1.01,1.95)]、脑血管病[HR(95%CI)=1.33(1.01,1.75)]、糖尿病眼病[HR(95%CI)=1.43(1.13,1.82)]、SUA[HR(95%CI)=1.72(1.26,2.33)]、24 h-UTP[HR(95%CI)=1.48(1.14,1.91)]、脾肾气虚兼湿聚血瘀证[HR(95%CI)=2.22(1.44,3.43)]与DKD发生终点事件的风险关系密切(P<0.05),服用中药[HR(95%CI)=2.57(2.00,3.31)]能够减少终点事件的发生(P<0.05)。结论年龄、DKD病程、吸烟史、饮酒史、脑血管病、糖尿病眼病、SUA、24 h-UTP、脾肾气虚兼湿聚血瘀证是影响DKD患者预后的危险因素,而服用中药治疗为其保护因素。

激素治疗慢性肾脏病进展高风险IgA肾病患者的预后分析——一项回顾性研究

背景IgA肾病(IgAN)是世界范围内常见的原发性肾小球肾炎之一,目前糖皮质激素是否能改善慢性肾脏病(CKD)进展高风险IgAN患者肾脏预后尚无明确结论。目的本研究旨在探讨激素治疗对CKD进展高风险IgAN患者的治疗反应及肾脏预后的影响。方法回顾性纳入2017年1月—2021年10月于郑州大学第一附属医院就诊的CKD进展高风险IgAN患者。根据是否进行激素治疗将患者分为激素治疗组和支持治疗组,采用倾向匹配法按照性别、年龄、24 h尿蛋白定量、估算肾小球滤过率(eGFR)对患者进行1∶1匹配筛选病例,收集患者的临床及病理资料,记录患者治疗1年内疾病缓解情况及不良反应发生情况。以开始支持治疗的日期作为随访起点,随访至2022-10-31,主要终点事件定义为:进展为终末期肾脏病(ESRD)或接受透析治疗。复合终点事件定义为:eGFR较基线持续下降超过30%或进入ESRD或接受透析或患者死亡;运用Kaplan-Meier法绘制生存曲线,并采用Log-rank检验比较两组患者主要/复合终点事件累积发生率的差异。运用Cox比例风险回归分析探究影响CKD进展高风险IgAN患者预后的可能因素。结果共有236例原发性IgAN患者符合纳入标准,经过1∶1匹配,激素治疗组97例与支持治疗组97患者匹配成功,两组基线数据匹配均衡。激素治疗组患者完全缓解率、部分缓解率均高于支持治疗组(χ^(2)=6.171,P=0.013;χ^(2)=3.973,P=0.046)。中位随访时间为18.00(9.75,28.00)个月。Kaplan-Meier生存分析结果显示,激素治疗组的主要终点事件累积发生率低于支持治疗组(χ^(2)=4.495,P=0.034);激素治疗组的复合终点事件累积发生率低于支持治疗组(χ^(2)=4.419,P=0.036)。符合纳入标准的236例患者中有177例中等量蛋白尿患者,采用倾向匹配法按照性别、年龄、24 h尿蛋白定量、eGFR对激素治疗和支持治疗的177例患者进行1∶1匹配后,激素治疗中等量蛋白尿者和支持治疗中等量蛋白尿者各有76例患者匹配成功。Kaplan-Meier生存曲线结果显示,激素治疗中等量蛋白尿者的主要终点事件累积发生率低于支持治疗中等量蛋白尿者(χ^(2)=4.127,P=0.042);激素治疗中等量蛋白尿者的复合终点事件累积发生率低于支持治疗中等量蛋白尿者(χ^(2)=4.934,P=0.026)。多因素Cox比例风险回归分析结果显示血红蛋白(HR=0.982)、血肌酐(HR=1.019)、eGFR(HR=1.020)、24 h尿蛋白定量(HR=1.205)是影响CKD进展高风险IgAN患者发生主要终点事件的影响因素(P<0.05)。激素治疗组感染发生率高于支持治疗组(P<0.05)。结论在CKD进展高风险IgAN肾病患者中,与单纯支持治疗相比,激素治疗可以显著提升肾脏缓解率,降低肾功能下降、肾衰竭风;但仍需警惕其不良反应的发生。

先兆早期早产孕妇近期分娩的风险预测

目的:探索先兆早期早产(妊娠28~33+6周)孕妇近期(1周内)分娩的Cox比例风险预测模型的建立。方法:收集2021年1月至2022年12月川北医学院附属医院产科收治的293例妊娠28~33+6周先兆早期早产患者的临床资料进行回顾性分析。依据患者自住院后近期(1周内)是否分娩,分为分娩组(n=88)和未分娩组(n=205)。收集人口学资料、专科情况及临床生化指标,比较各观察指标在两组中的差异。采用Cox多因素分析筛选变量,再据此构建Cox比例风险预测模型,计算风险比(HR)。利用C-指数来检验模型的预测能力,绘制列线图可视化展示Cox比例风险预测模型,采用校准曲线来检验模型预测结果与实际情况的一致性。结果:分娩组平均年龄30.2±5.0岁,平均延长妊娠时间61.3±47.5 h。分娩组与未分娩组比较,患者入院时体质量指数(BMI)、产次>1次、患妊娠期糖尿病或糖尿病合并妊娠(GDM/PGDM)、胎方位异常、入院时有规律宫缩、入院时阴道清洁度≥Ⅲ度、入院时白细胞总数≥10×10^(9)/L及入院时子宫颈长度<20 mm的患者比例,差异有统计学意义(P<0.05)。多因素Cox回归分析,入院时BMI>30 kg/m^(2)(HR 2.178,95%CI 1.208~3.928,P=0.010)、产次>1次(HR 3.095,95%CI 1.759~5.447,P<0.001)、入院时有规律宫缩(HR 3.447,95%CI 2.216~5.362,P<0.001)、入院时子宫颈长度<20 mm(HR 3.594,95%CI 2.289~5.646,P<0.001)以及入院时白细胞总数≥10×10^(9)/L(HR 2.124,95%CI 1.352~3.335,P=0.001)是先兆早期早产孕妇近期(1周内)分娩的独立危险因素;将以上5个指标纳入预测模型,其C-指数为0.797(95%CI 0.750~0.844),提示模型的预测能力好。绘制校准图提示模型预测结果与实际结果的一致性较好。结论:入院时BMI>30 kg/m^(2)、产次>1次、入院时有规律宫缩、入院时子宫颈长度<20 mm以及入院时白细胞总数≥10×10^(9)/L的先兆早期早产孕妇易于近期内(1周内)发生早产,基于上述危险因素构建的Cox比例风险预测模型具有一定的准确度,可用于指导临床工作者提前采取相应的干预措施,避免或降低患者发生近期早产的可能,改善母婴结局。

急性硬膜下血肿患者围术期继发颅内高压的Cox比例风险预测模型的建立及评价

目的构建急性硬膜下血肿(ASDH)患者围术期继发颅内高压的Cox比例风险预测模型,并对其效能进行验证。方法回顾性收集78例ASDH患者的临床资料,按围术期继发颅内高压情况将其分为继发组(25例,围术期继发颅内高压)和对照组(53例,围术期未继发颅内高压)。比较2组患者人口统计学指标、合并症、临床生化指标及影像学资料等因素的差异。应用Cox比例风险模型对ASDH患者围术期继发颅内高压的可能影响因素进行多因素分析。建立ASDH患者围术期继发颅内高压的预测模型并测算Harrell′s C指数以评估模型预测的准确度。通过列线图及校准曲线评估该模型预测风险概率与实际风险概率的符合程度。结果全组半年随访率为89.74%(70/78)。继发组年龄、吸烟史、高血压病、糖尿病、术前格拉斯哥昏迷量表(GCS)评分、格拉斯哥预后量表(GOS)评分、复合型血肿、颅内血肿体积、平均动脉压、糖化血红蛋白(HbA1c)、国际标准化比值(INR)、白细胞介素-6(IL-6)、降钙素原(PCT)与对照组比较,差异有统计学意义(P<0.05)。年龄(OR=2.895,95%CI:1.264~6.633,P=0.022)、吸烟史(OR=2.146,95%CI:1.029~4.475,P=0.036)、GOS评分(OR=0.288,95%CI:0.112~0.741,P=0.015)、HbA1c(OR=3.325,95%CI:1.243~8.894,P=0.028)、INR(OR=2.746,95%CI:1.203~6.267,P=0.027)、PCT(OR=3.426,95%CI:1.335~8.795,P=0.019)是ASDH患者围术期继发颅内高压的独立影响因素,Harrell′s C指数为0.812(95%CI:0.789~0.872)。列线图及校准曲线显示实际风险与模型预测风险存在较好的一致性。结论急性硬膜下血肿患者的Cox比例风险模型预测围术期继发颅内高压风险的准确度高,适宜临床推广。

Uncertainties of landslide susceptibility prediction:Influences of different spatial resolutions,machine learning models and proportions of training and testing dataset

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.

南昌市抗病毒治疗艾滋病患者生存状况及影响因素分析

目的探讨南昌市抗病毒治疗艾滋病患者的生存状况及影响因素,为提高艾滋病抗病毒治疗工作的效果提供参考依据。方法从中国疾病预防控制信息系统-艾滋病综合防治信息系统中,选取2004年5月—2012年10月南昌市所有接受免费抗病毒治疗的符合纳入与排除标准的艾滋病患者599例为研究对象。收集患者基本情况（如性别、年龄、婚姻状况等）、患者临床相关资料（如感染途径、WHO临床分期、抗病毒治疗方案、机会性感染的种类、CD4＋细胞计数等）、患者的生存结局（分为继续治疗、死亡、停药、失访）、生存时间。采用寿命表法计算患者的生存率,采用Cox比例风险回归模型分析患者生存时间的影响因素。结果 599例患者感染途径以性传播〔473例（79.0%）〕为主;WHO临床分期以Ⅲ期〔232例（38.7%）〕、Ⅳ期〔191例（31.9%）〕为主;抗病毒治疗方案以3种抗病毒药物联用〔472例（78.8%）〕为主;机会性感染排在前3位的种类为持续或间断发热122例（20.4%）、结核病100例（16.7%）、持续腹泻（〉1个月）41例（6.8%）;CD4＋细胞计数为（125.8±105.1）个/μl。随访截止时,共有490例（81.8%）继续治疗,80例（13.4%）死亡,12例（2.0%）停药,17例（2.8%）失访;生存时间为（6.7±0.2）年,95%CI为（6.4,7.1）年。第1~5年累积生存率分别为90.24%、85.21%、82.80%、79.65%、76.92%。Cox比例风险回归分析结果显示,年龄、血源传播（与其他传播相比）、WHO临床分期、持续腹泻（〉1个月）、反复严重的细菌性肺炎、脑淋巴瘤或B细胞非霍奇金淋巴瘤是艾滋病患者生存时间的影响因素（P〈0.05）。结论南昌市抗病毒治疗艾滋病患者5年后生存率较为稳定。宜早发现、早诊断、早纳入抗病毒治疗;加强年龄较大者、血源传播者、WHO临床分期Ⅲ期及以上者、有机会性感染患者的治疗和管理,确保抗病毒治疗效果,有效提高艾滋病患者的生存率。

基于比例风险模型的环境折合系数确定方法

研究了环境因素对产品可靠性的影响,给出了一种综合利用变环境试验数据的环境折合系数确定方法.利用比例风险模型来描述可靠性与环境因素的关系,给出了环境因素对产品可靠性影响的定量度量.基于该模型给出了常用寿命分布下的环境折合系数的统计推断方法.针对复杂环境,采用广义比例风险模型用于度量不同环境因素之间对可靠性的交互作用.利用径向基函数法来拟合该模型,并对模型进行了优化.计算实例表明:该方法合理可行,便于工程应用.

局部肌层浸润性膀胱癌预后影响因素分析

目的:探讨影响局部肌层浸润性膀胱癌患者长期生存的预后因素方法:回顾性分析2002年1月至2011年6月新疆医科大学第一附属医院肿瘤中心收治的133例局部浸润性膀胱癌患者临床资料,选择年龄、合并症、肾积水、肿瘤大小、病灶数目、T分期、淋巴结转移、病理类型、肿瘤分级、治疗方式等10个对预后可能产生影响的因素,采用Kaplan-Meier法和Log-rank检验,对有意义的单因素进行Cox多因素分析。结果:随访4～114个月,全组患者1、3、5年生存率分别为77%、64%、52%。单因素分析显示T分期、淋巴结及肾积水对预后的影响有统计学意义(P<0.05)。Cox多因素分析显示影响局部肌层浸润性膀胱癌患者预后的因素分别为:T分期(RR=2.001,P=0.001)、淋巴结(RR=2.250,P=0.045)、肾积水(RR=1.954,P=0.047)。结论:肿瘤T分期。

基于Cox比例风险模型的电力电缆故障影响因素分析

电力电缆故障信息的深层次挖掘可提高对电缆故障影响因素的分析。因此,针对某供电公司10 k V电力电缆故障数据,运用统计学模型—Cox比例风险模型,定量分析了电缆故障影响因素,用以指导电缆采购、施工、运行和维护。为确保数据分析的准确性,提出了电缆数据预处理原则,探讨了合适的样本量大小。运用Cox比例风险模型对电缆故障影响因素进行单因素分析;运用Logistic回归模型确定了电缆故障影响因素类别,并统计计算了各电缆故障影响因素对应的电缆故障率,确定了各影响因素组成元素的相对危险程度,最终证明了Cox比例风险模型分析结果的正确性。结果表明:本体生产厂家M1、附件生产厂家N1、施工单位I3对应的电缆故障率最高分别为0.33、0.29、0.218,企业在进行电缆采购、施工、维护时应着重关注这3家单位。

230例手术治疗的口腔黏膜恶性黑色素瘤患者预后的多因素分析

目的:探讨影响口腔黏膜恶性黑色素瘤患者生存率的因素。方法:单纯手术治疗的口腔黏膜恶性黑色素瘤患者230例,男141例,女89例。TNM分期:Ⅰ期,34例;Ⅱ期,87例;Ⅲ期,109例。采用Cox比例风险模型进行预后因素筛选,Kaplan-Meier法进行影响因素及生存率的分析。Logrank法比较不同生存分布的差异。结果:肿瘤厚度、淋巴结转移、肿瘤有无溃破及原发部位是影响生存率的相对独立因素,其P值与RR值依次分别为:P<0.001、1.868,P<0.001、1.685,P<0.001、1.411,P=0.008、0.747。各因素不同水平间统计学差异明显。资料未显示年龄(P=0.136)、性别(P=0.721)及所用手术方法(P=0.944)对生存率有显著影响。结论:影响口腔黏膜恶性黑色素瘤的预后因素与皮肤部位发病者类似。各因素间无明显相互作用。单纯手术治疗并非最佳治疗措施。

江苏省初治结核病复发流行病学特征及影响因素

目的研究初治肺结核患者治疗成功后复发的流行病学特征及其影响因素,为制定和完善干预措施,减少结核病复发提供科学依据。方法采用回顾性队列研究方法,从结核病管理信息系统中导出江苏省重大传染病防治科技重大专项试点城市张家港、丹阳、泰兴三地2009-2018年患者病案信息,采用Kaplan-Meier法估计结核病复发风险,各组复发率之间比较采用Log-rank法检验,采用Cox比例风险模型研究肺结核复发的危险因素。结果纳入研究对象的8 704例成功治疗本地初治结核病患者中,截至观察终点共有410例(占4.71%)患者复发,复发率为1.02/100人年,其中77.32%的患者3年内复发,5年内累积复发占93.41%。单因素分析结果显示,男性(HR=1.385,95%CI:1.10~1.75,P=0.007)、40~岁组(HR=2.12,95%CI:1.117~4.032,P=0.022)和≥60岁组(HR=2.726,95%CI:1.446~5.140,P=0.002)、农民(HR=4.225,95%CI:1.884~9.476,P<0.001)、被动发现(HR=4.551,95%CI:2.261~9.164,P<0.001)、病原学阳性(HR=2.460,95%CI:2.007~3.015,P<0.001)、合并糖尿病(HR=3.028,95%CI:1.986~4.617,P<0.001)、单耐药(HR=3.456,95%CI:1.868~6.394,P<0.001)和诊断时长超过30天(HR=1.579,95%CI:1.205~2.069,P=0.001)患者复发率较高。多因素分析发现,合并糖尿病(aHR=2.698,95%CI:1.32~5.53,P=0.007)和耐药(aHR=3.496,95%CI:1.88~6.51,P<0.001)是结核病复发的独立危险因素。结论应加强结核病患者治疗成功后的随访和管理,以早期发现结核病复发并纳入规范化治疗,同时对结核病复发高危人群开展针对性干预措施。

利用变环境试验数据的可靠性综合评估

研究了试验环境对产品可靠性的影响,提出一种利用变环境试验数据的可靠性综合评估方法.利用Cox比例风险模型来描述产品的可靠性与试验环境因素的关系,给出不同环境因素对产品可靠性影响的定量度量.结合产品的可靠性模型提出一种数据折合方法.分别以指数分布和双参数Weibull分布为例,把不同环境条件下的试验数据折合为相同环境条件下的试验数据,进而利用这些试验数据对产品进行可靠性综合评估.该方法综合利用产品在不同环境条件下的试验数据,扩大了样本量,提高了产品可靠性评估精度.实例表明该方法合理可行,便于工程应用.

子宫内膜癌手术预后因素的多因素分析

目的:分析和评价子宫内膜癌常用临床病理因素(绝经前后、手术病理分期、组织学分级、淋巴结转移、病理类型、术后辅助治疗、肌层浸润深度)和生物学因素(COX-2、VEGF、KDR及ER、PR)对子宫内膜癌手术预后的影响,筛选出对子宫内膜癌手术预后最有显著的影响因素。方法:将可能与子宫内膜癌预后有关的常用临床病理因素和生物学因素进行单因素分析。通过CJox模型对单因素分析筛选出的影响子宫内膜癌预后的因素进行多因素分析。结果:137例子宫内膜癌患者中,通过随访,共获得确切随访者112例,5年生存率为83.04%。I～lV期5年生存率分别为89.66%,80.00%,53.85%,0。子宫内膜癌组织中CoX-2、VEGF及KDR蛋白的阳性率分别为47.32%、66.07%及52.68%,且三者表达具有协同性。CoX-2、VEGF、KDR蛋白阳性患者5年累积生存率分别为75.47%、78.38%、72.88%。将这些临床病理因素及生物学因素进行单因素分析,结果手术病理分期、组织学分级、肌层浸润深度、有无淋巴结转移、COX-2阳性表达、KDR阳性表达、ER阳性表达、PR阳性表达8个因素与预后明显相关(P均><0.05),绝经前后、病理类型、术后辅助治疗、VEGF阳性表达与预后无关(P均>0.05)。应用Cox模型对单因素分析筛选出的8个有显著意义的因素进行多因素分析表明:肌层浸润深度、手术病理分期、组织学分级是影响子宫内膜癌手术预后最显著的3个因素。结论:肌层浸润深度、手术病理分期、组织学分级是影响子宫内膜癌手术预后的因素,但浸润肌层是影响子宫内膜癌手术预后的最重要的因素。