临床试验中多终点期中分析方法研究

[目的]介绍临床试验中多终点期中分析方法。[方法]在多终点分析的基础上将单变量成组序贯期中分析方法进一步扩展,介绍多终点(变量)期中分析方法。[结果]多终点期中分析方法可以分为2大类,分别对每个终点进行单变量分析,同时对每个终点的检验界值进行校正;或利用所有观察终点构造总的检验统计量进行分析。[结论]确定单变量成组序贯期中分析界值的方法可以扩展用于多终点期中分析。     

成组序贯Ⅰ类错误损耗函数期中分析方法

目的：介绍α损耗函数期中分析方法的基本原理。方法：结合实例说明期中分析方法的应用，以及最大样本数及信息部分的计算。结果；该法既可控制重复显著性检验的 I类错误率，又无需事先指定期中分析次数和时间。结论：该法具有很大的灵活性，信息部分将期中分析和第I类错误损耗函数建立联系，是每次期中分析的关键。     

期中分析方法在临床试验重复测量资料中的应用

目的：介绍成组序贯Ⅰ类错误率损耗函数期中分析方法在重复测量资料中的应用。方法：利用损耗函数计算期中分析的界值，通过界值与标准化检验统计量的关系作出统计学结论。结果：重复测量资料斜率比较时，利用时间部分计算期中分析时的Ⅰ类错误率及对应界值，利用实际信息计算序贯检验统计量之间的相关性。结论：重复测量资料斜率比较时，标准化检验统计量序列分布结构类似快速应答变量，针对后者已经建立许多成熟的理论，其基本方法可用于复杂的重复测量资料期中分析。     

分段α消费函数期中分析方法及其应用

传统的临床试验在试验结束后进行统计学分析,以了解某种治疗措施的疗效。1975年,美国Ｇｒｅｅｎｂｅｒｇ首先提出在临床试验过程中对中间结果进行检验,即期中分析〔1〕。经典的期中分析方法———成组序贯期中分析方法〔2,3〕在试验设计阶段要求的条件非常严格,所有结论都是建立在这些假定的基础上。在实际工作中,大量的临床实例及药物安全性评价过程并不一定满足这些条件,使用经典期中分析方法就会产生较大的误差,甚至出现错误的结论,因此,它的应用受到一定的限制。1981年ＬａｎＤｅｍｅｔｓ提出了α消费函数期中分析方法〔4〕,并给出五种…     

加速康复外科理念在腹腔镜胆囊切除围术期的应用研究

目的:探讨加速康复外科理念在腹腔镜胆囊切除患者围术期中应用的临床效果.方法:将100例择期腹腔镜胆囊切除患者分为加速康复组(实验组)50例,常规处理组(对照组)50例,对比分析2组患者术后肛门通气时间、进食、下床活动、并发症、住院时间、住院费用等有效指标.结果:实验组术后肛门通气时间、首次进食时间、下康活动时间提前,住院时间缩短,住院费用减少,两组比较有显著性差异(P＜0.05).结论:FTS理念指导的腹腔镜胆囊切除围术期处理有效地促进了患者术后快速康复.     

子宫内膜异位症患者血清及腹腔液中层粘连蛋白的变化及意义

目的:探讨子宫内膜异位症(内异症)患者血清及腹腔液中层粘连蛋白(LN)与内异症不同分期及内膜不同分期的关系。方法:采用放射免疫分析方法测定内异症患者血清及腹腔液中LN含量,并进行两两分析比较,血清及腹腔液中LN相关分析采用Pearson直线相关分析。结果:内异症组与对照组血清层粘连蛋白的比较差异无统计学意义(P>0.05),腹腔液中内异症组明显高于对照组,差异有统计学意义(t=4.216,P=0.000)。Ⅲ~Ⅳ期内异症患者血清及腹腔液LN水平明显高于Ⅰ~Ⅱ期,差异有极显著统计学意义(t=-2.985,P=0.004;t=-6.321,P=0.000)。无论在内异症组还是在对照组,血清及腹腔液中LN在不同内膜分期中比较差异无统计学意义。结论:LN在内异症的发生发展中起着重要作用。     

临床试验中多重性问题的统计学考虑

临床试验根据研究目的不同可分为"探索性临床试验"和"确证性临床试验",而临床研究结论通常需要根据确证性临床试验的统计推断结果得到。如果某一确证性临床试验需要对多个检验假设做出统计学推断,例如多个主要疗效指标的多重检验、多组间多重比较、多个时间点的期中分析(interim analysis)等情况     

食管癌手术患者围手术期中细节护理的应用分析

目的:对食管癌手术患者围手术期中细节护理的应用效果进行分析。方法:选择60例2017年1月至2018年1月期间在我院进行治疗的食管癌患者作为研究对象,分为常规组(30例)和护理组(30例),常规组于围术期给予常规护理,护理组在此基础上给予细节护理,比较两组患者各项临床指标(术毕至肛门排气时间、术毕至进食时间、住院时间)及护理满意度。结果:护理组患者各项临床指标显著优于常规组,差异对比具有统计学意义(P0.05);护理组护理满意度(93.33%)显效高于常规组(73.33%),差异对比具有统计学意义(P0.05)。结论:对食管癌手术患者围手术期中细节护理的应用效果显著。     

IVF-ET技术在子宫内膜异位症不孕症治疗中的应用

目的 :探讨体外受精 胚胎移植 (IVF ET)辅助生殖技术在子宫内膜异位症 (EMS)不孕症治疗中的应用。方法 :总结本生殖中心 2 0 0 2年 1月～ 2 0 0 4年 5月 5 3例EMS患者行体外受精 胚胎移植 /单精子胞浆内注射 (IVF ET/ICSI)共 5 7个周期为研究组。其中 13个周期为A组 ,给予GnRH α治疗后行IVF ET/ICSI ;另 44个周期为B组 ,未经任何治疗直接行IVF ET/IC SI。随机抽取同期行IVF ET病历 111个周期为对照组。比较研究与对照组在IVF ET治疗中的Gn使用量、获卵数、卵裂及受精情况 ,并对其成功率进行分析 ;对研究组中A、B组与对照组在IVF ET/ICSI治疗中的Gn使用量、获卵数、卵裂、受精情况及妊娠率进行比较。结果 :研究组 5 7个周期中获临床妊娠 19周期 (3 3 3 3 % ) ,对照组 111个周期中获临床妊娠 40周期 (3 6 0 4% ) ,两组比较无显著性差异 ;研究组中A组 13个周期获临床妊娠 6周期 (4 6 15 % ) ,B组 44个周期中获临床妊娠 12周期 (2 7 2 7% ) ,两组比较无显著性差异 ;A组Gn使用量明显大于对照组与B组。结论 :IVF ET技术是EMS不孕症患者获得生育的重要途径。     

延期妊娠对分娩结局及围产儿的影响分析

目的:分析延期妊娠对分娩结局及围产儿的影响,探讨相应的预防措施.方法:选择2011年1月～2012年12月的126例延期妊娠孕妇作为研究对象(观察组),并随机从同期中选取126例正常足月妊娠产妇作为对照组进行比较,记录比较两组患者在分娩方式、产后出血、胎儿窘迫、羊水污染、新生儿窒息、巨大儿方面的差异.结果:观察组在剖宫产、产后出血、胎儿窘迫、羊水Ⅱ～Ⅲ度污染、新生儿窒息、巨大儿产生方面显著高于对照组,差异具有统计学意义(P＜0.05).结论:延期妊娠容易引起产后出血、羊水污染,导致胎儿出现窘迫甚至窒息,并增加了巨大儿的出生率,因此应做好延期妊娠的预防和处理工作.     

A clinical trial with an interim analysis

We consider a fixed-sample parallel-group clinical trial with an interim analysis that tests H0:mu x = mu y against H1:mu x less than mu y. If we do not reject at the interim analysis, then the probability of making a type I error by rejecting H0 in favour of H2:mu x greater than mu y and the power at the final analysis are not appreciably affected by performing the interim analysis for certain relevant critical regions.     

Performance of adaptive sample size adjustment with respect to stopping criteria and time of interim analysis

The benefit of adjusting the sample size in clinical trials on the basis of treatment effects observed in interim analysis has been the subject of several recent papers. Different conclusions were drawn about the usefulness of this approach for gaining power or saving sample size, because of differences in trial design and setting. We examined the benefit of sample size adjustment in relation to trial design parameters such as 'time of interim analysis' and 'choice of stopping criteria'. We compared the adaptive weighted inverse normal method with classical group sequential methods for the most common and for optimal stopping criteria in early, half-time and late interim analyses. We found that reacting to interim data might significantly reduce average sample size in some situations, while classical approaches can out-perform the adaptive designs under other circumstances. We characterized these situations with respect to time of interim analysis and choice of stopping criteria.     

Interim monitoring of clinical trials based on long-term binary endpoints

This paper discusses interim analysis of randomized clinical trials for which the primary endpoint is observed at a specific long-term follow-up time. For such trials subjects only yield direct information on the primary endpoint once they have been followed through to the long-term follow-up time, potentially eliminating a large proportion of the accrued sample from an interim analysis of the primary endpoint. We advocate more efficient interim analysis of long-term endpoints by augmenting long-term information with short-term information on subjects who have not yet been followed through to the long-term follow-up time. While retaining the long-term endpoint as the subject of the analysis, methods of jointly analysing short- and long-term data are discussed for reversible binary endpoints. It is shown theoretically and by simulation that the use of short-term information improves the efficiency with which long-term treatment differences are assessed based on interim data. Sequential analysis of treatment differences is discussed based on spending functions, and is illustrated with a numerical example from a cholesterol treatment trial.     

Bayesian predictive power for interim adaptation in seamless phase II/III trials where the endpoint is survival up to some specified timepoint

Integration of a phase II and a phase III clinical trial into a single confirmatory study aims to shorten the development time without compromising the chance of success for a development program. These seamless phase II/III trials involve complex adaptations at the interim analysis, such as treatment selection, sample size reassessment, and stopping for futility. Bayesian methods can support these interim adaptations, and make this decision process more transparent. Use of a frequentist combination test for the final evaluation ensures that the type I error is controlled regardless of the adaptation rule employed at the interim analysis. In this paper, an adaptive seamless phase II/III trial design is proposed for studies where the endpoint is survival up to some specified timepoint and where Bayesian predictive power (PP) guides interim adaptations. For the evaluation of PP at the interim analysis, the event time is modelled as a piecewise exponential distribution, with informative priors for the hazard rates. As an illustrative example, regimen selection at interim in a four-arm trial with an active control is considered, where both non-inferiority and superiority to the control arm are tested. Frequentist properties of the adaptation criterion based on Bayesian PP are assessed by simulations.     

Estimation of treatment effects following a sequential trial of multiple treatments

When a clinical trial is subject to a series of interim analyses as a result of which the study may be terminated or modified, final frequentist analyses need to take account of the design used. Failure to do so may result in overstated levels of significance, biased effect estimates and confidence intervals with inadequate coverage probabilities. A wide variety of valid methods of frequentist analysis have been devised for sequential designs comparing a single experimental treatment with a single control treatment. It is less clear how to perform the final analysis of a sequential or adaptive design applied in a more complex setting, for example, to determine which treatment or set of treatments amongst several candidates should be recommended. This article has been motivated by consideration of a trial in which four treatments for sepsis are to be compared, with interim analyses allowing the dropping of treatments or termination of the trial to declare a single winner or to conclude that there is little difference between the treatments that remain. The approach taken is based on the method of Rao-Blackwellization which enhances the accuracy of unbiased estimates available from the first interim analysis by taking their conditional expectations given final sufficient statistics. Analytic approaches to determine such expectations are difficult and specific to the details of the design: instead “reverse simulations” are conducted to construct replicate realizations of the first interim analysis from the final test statistics. The method also provides approximate confidence intervals for the differences between treatments.     

Predictive probability methods for interim monitoring in clinical trials with longitudinal outcomes

In clinical research and development, interim monitoring is critical for better decision‐making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on‐going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on‐going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed‐form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed‐form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials.     

Application of sequential methods in randomized clinical trials with censored response criteria

In randomized clinical trials with a censored response criterion, it is common practice to perform interim analyses, especially for reasons of medical ethics. Sequential methods allow for repeated testing. Three sequential methods are well adapted to censored data: the group sequential analysis, the sequential probability ratio test and the triangular test. Among them, the triangular test has the best statistical properties. The influence of the frequency of the analyses on the statistical properties of sequential methods has been studied by simulation: sequential analyses need not be performed more frequently than interim analyses. Sequential methods have been applied to several randomized clinical trials. The results are briefly reported for two of them. The ability of the triangular test and of the sequential probability ratio test to reach conclusions early when there is no difference between two compared treatments is supported by these two examples. Moreover, careful patient follow-up must be planned in the protocol in order to reduce the time for data updating and to perform sequential analyses at the required frequency. It appears well-founded to propose the use of the triangular test in randomized clinical trials with a censored response criterion. Four types of analyses with different aims must be planned in the protocol.     

Repeated significance tests on accumulating survival data.

The aim of the study was to compare the properties of two well-known group sequential methods and to demonstrate the effect of performing interim analyses on accumulating survival data without making appropriate adjustments of the nominal significance level. The properties of a group sequential method with fixed nominal significance level (Pocock stopping boundaries) and a method with increasing nominal level with each interim analysis (O'Brien-Fleming boundaries) were compared by stochastic simulation. Simulation experiments with survival times sampled from a breast cancer trial and from exponential distributions were performed. The true overall significance level with unplanned interim analyses increased from 5% to 14% when a maximum of five tests were performed. Both group sequential methods maintained the desired overall significance level. The O'Brien-Fleming method had higher power than Pocock's method. It also reduced the risk of early stopping based on immature data and should usually be preferred.     

Bayesian monitoring of phase II trials in cancer chemoprevention.

Early randomized Phase II cancer chemoprevention trials which assess short-term biological activity are critical to the decision process to advance to late Phase II/Phase III trials. We have adapted published Bayesian interim analysis methods (Spiegelhalter et al., J. R. Statist. Soc A, 1994; 157: 357-416) which give greater flexibility and simplicity of inference to the monitoring of randomized controlled Phase II trials using intermediate endpoints. The Bayesian stopping rule is designed to stop the trial more quickly when the evidence suggests ineffectiveness rather than when it suggests biological activity, thus allowing resources to be concentrated on those agents that show the most promise in this early stage of testing. We investigate frequentist performance characteristics of the proposed method through simulation of randomized placebo controlled trials with a growth factor intermediate end-point using mean and variance values derived from the literature. Simulation results show expected error rates and trial size similar to other commonly used group sequential methods for this setting. These results suggest that the Bayesian approach to interim analysis is well suited for monitoring small randomized controlled Phase II chemoprevention trials for early detection of either inactive or promising agents.