固定点处k组生存率比较的非参数统计方法研究

目的当仅需比较某固定时间点上组间生存率的差异,或者不满足组间风险率成比例假设时,如生存曲线存在交叉,log-rank检验不再适用,且现有固定点检验法仅限于两组间,故本文发展固定点处多组间生存率的比较方法。方法首先提出多组间五种固定点检验法(经典法、对数转换法、双对数转换法、反正弦平方根转换法及逻辑转换法),并通过Monte Carlo模拟评价五种方法在不同情形下的一类错误和检验效能。最后对满足和不满足风险率成比例假设两个实例用上述方法进行分析。结果综合Monte Carlo模拟得到的一类错误及检验效能结果,以经典法和反正弦平方根转换法最为激进,对数转换法略保守,逻辑转换法最为保守,而双对数转换法最为稳健。结论在进行多组间生存率比较时,当仅想比较多组间某固定点处生存率差异或者组间不满足成比例假设,可使用上述五种固定点检验法,其中优先建议使用双对数转换法。     

基于两条生存曲线间面积的非参数统计推断方法研究

目的在生存数据组间比较研究中,当风险率成比例假设失效,特别是生存曲线交叉时,Log-rank检验的检验效能很低,本文介绍和研究一类无上述假设条件的检验法。方法首先介绍一种基于两条生存曲线间面积值的检验法,其次基于置换检验思想提出校正的置换面积检验法,并通过Monte Carlo模拟将上述两种方法与常用的Log-rank和加权Kaplan-Meier检验进行性能比较和评价。结果模拟结果显示,在I类错误上除面积检验法偏离较大外,其余检验法仅有轻微波动。在检验效能方面,风险率成比例假设满足时,Log-rank的检验效能最高;生存曲线交叉于早期时,面积检验和置换面积检验的检验效能最高;除此之外,置换面积检验法效能最高。结论当生存数据风险率成比例假设成立时,推荐Log-rank检验;但当该假设失效,特别是生存曲线出现交叉时,推荐使用置换面积检验法。     

2010―2019年宁波市胰腺癌生存率分析

目的 了解宁波市2010―2019年新发胰腺癌患者的生存情况，为胰腺癌的预后评价和防治提供参考。方法 2010―2019年胰腺癌发病资料来源于宁波市慢性病发病监测，死亡资料来源于死因监测，生存结局来源于定期随访，随访分主动随访和被动随访两种方式。随访截止时间为2020年12月31日。采用R 4.0.5计算观察生存率和相对生存率，绘制生存曲线并采用Log-rank检验进行比较，固定时点上的生存率比较采用双对数转换秩检验。结果 2010―2019年宁波市胰腺癌患者的5年观察生存率为8.91%,5年相对生存率为10.29%，男性患者的5年观察生存率为7.72%,5年相对生存率为9.06%，女性患者的5年观察生存率为10.65%,5年相对生存率为12.05%。男性观察生存率曲线和相对生存率曲线均低于女性（P<0.05）。中年组和老年组观察生存率曲线和相对生存率曲线均低于青年组（P<0.01）。不同亚部位胰腺癌中，胰头癌、胰体癌和胰尾癌的观察生存率曲线和相对生存率曲线均低于其他部位（P<0.01）。不同组织学类型中神经内分泌肿瘤的观察生存率曲线和相对生存率曲线均最高（P<...     

长期生存率比较的统计检验法

目的针对基于生存数据的长期疗效分析的问题,特别是两条生存曲线出现交叉或危险率不成比例的情况下,比较在特定时刻点后组间生存率的差异,本文主要介绍组合检验法Z_(OLS)、Z_(SP,p)和χ~2三种检验的适用情况。方法在两组危险率不满足比例风险假定的前提下,对生存曲线交叉和后期差异大的两个例子,分别使用Z_(OLS)、Z_(SP,p)和χ~2三种方法检验在不同时刻点之后组间生存率的差异。结果两个例子中,Z_(OLS)检验与χ~2检验对不同时刻点之后的组间生存率差异进行检验,均能得出较为一致的结论,并且与实际情况符合。而Z_(SP,p)检验的结果与前两种相比有差异。结论结合实例分析,在比较长期疗效或者生存曲线特定时间点后的生存率时,建议使用Z_(OLS)检验与χ~2检验。     

髂内动脉化疗栓塞联合鸦胆子油乳术前膀胱灌注对膀胱癌疗效观察

目的 观察骼内动脉化疗栓塞联合鸦胆子油乳术前膀胱灌注治疗浸润性膀胱癌的临床疗效.方法 59例浸润性膀胱癌患者随机分为单纯骼内动脉(吉西他滨、顺铂)化疗栓塞组(n＝27)及合并鸦胆子油乳术前膀胱灌注组(n=32),采用X2检验比较两组膀胱保留率、5a复发率,Kaplan-Meier法计算5 a总生存率并绘制生存曲线,Log-Rank检验进行两组间生存曲线比较.结果 单纯髂内动脉化疗组膀胱保留率63.0%,术后1～,2～、3～、5a～复发率分别为14.8%,25.9%,40.7%,51.9%,5a总生存率39.4%.髂内动脉化疗并鸦胆子组膀胱保留率87.5%,术后1～、2～、3～、5 a～复发率分别为9.4%15.6%,28.1%,40.6%,5a总生存率69.1%.两组间膀胱保留率、5a复发率、5a总生存率差别均有统计学意义(X2值分别为4.872,4.800,4.188,P<0.05).结论 髂内动脉化疗栓塞联合鸦胆子油乳术前膀胱灌注治疗浸润性膀胱癌疗效较好.     

限制平均生存时间在区间删失数据中的应用

目的针对区间删失生存数据的分析研究,提出限制平均生存时间(restricted mean survival time,RMST)的估计和两组比较检验。方法利用修正EM算法进行迭代并得到生存率估计值,并基于此估计值构建RMST检验统计量求得P值,通过Monte-Carlo模拟验证其统计性能。结果本文提出的区间删失生存数据中RMST检验法的I类错误在0.05附近波动,且其检验效能与现有常用方法Sun模型相当。结论针对区间删失生存数据的组间比较,本文提出RMST检验法不仅准确估计各组生存率,且通过计算RMST能够直观解释组间差异大小并由此作出统计推断,具有较好的统计性能,为临床研究者和病人提供决策依据。     

早期认知与平衡功能训练对青年脑卒中患者生活质量及生存率影响的研究

目的探讨早期介入认知功能训练结合平衡功能训练对青年脑卒中患者健康相关生活质量及生存率的影响。方法对符合我国第2次脑血管病会议制定诊断标准的132例小于45岁的青年脑卒中患者随机分为两组:观察组65例,即在常规治疗、护理的基础上在入院后36h内早期介入认知功能训练结合平衡功能训练;对照组67例,进行常规的治疗及护理;于入组时和半年后采用健康相关生活质量量表(MOS-SF36)对两组患者各调查评定1次;生存分析采用Kplan-Meier法绘制生存曲线,两组间生存时间的比较采用Log-rank检验。结果入组时两组青年脑卒中患者的生活质量(躯体功能及心理功能)得分无统计学差异,具有可比性(P0.05);随访6个月后生活质量评分,观察组:躯体功能(48.78±5.49)分;心理功能(47.79±5.56)分;对照组:躯体功能(41.32±4.88)分;心理功能(42.44±5.97)分;观察组得分明显高于对照组,差异有统计学意义(P0.001);随访6个月后,观察组死亡2人,常规护理治疗组死亡9人。Kplan-Meier法绘制生存曲线,观察组患者的生存率高于常规护理治疗组(P=0.029,P0.05)。结论早期介入认知功能训练结合平衡功能训练,有助于改善青年脑卒中患者的心理状态,提高患者的生活质量及生存率。     

1990-2018年云南省大理州HIV/AIDS生存分析

目的 分析1990-2018年云南省大理州HIV/AIDS生存状况及影响因素。方法 通过中国艾滋病综合防治数据信息系统收集1990-2018年现住址为大理州的10895 HIV/AIDS病例相关信息,应用寿命表法分析HIV/AIDS研究对象的生存率,采用乘积极限法（Kaplan-Meier法）计算研究对象的中位生存时间并绘制生存曲线。用Log-rank法比较治疗组与未治疗组的生存曲线。用Cox比例风险回归模型分析生存时间的影响因素。结果 10895例HIV/AIDS,全死因死亡3337例,中位生存时间15.58年（95%CI:14.83~16.5）。研究对象1、5、10、15、20年的累积生存率,抗病毒治疗组均高于未治疗组。根据不同防治措施分成1990-2004年、2005-2010年2011-2018年3个时期。相较于第一个时期,后两个时期5年生存率和7年生存率均有所升高而死亡风险均下降。Cox回归结果显示男性、初中及以下文化水平、样本来源于医疗机构、未进行抗病毒治疗、病程阶段是艾滋病、首次CD4水平低是研究对象死亡的危险因素。 结论 早发现,早治疗能降低HIV/AIDS患者死亡风险     

广州市2009年报告发病的鼻咽癌患者生存率及其影响因素分析

目的 分析广州市鼻咽癌患者的生存状况,为预后评价和防治提供依据。 方法 对2009年广州市肿瘤登记处的1 291例鼻咽癌的生存情况随访截止日期为2014-12-31,数据应用SPSS19.0进行统计分析,寿命表法计算观察生存,Log-rank检验比较组间肿瘤生存率,Cox回归模型进行多因素分析。 结果 广州市鼻咽癌患者1年、3年、5年生存率分别为85.80%、68.80%、57.89%,单因素分析显示不同性别、不同年龄、不同临床分期、不同治疗方法对鼻咽癌预后有影响,Cox多因素分析显示性别(OR=0.804)、分期(OR=1.458)、年龄分组(OR=1.338)、治疗方式(OR=1.466)是独立预后危险因素。 结论 广州市鼻咽癌患者生存率略高于全国平均水平,但低于中国香港,鼻咽癌防治应提高早诊率和采取有效的治疗方法。     

294例原发性甲状腺鳞状细胞癌治疗与生存情况分析

目的：分析不同治疗方式对原发性甲状腺鳞状细胞癌预后的影响。方法：对在中国知网检索到的1981-2011年间报道的294例原发性甲状腺鳞状细胞癌患者,采用Kaplan-Meieri法进行Log-rank生存分布检验,比较不同治疗方式下患者的生存率。结果：原发性甲状腺鳞状细胞癌中位生存期7个月,仅有27．1％的患者行甲状腺癌根治术;手术组1年生存率低于手术加放疗组,但3年生存率手术组高于手术加放疗组,整体上放疗组生存率高于未放疗组。结论：原发性甲状腺鳞状细胞癌预后差,手术是综合治疗的基础,术后放疗有助于获得短期的生存改善。     

Analyzing survival curves at a fixed point in time

A common problem encountered in many medical applications is the comparison of survival curves. Often, rather than comparison of the entire survival curves, interest is focused on the comparison at a fixed point in time. In most cases, the naive test based on a difference in the estimates of survival is used for this comparison. In this note, we examine the performance of alternatives to the naive test. These include tests based on a number of transformations of the survival function and a test based on a generalized linear model for pseudo-observations. The type I errors and power of these tests for a variety of sample sizes are compared by a Monte Carlo study. We also discuss how these tests may be extended to situations where the data are stratified. The pseudo-value approach is also applicable in more detailed regression analysis of the survival probability at a fixed point in time. The methods are illustrated on a study comparing survival for autologous and allogeneic bone marrow transplants.     

Permutation tests for comparing marginal survival functions with clustered failure time data

We propose a class of two-sample non-parametric permutation tests to compare the marginal survival distributions of two groups when the failure times are correlated within cluster, with clusters nested within each group. The permutation distribution effectively takes into account the correlation between failure times within a cluster. The method is able to handle data with clusters of either fixed or variable sizes. Moreover, this class of test statistics is sensitive to various alternatives. The size and power of the proposed tests are assessed by a series of simulation studies. The method is illustrated by application to data from the Hypertension Detection and Follow-up program trial.     

Power of tests for comparing trend curves with application to national immunization survey (NIS)

To develop statistical tests for comparing trend curves of study outcomes between two socio-demographic strata across consecutive time points, and compare statistical power of the proposed tests under different trend curves data, three statistical tests were proposed. For large sample size with independent normal assumption among strata and across consecutive time points, the Z and Chi-square test statistics were developed, which are functions of outcome estimates and the standard errors at each of the study time points for the two strata. For small sample size with independent normal assumption, the F-test statistic was generated, which is a function of sample size of the two strata and estimated parameters across study period. If two trend curves are approximately parallel, the power of Z-test is consistently higher than that of both Chi-square and F-test. If two trend curves cross at low interaction, the power of Z-test is higher than or equal to the power of both Chi-square and F-test; however, at high interaction, the powers of Chi-square and F-test are higher than that of Z-test. The measurement of interaction of two trend curves was defined. These tests were applied to the comparison of trend curves of vaccination coverage estimates of standard vaccine series with National Immunization Survey (NIS) 2000-2007 data.     

Comparison of median survival times with adjustment for covariates

Brookmeyer and Crowley derived a non-parametric confidence interval for the median survival time of a homogeneous population by inverting a generalization of the sign test for censored data. The 1 – α confidence interval for the median is essentially the set of all values t such that the Kaplan—Meier estimate of the survival curve at time t does not differ significantly from one-half at the two-sided α level. Su and Wei extended this approach to the two-sample problem and derived a confidence interval for the difference in median survival times based on the Kaplan-Meier estimates of the individual survival curves and a ‘minimum dispersion’ test statistic. Here, I incorporate covariates into the analysis by assuming a proportional hazards model for the covariate effects, while leaving the two underlying survival curves virtually unconstrained. I generate a simultaneous confidence region for the two median survival times, adjusted to any selected value, z , of the covariate vector using a test-based approach analogous to Brookmeyer and Crowley's for the one-sample case. This region is, in turn, used to derive a confidence interval for the difference in median survival times between the two treatment groups at the selected value of z . Employment of a procedure suggested by Aitchison sets the level of the simultaneous region to a value that should yield, at least approximately, the desired confidence coefficient for the difference in medians. Simulation studies indicate that the method provides reasonably accurate coverage probabilities.     

Combining stratified and unstratified log‐rank tests in paired survival data

The log‐rank test is the most widely used nonparametric method for testing treatment differences in survival between two treatment groups due to its efficiency under the proportional hazards model. Most previous work on the log‐rank test has assumed that the samples from the two treatment groups are independent. This assumption is not always true. In multi‐center clinical trials, survival times of patients in the same medical center may be correlated due to factors specific to each center. For such data, we can construct both stratified and unstratified log‐rank tests. These two tests turn out to have very different powers for correlated samples. An appropriate linear combination of these two tests may give a more powerful test than either of the individual test. Under a bivariate frailty model, we obtain closed‐form asymptotic local alternative distributions and the correlation coefficient between these two tests. Based on these results we construct an optimal linear combination of the two test statistics to maximize the local power. Simulation studies with Hougaard's model confirm our construction. We also study the robustness of the combined test by simulations. Copyright © 2010 John Wiley & Sons, Ltd.     

The economics of diagnosis

Any population can be divided into two groups, one with the presence of a given disease or condition, and the other without. Diagnosis consists of using tests to sort the population into these groups. Diagnostic tests use a threshold value of a diagnostic variable to distinguish between disease‐positive and disease‐negative individuals. The analysis of error in diagnostic tests has typically been undertaken using receiver‐operator characteristic (ROC ) curves. More recently, economic value of information (VOI ) methods have characterised the costs and consequences of testing. This paper develops a new method for economic test evaluation, which we call ROTS analysis. The ROTS curve plots the costs and effects of changing test thresholds, in cost‐effectiveness space. We illustrate the use of our method with a worked example, and show how it can answer three key questions: (1) Is there any test that is worth doing? (2) What is a test's optimum operating point in terms of sensitivity and specificity? (3) If two tests are available, which is best? We contrast the merits of our method with those of established ROC and VOI analysis. We argue that ROTS analysis more clearly reveals the link between changing test thresholds and the cost‐effectiveness of different treatments. Copyright © 2006 John Wiley & Sons, Ltd.     

Determining efficacy of monitoring devices: evaluating new technologies

We point out the connection between electronic monitoring and repeated screening tests, and suggest a flexible and practical method to determine the efficacy of monitoring devices and new technologies. We point out the benefits of the Mantel-Haenszel estimator of an odds ratio for determining diagnostic test indices. From estimates of sensitivities and specificities, we produce receiver operating characteristic (ROC) curves to compare the performance of two or more monitoring devices in a clinical setting.     

Restricted mean survival time for interval-censored data

Restricted mean survival time (RMST) evaluates the mean event-free survival time up to a prespecified time point. It has been used as an alternative measure of treatment effect owing to its model-free structure and clinically meaningful interpretation of treatment benefit for right-censored data. In clinical trials, another type of censoring called interval censoring may occur if subjects are examined at several discrete time points and the survival time falls into an interval rather than being exactly observed. The missingness of exact observations under interval-censored cases makes the nonparametric measure of treatment effect more challenging. Employing the linear smoothing technique to overcome the ambiguity, we propose a new model-free measure for the interval-censored RMST. As an alternative to the commonly used log-rank test, we further construct a hypothesis testing procedure to assess the survival difference between two groups. Simulation studies show that the bias of our proposed interval-censored RMST estimator is negligible and the testing procedure delivers promising performance in detecting between-group difference with regard to size and power under various configurations of survival curves. The proposed method is illustrated by reanalyzing two real datasets containing interval-censored observations.     

PDE4B基因过表达对弥漫大B细胞淋巴瘤患者的预后价值

目的 探讨磷酸二酯酶4B（phosphdiesterase 4B,PDE4B）基因过表达对弥漫大B细胞淋巴瘤（diffuse large B-cell lymphoma,DLBCL）患者预后的影响,并分析其预后价值。方法 从GEO（gene expression omnibus）数据库获得320 例DLBCL患者的基因芯片数据,比较PDE4B基因过表达与正常表达患者生存率的差异。构建单变量国际预后指数（international prognostic index,IPI）、PDE4B和多变量（IPI+PDE4B）Cox比例风险模型,并用时点/动态受试者工作曲线（receiver operating characteristic curves,ROC）法分析模型的预测能力随时间的变化趋势,一致性统计量C用于评价和比较各模型的预测准确度。结果 PDE4B基因过表达的患者死亡的风险是正常表达的1.431倍（HR=1.431,95% CI:1.013~2.002,P=0.042）。多变量模型的C统计量与单变量模型相比,差异有统计学意义（IPI vs. IPI+PDE4B:Z=15.0,P<0.001;PDE4B vs. IPI+PDE4B:Z=53.7,P<0.001）。结论 PDE4B基因过表达是DLBCL患者预后的危险因素,结合IPI和PDE4B基因能提高DLBCL的预后准确性。     

同一时点生命质量资料分析的TOPSIS法

本文较全面地介绍同一时点（横向）生命质量资料分析的TOPSIS法,将其推广于组间比较并对有关问题进行讨论。对两戒毒组生命质量进行的TOPSIS分析表明:两戒毒组生命质量在刚入戒毒所时总的看来无统计学差异。