Log-Rank Test for Comparing Survival Curves of Neonatal Mortality Characteristic Groups in River Nile State-Sudan

This paper concerns the Log-rank test for comparing survival curves of neonatal mortality characteristic groups in River Nile State, Sudan. In this paper, log-rank test is used to compare two or more survival curves for the characteristics of newborn associated with newborn death after using Kaplan-Meier methods to estimate and graph survival curves for the variable of interest as (sex of newborn, weight of newborn, gestational age, mode of delivery and resident type), at the hospital of River Nile state—Sudan, with a sample size 700 of newborn in which the admission to the Neonatal Intensive Care Unit (NICU) of those hospitals during the period 2018-2020. In term of risk of death for newborn we found that 25% of sample study for newborns who were born in River Nile State-Sudan died. In addition, we conclude that after the log-rank statistics and Kaplan-Meier methods were applied, gender does not affect the newborn’s risk of survival, while the risk of survival increases when the birth weight is greater than 4.35 kg and the gestational age is greater than 42 weeks. There is no difference in the probability of survival for newborns whether the delivery is normal or cesarean. However, newborns are significantly more likely to survive in urban areas than in rural areas.

Juvenile survival curves in a solitary ground squirrel with a prolonged hibernation:effects of individual characteristics,environment,and maternal investment

Juvenile survival is a key life-history influence on population dynamics and adaptive evolution.We analyzed the effects of individual chara-cteristics,early environment,and maternal investment on juvenile survival in a large solitary hibernating rodent-yellow ground squirrel Spermophilus fulvus using Cox mixed-effects models.Only 48%of weaned pups survived to dispersal and 17%survived to hibernation.Early life expectancy was primarily determined by individual characteristics and,to a lesser extent,by the early environment.The strongest and pos-itive predictor of juvenile survival was body mass which crucially affected mortality immediately after weaning.Males suffered higher mortality than females after the onset of dispersal;however,the overall difference between sexes was partly masked by high rates of mortality in the first days after emergence in both sexes.Later emerged juveniles had lower life expectancy than the earliest pups.The overall effect of local juvenile density was positive.Prolonged lactation did not enhance juvenile survival:Pups nursed longer survived shorter than the young nursed for a shorter period.Our findings support the hypothesis that females of S.fulvus cannot effectively regulate maternal expenditures to mitigate the effects of unfavorable conditions on their offspring.The strategy to deal with seasonal time constraints on life history in female S.fulvus suggests an early termination of maternal care at the cost of juvenile quality and survival.This female reproductive strategy corresponds to a"fast-solitary"life of folivorous desert-dwelling S.fulvus and other solitary ground squirrels with prolonged hibernation.

Lutetium in prostate cancer: Reconstruction of patient-level data from published trials and generation of a multi-trial Kaplan-Meier curve

BACKGROUND Lutetium has been shown to be an important potential innovation in pre-treated metastatic castration-resistant prostate cancer.Two clinical trials have evaluated lutetium thus far(therap and vision with 99 and 385 patients,respectively),but their results are discordant.AIM To synthetize the available evidence on the effectiveness of lutetium in pre-treated metastatic castration-resistant prostate cancer;and to test the application of a new artificial intelligence technique that synthetizes effectiveness based on reconstructed patient-level data.METHODS We employed a new artificial intelligence method(shiny method)to pool the survival data of these two trials and evaluate to what extent the lutetium cohorts differed from one another.The shiny technique employs an original reconstruction of individual patient data from the Kaplan-Meier curves.The progression-free survival graphs of the two lutetium cohorts were analyzed and compared.RESULTS The hazard ratio estimated was in favor of the vision trial;the difference was statistically significant(P<0.001).These results indicate that further studies on lutetium are needed because the survival data of the two trials published thus far are conflicting.CONCLUSION Our study confirms the feasibility of reconstructing patient-level data from survival graphs in order to generate a survival statistics.

Kaplan-Meier生存曲线分析CEA、CA19-9在结直肠癌中的临床价值

目的研究癌胚抗原(CEA)、糖类抗原19-9(CAl9-9)在判断结直肠癌预后方面的临床意义。方法选择于2005年1月—2011年12月期间在泰山医学院附属医院住院治疗的结直肠癌患者共136例,定期随访,监测血清CEA、CA19-9,应用Kaplan-Meier生存曲线及相关生存分析研究二者与结直肠癌预后的相关性。结果血清CEA、CA19-9升高与结直肠癌出现复发或转移具有相关性;在预测结直肠癌根治术后复发或转移方面,联合检测CEA、CA19-9比单独检测二者更为灵敏;联合检测CEA、CA19-9可预测结直肠癌的不良预后。结论联合检测结直肠癌根治术后患者的血清CEA、CA19-9水平,可能成为预测复发或转移、判断结直肠癌无病进展生存期和总生存期的预测方法。

Quinone oxidoreductase 1 is overexpressed in gastric cancer and associated with outcome of adjuvant chemotherapy and survival

BACKGROUND Quinine oxidoreductase 1(NQO1)plays a vital role in protecting normal cells against oxidative damage and electrophilic attack.It is highly expressed in many solid tumors,suggesting a role in cancer development and progression.However,the role of NQO1 in gastric cancer and its effect on cancer development and prognosis have not been fully investigated.AIM To investigate the clinical relevance of NQO1 protein expression in gastric cancer and to explore the potential of NQO1 to serve as a prognostic biomarker and therapeutic target.METHODS In this retrospective study,gastric cancer specimens of 175 patients who were treated between 1995 and 2011 were subjected to immunohistochemistry analyses for NQO1.The correlation of NQO1 expression with gastric cancer prognosis and clinical and pathological parameters was investigated.RESULTS NQO1 protein was overexpressed in 59.43%(104/175)of the analyzed samples.Overexpression of NQO1 was associated with a significantly inferior prognosis.In addition,multivariate analysis suggested that NQO1 overexpression,along with tumor stage and patient age,are prominent prognostic biomarkers for gastric cancer.Moreover,NQO1 overexpression was correlated to a better response to 5-fluorouracil(5-FU)-based adjuvant chemotherapy.CONCLUSION NQO1 overexpression is associated with a significantly poor prognosis and better response to 5-FU in patients with gastric cancer.These findings are relevant for improving therapeutic approaches for gastric cancer patients.

Parametrization of Survival Measures (Part II): Single Arm Studies

In some clinical applications in oncology randomized, double armed, and double-blind trials are not possible. In case of device applications, double-blinded conditions are nonrealistic, and with many times the randomization also has complications due to the high-line treatments where the reference cohort is not available;the active “arm” has mainly palliative initiative. Sometimes highly personalized therapies block the collection of the homogeneous group and limit its double-arm randomization. Our objective is to discuss the situations of the single arm evaluation and to give methods for the mining of information from this to increase the level of evidence of the measured dataset. The basic idea of the data-separation is the appropriate parameterization of the non-parametric Kaplan-Meier survival pattern by the poly-Weibull fit.

Post-Hoc Comparison in Survival Analysis: An Easy Approach

Survival studies mainly deal with distribution of time to event. Often in such studies researchers are interested in comparing several treatment or prognostic groups. At the time of analysis, there is an unmeasured chance of making type I error, or finding a falsely significant difference between any two groups. The chance of making type I error is increased, if multiple groups are compared simultaneously. In this paper, survival analysis with Bonferroni correction is explained in easy way to cope up with this issue. The DLHS-3 data are taken to explain this methodology in the context of neonatal survival. Kaplan-meier plot with three survival comparison test is used to elaborate the application of Bonferroni correction.

Predictive formulas expressing relationship between dose rate and survival time in total body irradiation in mice

The Gompertz model is the long-time well-known mathematical model of exponential expression among mortality models in the literature that are used to describe mortality and survival data of a population. The death rate of the “probacent” model developed by the author based on animal experiments, clinical applications and mathematical reasoning was applied to predict age-specific death rates in the US elderly population, 2001, and to express a relationship among dose rate, duration of exposure and mortality probability in total body irradiation in humans. The results of both studies revealed a remarkable agreement between “probacent”-formula-predicted and published-reported values of death rates in the US elderly population or mortality probabilities in total body irradiation in humans (p - value > 0.995 in χ2 test in each study). In this study, both the Gompertz and “probacent” models are applied to the Sacher’s comprehensive experimental data on survival times of mice daily exposed to various doses of total body irradiation until death occurs with an assumption that each of both models is applicable to the data. The purpose of this study is to construct general formulas expressing relationship between dose rate and survival time in total body irradiation in mice. In addition, it is attempted to test which model better fits the reported data. The results of the comparative study revealed that the “probacent” model not only fit the Sacher’s reported data but also remarkably better fit the reported data than the Gompertz model. The “probacent” model might be hopefully helpful in research in human tolerance to low dose rates for long durations of exposure in total body irradiation, and further in research in a variety of biomedical phenomena.

Survival Analysis of Lung Cancer Patients from TCGA Cohort

Lung cancer is one of the leading causes of death worldwide, accounting for an estimated 2.1 million cases in 2018. To analyze the risk factors behind the lung cancer survival, this paper employs two main models: Kaplan-Meier estimator and Cox proportional hazard model [1]. Also, log-rank test and wald test are utilized to test whether a correlation exists or not, which is discussed in detail in later parts of the paper. The aim is to find out the most influential factors for the survival probability of lung cancer patients. To summarize the results, stage of cancer is always a significant factor for lung cancer survival, and time has to be taken into account when analyzing the survival rate of patients in our data sample, which is from TCGA. Future study on lung cancer is also required to make improvement for the treatment of lung cancer, as our data sample might not represent the overall condition of patients diagnosed with lung cancer;also, more appropriate and advanced models should be employed in order to reflect factors that can affect survival rate of patients with lung cancer in detail.

A MICROCOMPUTER PROGRAM FOR CALCULATINGTHE CONFIDENCE INTERVALS OF SURVIVAL PROBABILITY IN MEDICAL FOLLOW－UP STUDIES

In cancer survival analysis, it is very frequently to estimate the confidence intervals for survival probabilities.But this calculation is not commonly involve in most popular computer packages, or only one methods of estimation in the packages. In the present Paper, we will describe a microcomputer Program for estimating the confidence intervals of survival probabilities, when the survival functions are estimated using Kaplan-Meier product-limit or life-table method. There are five methods of estimation in the program (SPCI), which are the classical(based on Greenwood's formula of variance of S(ti), Rothman-Wilson, arcsin transformation, log(-Iog) transformation, Iogit transformation methods. Two example analysis are given for testing the performances of the program running.

Change-Point Analysis of Survival Data with Application in Clinical Trials

Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects’ failure rate. This paper focuses on the detection and estimation of such changes which is important for the evaluation and comparison of treatments and prediction of their effects. Unlike the classical change-point model, measurements may still be identically distributed, and the change point is a parameter of their common survival function. Some of the classical change-point detection techniques can still be used but the results are different. Contrary to the classical model, the maximum likelihood estimator of a change point appears consistent, even in presence of nuisance parameters. However, a more efficient procedure can be derived from Kaplan-Meier estimation of the survival function followed by the least-squares estimation of the change point. Strong consistency of these estimation schemes is proved. The finite-sample properties are examined by a Monte Carlo study. Proposed methods are applied to a recent clinical trial of the treatment program for strong drug dependence.

Survival Analysis of Logistics Service Providers: An Empirical Study of Chengdu, Area in China

This paper worked on a sample of 6791 logistics establishments registered in Chengdu, China over the period 1984-2016 to understand the survival status of logistics service providers (LSPs) by non-parametric Kaplan-Meier estimation, together with Cox proportional hazard regression model, to identify factors affecting the failure of LSPs. In particular, it studies the interaction effect between LSPs’ size and entry timing and location. The empirical results show that: 1) Regarding the survival time, 1365 of the 6791 sample LSPs exited from the market by 2017. The exit rate is 20.1%, and the average life of the 6791 LSPs is about 6 years. 2) The survival of LSPs depends on their typology, ownership structure. And there is no significant difference in the probability of survival for both independent LSPs and logistics branches after controlling the effects of other variables. 3) Location and entry timing also play an important role in the survival of small-scale LSPs, but these factors cannot explain large-scale LSPs’ failure.

Cox Proportional Hazard Model for Survival Time of Neonatal Mortality in Neonatal Intensive Care Unit of Hospitals in River Nile State-Sudan

Cox Proportional Hazard model is a popular statistical technique for exploring the relationship between the survival time of neonates and several explanatory variables. It provides an estimate of the study variables’ effect on survival after adjustment for other explanatory variables, and allows us to estimate the hazard (or risk) of death of newborn in NICU of hospitals in River Nile State-Sudan for the period (2018-2020). Study Data represented (neonate gender, mode of delivery, birth type, neonate weight, resident type, gestational age, and survival time). Kaplan-Meier method is used to estimate survival and hazard function for survival times of newborns that have not completed their first month. Of 700 neonates in the study area, 25% of them died during 2018-2020. Variables of interest that had a significant effect on neonatal death by Cox Proportional Hazard Model analysis were neonate weight, resident type, and gestational age. In Cox Proportional Hazard Model analysis all the variables of interest had an effect on neonatal death, but the variables with a significant effect included, weight of neonate, resident type and gestational age.

Survival Rate Analysis on Breast Cancer Cases at Univesity College Hospital, Ibadan, Nigeria

Breast cancer is one of the leading diseases that affect women’s lives. It affects their lives in so many ways by denying them the required standard of health needed to carry out all of their daily activities for some days, weeks, months or years before eventually causing death. This research estimates the survival rate of breast cancer patients and investigates the effects of stage of tumor, gender, age, ethnic group, occupation, marital status and type of cancer upon the survival of patients. Data used for the study were extracted from the case file of patients in the Radiation Oncology Department, University College Hospital, Ibadan using a well-structured pro forma in which 74 observations were censored and 30 events occurred. The Kaplan-Meier estimator was used to estimate the overall survival probability of breast cancer patients following their recruitment into the study and determine the mean and median survival times of breast cancer patients following their time of recruitment into the study. Since there are different groups with respect to the stages of tumor at the time of diagnosis, the log-rank test was used to compare the survival curve of the stages of tumor with considering p-values below 0.05 as statistically significant. Multivariate Cox regression was used to investigate the effects of some variables on the survival of patients. The overall cumulative survival probability obtained is 0.175 (17.5%). The overall estimated mean time until death is 28.751 weeks while the median time between admission and death is 23 weeks. As the p-value (0.000032) of the log-rank test for comparing stages of tumor is less than 0.05, it is concluded that there is significant evidence of a difference in survival times for the stages of tumor. The survival function plot for the stages of tumor shows that patients with stage III tumor are less likely to survive. From the estimated mean time until death for the stages of tumor, it was deduced that stage I tumor patients have an increased chance of survival. Types of cancer, gender, marital status, ethnic group, occupation and patient’s age at entry into the study are not important predictors of chances of survival.

江苏宝华山宝华玉兰种群结构与动态特征分析

为了揭示极危(CR)植物宝华玉兰〔Yulania zenii(W.C.Cheng)D.L.Fu〕野生种群的结构和动态特征,对江苏省句容市宝华山宝华玉兰野生资源进行全面调查,并运用静态生命表、存活曲线和生存分析等方法综合分析宝华玉兰种群结构和动态变化。结果表明:宝华山全境现存宝华玉兰野生开花植株115株,主要分布于宝华山北坡中部。宝华玉兰种群以中树和大树〔10 cm≤胸径(DBH)<25 cm〕为主,占比达66.1%,属于衰退型种群。从静态生命表看,宝华玉兰种群的生命期望随着径级的增加逐渐下降,种群存活曲线趋向于Deevey-Ⅱ型。生存分析结果显示:宝华玉兰种群趋势具有前期锐减、中期稳定、后期衰退的特点。种群动态量化分析结果表明:宝华玉兰种群动态总体呈增长趋势,但增长极为缓慢。基于上述研究结果,建议采取人工播种、补植实生苗等方式加强对宝华玉兰种群的保护和管理,促进其种群的更新和良性发展。

喉癌术后放疗患者复发影响因素及与不同放疗剂量的关系

目的:探讨喉癌术后放疗患者复发的影响因素、各术式下不同放疗剂量与复发的关系及对生存率的影响。方法:回顾性分析石家庄市人民医院收治的134例喉癌术后放疗患者临床资料,先对其进行单因素分析,后采用多因素Logistic回归分析术后复发的危险因素。同时探讨不同放疗剂量与各术式的关系,利用Kaplan-meler法、Log rank检验对喉癌患者2年生存率进行分析。结果:134例患者中,30例术后复发者设为复发组,其余104例为未复发组。经多因素Logistic回归分析可知,临床分期(Ⅲ~Ⅳ期)、T分期(T3~T4期)、N分期(N1~N3)、放疗剂量(低剂量)、甲状腺软骨受侵(是)、手术切缘(阳性)为喉癌术后复发的独立危险因素(OR>1,P<0.05)。支撑喉镜下激光声带肿物切除/喉部分切除术、喉全切除术后接受高剂量放疗患者复发率分别低于接受低剂量放疗患者,总复发率相比具有统计学差异(P<0.05)。截至2023年6月,134例喉癌术后放疗患者中,中位随访时间为14.23个月(1~24个月),其中低剂量组死亡8例,高剂量组死亡2例,中位生存时间估计值分别为19.13、22.13月,患者的生存曲线具有差异性(Log rank P=0.20),高剂量组优于低剂量组。结论:喉癌术后联合放疗的整体疗效较好,但临床分期、T分期、N分期、低剂量放疗、肿瘤侵及甲状腺软骨、手术切缘阳性、术后至放疗结束时间>11周因素均可影响疾病复发,临床上应当结合患者病情适当调高放疗剂量,以改善预后,延长生存期限。

极小种群野生植物狭叶坡垒种群结构与动态特征

【目的】研究极小种群野生植物狭叶坡垒[Hopea chinensis(Merr.) Hand.-Mazz.]的种群结构特征和数量动态特征,探讨种群发展潜力和濒危原因,为狭叶坡垒种质资源的保护和恢复提供参考依据。【方法】采用样地调查法对广西十万大山区域8个狭叶坡垒自然集中分布区进行调查,以空间代替时间分析狭叶坡垒种群结构特征,绘制种群静态生命表和存活曲线,分析种群动态指数及种群生存函数,对种群未来发展趋势进行预测。【结果】狭叶坡垒野生种群龄级结构呈倒J型,幼龄个体数占比较高,表现为增长型结构,种群个体幼龄期淘汰率较高;种群动态指数、随机干扰敏感指标和随机干扰风险概率分别为52.61%、4.38%和8.33%,对外界干扰敏感,环境对个体有较强的选择作用。狭叶坡垒野生种群存活曲线为DeeveyⅡ型的B1亚型,狭叶坡垒种群生存率在Ⅰ~Ⅵ龄级期间随龄级增加而减小,累积死亡率随龄级增加而增加,危险率在Ⅵ龄级达最高值,狭叶坡垒种群在Ⅵ龄级之后进入生理衰退期。时间序列分析表明,除Ⅻ龄级的个体数经过4个龄级时间后保持不变,经过4、6、8、10个龄级时间后,狭叶坡垒各龄级的个体数均呈增加趋势,其中Ⅳ~Ⅷ龄级逐渐成为种群的主体,狭叶坡垒种群总体呈增长状态。【结论】狭叶坡垒野生种群为增长型,种群处于波动变化状态中,对外界干扰敏感。幼苗数量充足,在生境不被破坏的前提下,具有较好的恢复潜力。但幼苗向幼树、幼树向小树转换过程中存在增补限制,制约狭叶坡垒种群发展。

血压升高与我国中年人群生存时间关系的前瞻性研究

目的 基于前瞻性队列探讨血压升高对我国中年人群生存时间的影响。方法 1982-1985年在全国10组35~59岁人群中进行心血管病危险因素调查并对死亡结局进行随访。采用生存曲线下面积法计算血压升高人群及非血压升高人群的平均生存时间并进行比较。将收缩压超过115 mmHg定义为血压升高。结果 纳入分析的研究对象共24096人,其中男性12161人,女性11935人,平均随访19.7年。血压升高人群与非血压升高人群相比,在总人群中,生存时间减少0.50岁;在男性人群中,生存时间减少0.46岁;在女性人群中,生存时间减少0.94岁;在城市人群中,生存时间减少0.63岁;在农村人群中,生存时间减少0.61岁;在35~44岁人群中,生存时间减少0.27岁;在45~54岁人群中,生存时间减少0.50岁;在55岁及以上的人群中,生存时间减少0.67岁。结论 血压升高对我国中年人群生存时间的影响女性大于男性,城市大于农村,年龄越大,影响越大。

基于TCGA数据集分析LINC00900在甲状腺乳头状癌中的表达及临床意义

目的研究甲状腺乳头状癌(PTC)中长链非编码RNA00900(LINC00900)的表达与患者临床病理特征及预后的关系。方法利用美国癌症基因组图谱(TCGA)数据库收集PTC数据资料,下载基因表达谱资料和PTC临床病例信息资料。比较PTC癌组织和癌旁甲状腺组织中LINC00900的表达。分析LINC00900表达与患者临床病理特征的关系。绘制Kaplan-Meier生存曲线分析LINC00900表达与PTC患者生存时间的关系。通过加权基因共表达网络分析(WGCNA)预测与LINC00900相关的蛋白编码基因(PCGs),利用注释、可视化和集成发现数据库(DAVID)在线注释工具对筛选出的PCGs进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)通路分析。结果LINC00900在PTC癌组织的表达水平显著高于癌旁正常甲状腺组织(P<0.001)。不同年龄、病理T分期、组织学类型PTC患者的LINC00900表达水平比较,差异均有统计学意义(P<0.05);≥55岁PTC患者LINC00900表达水平明显低于<55岁组(P<0.01);T3~T4期PTC患者的LINC00900表达水平明显低于T1~T2期(P<0.01);高细胞亚型PTC患者LINC00900表达水平低于经典型、滤泡型和其他型(P<0.01)。Kaplan-Meier生存曲线结果显示,LINC00900高表达组PTC患者的生存率明显高于低表达组(Log-rankχ^(2)=6.691,P<0.05)。功能富集分析结果显示,LINC00900可能通过核小体装配、mRNA的剪接、组蛋白H3甲基化的修饰、染色体绑定、组蛋白乙酰转移酶调节因子活性调节、鞘氨醇-1-磷酸磷酸酶活性调节、黏蛋白型O-聚糖生物合成等通路影响PTC的进展及预后。结论在PTC中,LINC00900高表达是一种预后保护因素,可作为辅助判断PTC预后的有效生物标志物之一。

蓖麻粕对黑水虻幼虫生长性能的影响

试验旨在探究蓖麻粕代替豆粕作为饲粮对黑水虻幼虫生长发育及其肠道微生物的影响。将黑水虻幼虫分为3组,分别是豆粕组(SM组)、50%豆粕和50%蓖麻粕组(SCM组)以及蓖麻粕组(CM组),每组3个重复,测定幼虫存活数、体重和体长等,检测分析幼虫和虫沙粗蛋白和粗脂肪含量、幼虫肠道细菌群落等变化。试验期12 d。结果显示,CM组虫沙的粗蛋白和粗脂肪含量显著低于其他两组(P<0.05)。16S核糖体(RNA)基因测序显示,蓖麻粕对幼虫肠道细菌的主要类型、丰富度和多样性无明显影响(P>0.05)。CM组的黑水虻幼虫生长更快,粗蛋白和粗脂肪含量显著高于SM组(P<0.05),而死亡率无显著差异(P>0.05)。研究表明,蓖麻粕代替豆粕作为饲粮更有利于黑水虻幼虫的生长。