简单介绍成组序贯设计在临床试验中的应用

在临床试验中，常常因为伦理或经济的原因需要进行期中分析，成组序贯设计是ICH-GCP推荐的期中分析方法。本文对该方法的原理及其在临床试验中的几个问题：期中分析的次数、检验界值、样本估算和终点指标的影响等进行了简单介绍。     

16排螺旋CT诊断早期中心型肺癌的临床应用价值探究

目的分析探讨临床上采用16排螺旋CT诊断早期中心型肺癌的作用及其应用价值。方法回顾性分析2013年4月-2014年5月收治的35例早期中心型肺癌患者临床资料,将16排螺旋CT诊断结果与病理学检查结果以及纤维支气管镜观察结果相比较,评价采用16排螺旋CT诊断早期中心型肺癌的临床价值。结果 35例患者采用16排螺旋CT诊断结果均为早期中心型肺癌阳性;临床病理学检测方法的结果与16排螺旋CT诊断结果相比差异无统计学意义（P〉0.05）。结论采用16排螺旋CT对早期中心型肺癌患者进行诊断具有较高的准确性,且属于无创检测,操作也较为方便。     

快速康复外科理念结合中西医护理在腰椎管狭窄症患者围手术期中的应用价值

目的分析在腰椎管狭窄症患者围手术期中采用快速康复外科理念与中西医护理联合护理的临床护理效果。方法选取我院2013年1年至2016年1月收治的15例腰椎管狭窄症患者进行分析,采用随机数字法将其平分为2组,其中7例采用常规护理的患者作为对照组,而另8例采用快速康复外科理念与中西医护理联合护理的患者为实验组,护理后将2组患者的临床治疗效果进行比较和分析。结果不同护理干预,相较于对照组,实验组患者的并发症发生率明显较低,而护理满意度明显较高,比较差异均P<0.05,形成了统计学意义。结论在腰椎管狭窄症患者围手术期中采用快速康复外科理念与中西医护理联合护理效果理想,降低了并发症发生率,在临床护理中值得应用和推广。     

用通路分析解读病理学习中的顺向迁移情况探讨

收集两个班 77个学生的正常组织学阅片成绩 ( X1 )与病理组织学阅片绘图成绩 ( X2 )和期中理论考试成绩 ( Y) ,以 X1与 X2 为原因变量 ,Y为应变量构筑框架模型 ,采用通路分析方法探讨原因变量与应变量间的关系 ,以推断学习迁移情况     

噻托溴铵对稳定期中、重度COPD患者肺功能和运动耐量的影响

目的观察噻托溴铵对稳定期中、重度COPD患者肺功能和运动耐量的影响,探讨其临床治疗稳定期中、重度COPD的疗效。方法对我院67例诊断为稳定期中、重度COPD患者进行随机分组用药干预研究,观察组采用噻托溴铵干粉吸入,对照组采用异丙托溴铵气雾剂吸入,对患者进行为期1个月的临床治疗。检测治疗前后两组患者的肺功能以及运动耐量的变化。结果与治疗前相比,两组患者的肺功能和运动耐量均有了一定的好转。观察组与对照组相比,前者肺功能和运动耐量的改善明显优于后者(P<0.01)。结论噻托溴铵干粉吸入对稳定期中、重度COPD患者有较好的临床疗效,较目前临床常用的托溴铵气雾剂有着更为可靠的改善肺功能及运动耐量的作用。     

期中分析在药物临床试验中的价值

期中分析是指在一个临床试验正式完成之前的任何时间内 ,为了比较组间的有效性和 或安全性而进行的分析。在新药开发中 ,期中分析被认为是一种有力的工具。期中分析的目的是及早终止试验。如果出现以下几种情况即可做出停止试验的决定 :所研究药物的有效性已清楚 ;预期的组间效应差异不可能达到 ;出现了无法耐受的药物不良反应。期中分析现已被广泛用于大规模的临床试验中 ,如果Ⅰ型错误或检验效能没有降低 ,且能比固定样本设计节省 30 %～ 4 0 %的样本量 ,能够有效控制偏倚 ,对保证临床试验结果的真实性起着重要的作用。     

中药质量标准的研究进展(下)

4 中药质量标准分析方法验证指导原则中药质量标准分析方法验证的目的,是证明采用的方法是否适合于相应的检测要求。在建立中药质量标准时,分析方法需经验证;在处方、工艺等变更或改变原分析方法时,也需对分析方法进行验证。需验证的分析项目有:鉴别试验、限量检查和含量测     

Ⅱ、Ⅲ期低位直肠癌术前放化疗的临床研究

目的探讨术前放化疗对局部晚期中低位直肠癌疗效和预后的影响。方法对81例保肛术前行术前放化疗及113例接受Miles手术后常规化疗的局部晚期中低位直肠癌患者的临床资料进行回顾性分析。结果术前放化疗术后总的病理完全缓解率为13.6%,降期率达61.7%,保肛率达77.8%。术前放化疗组和Miles手术组的局部复发率分别为9.9%和20.4%(P<0.05),5年生存率分别为69.1%和61.1%(P>0.05)。结论术前放化疗有利于提高局部晚期中低位直肠癌的保肛率,采用保肛术的局部复发率较Miles手术低,生存率和Mi-les手术相近。     

清洁验证中的分析方法验证

目的:论述如何验证清洁验证中所采用的分析方法.方法:制定验证方案,对方法的专属性、线性和范围、准确度、精密度、定量限等一一进行验证.结果:通过方法验证,确认可适用于清洁验证的分析方法.结论:采用经过验证的分析方法,是保证分析结果真实可靠的前提条件.清洁验证所采用的分析方法,同样也不能忽略方法验证.     

衍生化技术在酚类化合物分析中的研究进展

结合不同仪器分析方法对酚类化合物分析方法中衍生化技术的最新应用进展进行综述。具体介绍了酚类化合物分析方法中所采用的衍生化方式和条件,并对各种衍生化方法的特点进行了论述。     

Strategies for Including Patients Recruited During Interim Analysis of Clinical Trials

In clinical trials a periodical check of safety and efficacy data is often needed. For organizational reasons it is rarely desirable to stop a trial during such an interim analysis. Therefore, new study patients are included in the trial while the interim analysis is ongoing. Disregarding the additional information provided by these interim patients would be unsatisfactory, especially for an office of regulatory affairs. Consequently, the rules for group sequential or adaptive decisions must be adjusted to the recruitment of interim patients. In this paper, two strategies for modifying study designs to consider the analysis of interim patients are proposed.     

Strategies for including patients recruited during interim analysis of clinical trials

In clinical trials a periodical check of safety and efficacy data is often needed. For organizational reasons it is rarely desirable to stop a trial during such an interim analysis. Therefore, new study patients are included in the trial while the interim analysis is ongoing. Disregarding the additional information provided by these interim patients would be unsatisfactory, especially for an office of regulatory affairs. Consequently, the rules for group sequential or adaptive decisions must be adjusted to the recruitment of interim patients. In this paper, two strategies for modifying study designs to consider the analysis of interim patients are proposed.     

Optimal Timing for Interim Analyses in Clinical Trials

In clinical trials, interim analyses are often performed before the completion of the trial. The intention is to possibly terminate the trial early or adjust the sample size. The time of conducting an interim analysis affects the probability of the early termination and the number of subjects enrolled until the interim analysis. This influences the expected total number of subjects. In this study, we examine the optimal time for conducting interim analyses with a view to minimizing the expected total sample size. It is found that regardless of the effect size, the optimal time of one interim analysis for the early termination is approximately two-thirds of the planned observations for the O'Brien–Fleming type of spending function and approximately half of the planned observations for the Pocock type when the subject enrollment is halted for the interim analysis. When the subject enrollment is continuous throughout the trial, the optimal time for the interim analysis varies according to the follow-up duration. We also consider the time for one interim analysis including the sample size adjustment in terms of minimizing the expected total sample size.     

ON THE TIMING OF A FUTILITY ANALYSIS IN CLINICAL TRIALS

In recent years, many researchers have been interested in performing futility analyses at interim points during clinical trials in order to terminate futile trials. To implement such an analysis, the researcher must decide what percentage of the total number of patients must have completed the trial before the interim analysis is done. This percentage is usually between 20% and 80%. We examined the relationship between the percentage chosen and the probability of stopping the trial, given a parameter value associated with the treatment effect. The results of this examination will help the researcher plan when to do a futility analysis.     

On the timing of a futility analysis in clinical trials

In recent years, many researchers have been interested in performing futility analyses at interim points during clinical trials in order to terminate futile trials. To implement such an analysis, the researcher must decide what percentage of the total number of patients must have completed the trial before the interim analysis is done. This percentage is usually between 20% and 80%. We examined the relationship between the percentage chosen and the probability of stopping the trial, given a parameter value associated with the treatment effect. The results of this examination will help the researcher plan when to do a futility analysis.     

Sample size determination using an interim analysis

Interim analyses are often employed to terminate comparative clinical trials for ethical or economic reasons when the evidence indicates that one treatment is superior to the other. Here an interim analysis is proposed for the situation where a one-sided test is to be performed. The proposed interim analysis consists of a one-sided test to terminate the clinical trial if it appears that the null hypothesis of interest is true. By noting that incorporation of a single interim analysis is similar to the two-stage procedure used for constructing a test procedure with power independent of the unknown variance, it also includes estimation of the variance, which can be used to control the power of the test if the trial is not terminated. Various properties of this two-stage procedure and derivation of the constants needed for its implementation are presented.     

Accounting for Interim Safety Monitoring of an Adverse Event Upon Termination of a Clinical Trial

Upon termination of a clinical trial that uses interim evaluations to determine whether the trial can be stopped, a proper statistical analysis must account for the interim evaluations. For example, in a group-sequential design where the efficacy of a treatment regimen is evaluated at interim stages, and the opportunity to stop the trial based on positive efficacy findings exists, the terminal p-value, point estimate, and confidence limits of the outcome of interest must be adjusted to eliminate bias. While it is standard practice to adjust terminal statistical analyses due to opportunities to stop for “positive” findings, adjusting due to opportunities to stop for “negative” findings is also important. Stopping rules for negative findings are particularly useful when monitoring a specific rare serious adverse event in trials designed to show safety with respect to the event. In these settings, establishing conservative stopping rules are appropriate, and therefore accounting for the interim monitoring can have a substantial effect on the final results. Here I present a method to account for interim safety monitoring and illustrate its usefulness. The method is demonstrated to have advantages over methodology that does not account for interim monitoring.     

Accounting for interim safety monitoring of an adverse event upon termination of a clinical trial

Upon termination of a clinical trial that uses interim evaluations to determine whether the trial can be stopped, a proper statistical analysis must account for the interim evaluations. For example, in a group-sequential design where the efficacy of a treatment regimen is evaluated at interim stages, and the opportunity to stop the trial based on positive efficacy findings exists, the terminal p-value, point estimate, and confidence limits of the outcome of interest must be adjusted to eliminate bias. While it is standard practice to adjust terminal statistical analyses due to opportunities to stop for "positive" findings, adjusting due to opportunities to stop for "negative" findings is also important. Stopping rules for negative findings are particularly useful when monitoring a specific rare serious adverse event in trials designed to show safety with respect to the event. In these settings, establishing conservative stopping rules are appropriate, and therefore accounting for the interim monitoring can have a substantial effect on the final results. Here I present a method to account for interim safety monitoring and illustrate its usefulness. The method is demonstrated to have advantages over methodology that does not account for interim monitoring.     

Optimal Designs for Estimating the Interesting Part of a Dose-Effect Curve

We consider a dose-finding trial in phase IIB of drug development. For choosing an appropriate design for this trial the specification of two points is critical: an appropriate model for describing the dose-effect relationship, and the specification of the aims of the trial (objectives), which will be the focus in the present paper.

For many situations it is essential to have a robust trial objective that has little risk of changing during the complete trial due to external information. An important and realistic objective of a dose-finding trial is to obtain precise information about key parts of the dose-effect curve. We reflect this goal in a statistical optimality criterion and derive efficient designs using optimal design theory. In particular, we determine nonadaptive Bayesian optimal designs, i.e., designs which are not changed by information obtained from an interim analysis. Compared with a traditional balanced design for this trial, it is shown that the optimal design is substantially more efficient. This implies either a gain in information, or essential savings in sample size. Further, we investigate an adaptive Bayesian optimal design that uses different optimal designs before and after an interim analysis, and we compare the adaptive with the nonadaptive Bayesian optimal design. The basic concept is illustrated using a modification of a recent AstraZeneca trial.