Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.

A Comparison of Within-Subjects and Between-Subjects Designs in Studies with Discrete-Time Survival Outcomes

Crossover designs are well-known to have major advantages when comparing the effects of various non-curative treatments. We compare efficiencies of several crossover designs along with the Balaam’s design with that of a parallel group design pertaining to longitudinal studies where event time can only be measured in discrete time intervals. With equally sized sequences, the parallel group design results in the greater efficiency if the number of time periods is small. However, the crossover and Balaam’s designs tend to be more efficient as the study duration increases. The degree to which these designs add efficiency depends on the baseline hazard function and effect size. Additionally, we incorporate different cost considerations at the subject level when comparing the designs to determine the most cost-efficient design. Researchers might consider the crossover or Balaam’s design more efficient if the duration of the study is long enough, especially if the costs of applying the baseline treatment are higher.

Incidence and Survivability of Acute Lymphocytic Leukemia Patients in the United States: Analysis of SEER Data Set from 2000-2019

The main goal of this research is to assess the impact of race, age at diagnosis, sex, and phenotype on the incidence and survivability of acute lymphocytic leukemia (ALL) among patients in the United States. By taking these factors into account, the study aims to explore how existing cancer registry data can aid in the early detection and effective treatment of ALL in patients. Our hypothesis was that statistically significant correlations exist between race, age at which patients were diagnosed, sex, and phenotype of the ALL patients, and their rate of incidence and survivability data were evaluated using SEER*Stat statistical software from National Cancer Institute. Analysis of the incidence data revealed that a higher prevalence of ALL was among the Caucasian population. The majority of ALL cases (59%) occurred in patients aged between 0 to 19 years at the time of diagnosis, and 56% of the affected individuals were male. The B-cell phenotype was predominantly associated with ALL cases (73%). When analyzing survivability data, it was observed that the 5-year survival rates slightly exceeded the 10-year survival rates for the respective demographics. Survivability rates of African Americans patients were the lowest compared to Caucasian, Asian, Pacific Islanders, Alaskan Native, Native Americans and others. Survivability rates progressively decreased for older patients. Moreover, this study investigated the typical treatment methods applied to ALL patients, mainly comprising chemotherapy, with occasional supplementation of radiation therapy as required. The study demonstrated the considerable efficacy of chemotherapy in enhancing patients’ chances of survival, while those who remained untreated faced a less favorable prognosis from the disease. Although a significant amount of data and information exists, this study can help doctors in the future by diagnosing patients with certain characteristics. It will further assist the health care professionals in screening potential patients and early detection of cases. This could also save the lives of elderly patients who have a higher mortality rate from this disease.

Survival and comorbidities in lung cancer patients:Evidence from administrative claims data in Germany

Lung cancer is the most common cancer type worldwide and has the highest and second highest mortality rate for men and women respectively in Germany.Yet,the role of comorbid illnesses in lung cancer patient prognosis is still debated.We analyzed administrative claims data from one of the largest statutory health insurance(SHI)funds in Germany,covering close to 9 million people(11%of the national population);observation period was from 2005 to 2019.Lung cancer patients and their concomitant diseases were identified by ICD-10-GM codes.Comorbidities were classified according to the Charlson Comorbidity Index(CCI).Incidence,comorbidity prevalence and survival are estimated considering sex,age at diagnosis,and place of residence.Kaplan Meier curves with 95%confidence intervals were built in relation to common comorbidities.We identified 70,698 lung cancer incident cases in the sample.Incidence and survival figures are comparable to official statistics in Germany.Most prevalent comorbidities are chronic obstructive pulmonary disease(COPD)(36.7%),followed by peripheral vascular disease(PVD)(18.7%),diabetes without chronic complications(17.4%),congestive heart failure(CHF)(16.5%)and renal disease(14.7%).Relative to overall survival,lung cancer patients with CHF,cerebrovascular disease(CEVD)and renal disease are associated with largest drops in survival probabilities(9%or higher),while those with PVD and diabetes without chronic complications with moderate drops(7%or lower).The study showed a negative association between survival and most common comorbidities among lung cancer patients,based on a large sample for Germany.Further research needs to explore the individual effect of comorbidities disentangled from that of other patient characteristics such as cancer stage and histology.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

Survivals after liver transplantation for hepatocellular carcinoma:Granular data for a better allocation process?

To the Editor:A large international study has been recently published focusing on the combination of morphological aspects and alpha-fetoprotein(AFP)as predictors of survival in patients with hepatocellular cancer(HCC)treated with liver transplantation(LT)[1].As a matter of fact,morphology and biology represent the two sides of the same

Artificial Neural Network Model for Predicting Lung Cancer Survival

The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.

Analysis of Influencing Factors on Survival Time of Patients with Heart Failure

To explore the influencing factors of survival time of patients with heart failure, a total of 1789 patients with heart failure were collected from Shanghai Shuguang Hospital. The Cox proportional hazards model and the mixed effects Cox model were used to analyze the factors on survival time of patients. The results of Cox proportional hazards model showed that age (RR = 1.32), hypertension (RR = 0.67), ARB (RR = 0.55), diuretic (RR = 1.48) and antiplatelet (RR = 0.53) have significant impacts on the survival time of patients. The results of mixed effects Cox model showed that age (RR = 1.16), hypertension (RR = 0.61), lung infection (RR = 1.43), ARB (RR = 0.64), β-blockers (RR = 0.77) and antiplatelet (RR = 0.69) have a significant impact on the survival time of patients. The results are consistent with the covariates age, hypertension, ARB and antiplatelet but inconsistent with the covariates lung infection and β-blockers.

Long-term survival of early-stage non-small cell lung cancer patients who underwent robotic procedure:a propensity score-matched study

Background:In the past decade,many researchers focused on to robot-assisted surgery.However,on long-term outcomes for patients with early-stage non-small cell lung cancer(NSCLC),whether the robotic procedure is superior to video-assisted thoracic surgery(VATS) and thoracotomy is unclear.Nonetheless,in the article titled "Long-term survival based on the surgical approach to lobectomy for clinical stage I non-small cell lung cancer:comparison of robotic,video assisted thoracic surgery,and thoracotomy lobectomy" by Yang et al.that was recently published in Annals of Surgery,the authors provided convincing evidence that the robotic procedure results in similar long-term survival as compared with VATS and thoracotomy.Minimally invasive procedures typically result in shorter lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Main body:Our propensity score-matched study generated high-quality data.Based on our findings,we see promise in expanding patient access to robotic lung resections.In this study,propensity score matching minimized the bias involved between groups.Nevertheless,due to its retrospective nature,bias may still exist.Currently,the concept of rapid rehabilitation is widely accepted,and it is very difficult to set up a randomized controlled trial to compare robotic,VATS,and thoracotomy procedures for the treatment of NSCLC.Therefore,to overcome this limitation and to minimize bias,the best approach is to use a registry and prospectively collected,propensity score-matched data.Conclusions:Robotic lung resections result in similar long-term survival as compared with VATS and thoracotomy.Robot-assisted and VATS procedures are associated with short lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Considering the alarming increase in the incidence of lung cancer in China,a nationwide database of prospectively collected data available for clinical research would be especially important.

Evaluating Methods for Dealing with Missing Outcomes in Discrete-Time Event History Analysis: A Simulation Study

Background: In discrete-time event history analysis, subjects are measured once each time period until they experience the event, prematurely drop out, or when the study concludes. This implies measuring event status of a subject in each time period determines whether (s)he should be measured in subsequent time periods. For that reason, intermittent missing event status causes a problem because, unlike other repeated measurement designs, it does not make sense to simply ignore the corresponding missing event status from the analysis (as long as the dropout is ignorable). Method: We used Monte Carlo simulation to evaluate and compare various alternatives, including event occurrence recall, event (non-)occurrence, case deletion, period deletion, and single and multiple imputation methods, to deal with missing event status. Moreover, we showed the methods’ performance in the analysis of an empirical example on relapse to drug use. Result: The strategies assuming event (non-)occurrence and the recall strategy had the worst performance because of a substantial parameter bias and a sharp decrease in coverage rate. Deletion methods suffered from either loss of power or undercoverage issues resulting from a biased standard error. Single imputation recovered the bias issue but showed an undercoverage estimate. Multiple imputations performed reasonably with a negligible standard error bias leading to a gradual decrease in power. Conclusion: On the basis of the simulation results and real example, we provide practical guidance to researches in terms of the best ways to deal with missing event history data.

Construction and application of Markov model for followup data

Objective: To construct Markov model for clinical follow up data and predict the number of survivals and deaths. Methods: The state of patients was classified into survival. death and censorship. The weighted least squares method was used for estimating parameters. Results: Markov model for survival analysis of follow--up data was presented. By using an example, the transition probability matrices were obtained and the number of survivals and deaths at each observation point was predicted respectively. Conclusion: Markov model constructed in the present study to analyze clinical follow up data could be used as effective supplemention for life table analysis.

Estimation of actual carbon dioxide removal in burned forests using satellite data:A case study in South Korea

With the increasing impact of climate change,carbon emissions and removals have become major issues.Forests are major carbon pools,and forest fires are an essential part of the carbon cycle.This study introduces a model for estimating the detailed actual CO_(2)removal in burned forests using burn severity and tree survivability.Actual CO_(2)removal was estimated from empirical yield tables without using the standard carbon removal provided by the national inventory.The primary CO_(2)calculation method followed the guidelines of the International Panel on Climate Change.The burn severity was mapped using Sentinel-2 multispectral instrument data,and the survivability of each forest type was estimated according to burn severity.The survivability was applied to the pre-fire CO_(2)removal of each forest to estimate post-fire CO_(2)removal.In our case study,the burned forest area was 1,034 ha,and the CO_(2)removal before the fire was 8,615.3t/year.After the fire,removal decreased by 81.2%to 1,618.4 t/yr.In particular,the decrease in coniferous forests was high,more than 86%.The lack of survivability data on burned trees was a major limitation of our study.Systematically accumulating field monitoring data of post-fire forests will be necessary for future research and could serve as a reference for devising immediate countermeasures against forest fires.

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.

肺腺癌中HPRT1基因表达对患者总生存的影响

目的 探索HPRT1基因在肺腺癌中的表达特征、总生存率、功能激活以及免疫浸润中的影响。方法 通过对TCGA肺腺癌数据以及多个GEO数据库中的肺腺癌数据进行挖掘分析,对比并验证HPRT1表达量与预后总生存(overall surival,OS)的关系,通过cluster Profiler分析HPRT1基因高表达组中上调基因的功能富集情况。且通过TIMER以及CIBERSORT算法计算肺腺癌中不同免疫细胞的浸润水平并对比在HPRT1高低表达组间的浸润程度差异。结果 HPRT1在肺腺癌组织中表达量显著上调,且在TCGA中证明HPRT1基因高表达的患者预后OS更差(P <0.01)。经过2个GEO数据集的验证同样发现HPRT1基因高表达表现为预后OS更差(GSE13213,P <0.01;GSE67639,P <0.001)。差异分析显示高表达的患者中有683个基因的表达量显著上调且这些上调基因的功能主要富集与p53以及细胞周期等癌症相关的信号通路。TIMER以及CIBERSORT算法进行的免疫细胞浸润程度分析发现在高表达差预后人群中B细胞以及CD4T细胞含量均更低(P <0.05)。结论 HPRT1基因表达量越高肺腺癌患者的总生存越差,且高表达患者的p53信号通路上调,B细胞以及CD4T细胞浸润程度显著下降。

基于履历信息的民族地区基层公务员晋升研究——以广西壮族自治区L少数民族自治县为例

多层级的政府结构下,基层公务员晋升受到限制,“科级天花板”问题较为普遍。文章收集了广西壮族自治区L少数民族自治县的106位基层公务员的履历数据,对其进行深度编码,并引入生存分析法进行分析,以考察个人信息、教育经历和工作信息等因素对基层公务员职业阶段发展的影响。研究发现:基层公务员存在晋升关键期;基层公务员晋升注重党员身份和教育经历;在进入公务员体制一定时间后,会出现晋升瓶颈。基于研究结论,文章建议从评价体系、晋升渠道和创造条件三个角度完善晋升机制,以促进基层公务员职业发展。

基于多模态数据融合与机器学习的高价值专利早期识别方法

[研究目的]如何构建有效的高价值专利识别方法,从海量的专利中识别出高价值专利,对于高价值专利的培育、运用、保护和管理具有重要意义。[研究方法]提出一种基于多模态数据融合与机器学习的高价值专利早期识别方法。首先,利用多模态融合方法对专利指标数据和文本数据两种模态数据进行融合;其次,利用基于机器学习的生存预测算法对历史已知的高价值专利进行分析,并获取高价值专利融合后的特征与其“高价值”之间的关联模式;最后,利用获取到的关联模式,对新公开专利的“价值”进行预判,从而实现对高价值专利的早期识别。[研究结论]以人工智能领域专利为例进行实证研究,验证了该方法的可行性和有效性,为高价值专利早期识别提供了新的研究方法。

河北省体外膜氧合开展情况调查分析

目的调查近5年河北省体外膜氧合(ECMO)开展情况。方法河北省医师协会体外生命支持委员会于2024年1月1日至1月20日通过电话随访本省各地委员及各医院中心负责人,收集近5年ECMO开展情况。结果近5年ECMO开展中心数由8个上升到31个,年例数由65例上升到489例,ECMO机器数量由28台增加到100台。2023年开展ECMO的科室共计47个,其中急诊ICU有18个,ICU有21个,心内科ICU有3个,呼吸ICU有2个,心外科有3个。近5年呼吸支持和循环支持患者的住院生存率有下降趋势,2022年最低,2023年较2022年有所升高,体外心肺复苏(ECPR)患者的生存率总体有升高趋势。2023年不同适应证成人ECMO患者生存率偏低,呼吸支持患者的住院生存率为41.7%、循环支持为36.4%、ECPR患者为17.9%。2023年11个地市共开展ECMO 489例,开展最多的城市为274例,开展最少的城市为11例,ECMO生存率最高的城市为51.7%,生存率最低的城市为9%。结论近5年来河北省ECMO技术开展中心数和例数均较前明显增多,但仍存在各地区发展不平衡,生存率较国际及国内水平偏低,有待进一步完善三级医疗ECMO救治网络,加强培训与质量控制,从而推动河北省ECMO技术发展。

基于随机生存森林的供水管道漏损风险评估

供水管网漏损正得到越来越多的关注,管道漏损不仅造成水资源的浪费,还可能引发城市安全事故,而管道漏损风险评估是供水企业进行漏损控制的重要步骤。在供水管道的生存分析中,未发生漏损的管道被认为是删失数据,而在构建漏损风险评估模型时未漏损的管道数据同样十分重要。研究利用生存分析能够处理删失数据的特点,采用随机生存森林算法构建H市供水管网漏损风险评估模型。结果表明,该模型有良好的预测精度,模型C指数超0.75。并且模型能准确识别影响漏损的关键因素,变量重要性结果说明管道固有属性比环境变量对管道漏损的影响程度更大。研究进一步分析了在不同温度和降雨下漏损风险动态变化,将预测漏损风险分为5个等级,发现寒冷干燥气候下管网中漏损风险等级Ⅱ和Ⅲ级管道数量最多,说明供水管网在冬季干燥天气下发生漏损概率最大。模型还预测管道在未来不同管龄下的生存概率,通过设定生存概率阈值判定何时发生漏损,结果表明短期漏损问题尚未十分严重,但10年后H市供水管网中漏损管道数量将明显增多。该研究为供水企业提供了长短期的漏损风险预测,并考虑环境因素对漏损的影响,有助于制定更为精确的管道维护和管道更新策略。

多组学数据驱动的机器学习模型在乳腺癌生存及治疗响应预测中的应用

乳腺癌的高度异质性导致其治疗及预后评估较为复杂。治疗方案的选择受到肿瘤亚型、病变分级、基因型等多种因素的影响,因此需要制定个体化治疗策略。患者的预后效果因病情不同而产生显著差异。作为人工智能的一个重要分支,机器学习能高效处理海量数据,并实现决策过程的自动化。机器学习方法的引入将为乳腺癌治疗的选择和预后评估提供新的解决方案。在癌症治疗领域,传统方法预测生存与治疗效果往往依赖于单一或少量的生物标志物,难以全面捕捉复杂的生物学过程。机器学习通过分析患者的多组学数据以及它们在疾病发生发展过程中复杂的变化趋势,预测患者的生存和治疗响应效果,从而选择适合的治疗措施,实施早期干预,改善患者的治疗效果。本文首先介绍了常用的机器学习方法,在此基础上分别从评估生存情况和预测治疗效果这两方面展开,详细分析了机器学习在乳腺癌患者生存预测及预后领域中的应用,以期为乳腺癌患者提供精准医疗治疗策略,提高治疗效果和生存质量。

Conditional-quantile screening for ultrahigh-dimensional survival data via martingale difference correlation

Using the so-called martingale difference correlation(MDC), we propose a novel censoredconditional-quantile screening approach for ultrahigh-dimensional survival data with heterogeneity(which is often present in such data). By incorporating a weighting scheme, this method is a natural extension of MDCbased conditional quantile screening, as considered by Shao and Zhang(2014), to handle ultrahigh-dimensional survival data. The proposed screening procedure has a sure-screening property under certain technical conditions and an excellent capability of detecting the nonlinear relationship between independent and censored dependent variables. Both simulation results and an analysis of real data demonstrate the effectiveness of the new censored conditional quantile-screening procedure.