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。     

期中分析方法在临床试验生存资料中的应用

目的：介绍成组序贯Logrank检验及I类错误率损耗函数期中分析方法在生存资料中的应用。方法：利用α损耗函数计算期中分析的界值，通过界值与标准化Logrank Wald统计量的关系作出统计结论。结果：序贯Logrank Score检验统计量类似独立、正态随机变量部分和，近似独立增加，其立差与失效数成比例。结论：标准化序贯Logrank Wald检验统计量分布结构类似立即应答变量，而针对后者已建立许多成熟的理论，其基本方法可扩展用于更复杂的临床试验生存资料期中分析。     

META-ANALYSIS AND META-ANALYTIC MONITORING OF CLINICAL TRIALS

Randomized trials are effective and usually unbiased for showing the average results in a selected outcome variable for treatment A versus treatment B, and meta-analyses produce an average of these averages. The results of both the trials and meta-analyses are often pragmatically unsatisfactory, however, because they do not reflect cogent distinctions desired by practising clinicians in the heterogeneous subgroups formed by diverse components in the patients baseline states, in proficiency of therapy, and in additional outcome phenomena. If the inadequacies of previous trials have led to performance of a suitable new trial, it should not be stopped by the numbers emerging from meta-analyses of prior non-pertinent results.     

THE ROLE OF META-ANALYSIS IN MONITORING CLINICAL TRIALS

The randomized clinical trial is the gold standard methodology for evaluating treatment interventions. The randomized trial is a prospective experimental study, the embodiment of the scientific method applied to the clinic. Both organizational and ethical considerations encourage the use of formal criteria for termination. Such criteria, typically based on an overall 5 per cent significance test, are frequently portrayed as guidelines, but none the less usually succeed in focusing the debate on termination decisions and thus facilitate decision making. The use of the 5 per cent criterion is entirely arbitrary and is not based in any way on optimality considerations. No credible model for optimizing the decision to terminate trials has been developed. Meta-analysis is an evolving technique for evaluating the totality of the evidence on general treatment strategies. It has enjoyed considerable success in influencing medical opinion and treatment practice in selected areas. Its role is in establishing proof of concept of general treatment strategies, and is thus a consensus builder, making use of data from trials which may be heterogeneous in the details of treatment administration and patient selection, but similar in conceptual intent. The level of evidence required for consensus is not well understood and cannot be defined precisely. The retrospective nature of meta-analysis and the risks of biased data acquisition mandate a conservative approach to data analysis and interpretation. Randomized trials and meta-analyses have distinct but complementary goals. Meta-analysis can be used productively in planning new clinical trials, and in supplying updated information to study monitors in the course of a trial. However, it is not advisable to employ meta-analysis in formal statistical procedures for terminating clinical trials.     

META-ANALYSIS IN THE DESIGN AND MONITORING OF CLINICAL TRIALS

Results from external studies often play an important role in many aspects of a clinical trial. Their incorporation into the decision making process of a trial, however, is rarely conducted in a formal manner. This conference will address what formal role, if any, meta-analytic summaries of external results should have in the design and monitoring of an ongoing trial. This introductory presentation describes in detail the example from obstetric research which motivated the conference topic, and, in a Bayesian framework, summarizes the general implications of formally incorporating meta-analytic results into the design and analysis of a new trial.     

SURVIVAL ANALYSES OF RANDOMIZED CLINICAL TRIALS ADJUSTED FOR PATIENTS WHO SWITCH TREATMENTS

Patients who switch treatment groups in randomized clinical trials can cause problems in the interpretation of the results. Although the intention-to-treat method is recognized as being the most reliable analysis, it may result in an underestimate of the treatment effect if there have been patients who switch treatments. In this paper, an adjusted analysis, based on a Cox model, is proposed which estimates the treatment effect allowing for patients who switch treatments. Because this analysis requires much weaker assumptions than ‘as treated’ analyses, it is likely to be much more reliable. The method is illustrated by a trial comparing radical radiotherapy with radiotherapy followed by cystectomy in the treatment of invasive bladder cancer.     

多发性硬化患者的临床特点分析

[目的]通过分析总结我院267例多发性硬化（MS）的临床资料，探寻MS的临床表现特点。[方法]从临床表现和影像学等方面对病例进行描述性分析。[结果]267例MS病例中，男110例，女157例；发病年龄5到70岁，平均40岁。小于15岁的早发型病例占4．5％；23．9％病例病前有诱发因素；77．5％病例呈急性或亚急性起病；首发症状以肢体无力、感觉障碍和视力减退最常见。本组头颅MR／以侧脑室周围、半卵圆中心、脑干等处多见。[结论]MS起病年龄与欧美相当，。以急性或亚急性起病；病变多累及视神经；临床类型和病程与神经功能缺失程度相关：     

AN OVERVIEW OF STATISTICAL METHODS FOR MULTIPLE FAILURE TIME DATA IN CLINICAL TRIALS

In a long term clinical trial to evaluate a new treatment, quite often each study subject may experience a number of ‘failures’ that correspond to repeated occurrences of the same type of event or events of entirely different natures during his/her follow-up period. To obtain efficient inference procedures for the therapeutic effect over time, it is desirable to utilize those multiple event times in the analysis. In this article, we review some useful procedures for analysing different kinds of multivariate failure time data. Specifically, we discuss the two-sample problems and the general regression problems with various survival models. We also give some recommendations of appropriate procedures for each type of multiple event data structure for practical usage. © 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 833–839 (1997).     

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。     

DEFINING,MONITORING AND COMBINING SAFETY INFORMATION IN CLINICAL TRIALS

Assessment of clinical trial safety data for industry, regulatory agencies, medical practitioners and patients requires definition and measurement, monitoring, and overall analysis. Prospective ‘safety’ definitions and reliable measurement tools reduce inefficient data collection and improve the validity of resulting analyses. Statistical tools can help investigators monitor safety data from controlled clinical trials and help improve post-marketing surveillance. Also, when evaluating overall safety, one needs to assess all available information by combining information from many trials and other sources. Planning for this combined assessment, incorporating flexibility to assess unanticipated yet important nuances in individual trials, may be more important than the actual statistical analysis method used. A keen awareness of the future needs of consumers of this infomation is quite important. Some current proposals to combine safety information will be discussed.     

TUTORIAL IN BIOSTATISTICS: BAYESIAN DATA MONITORING IN CLINICAL TRIALS

Many clinical trials organizations use regular interim canalyses to monitor the accruing results in large clinical trials. In disease areas such as cancer, where survival is usually a major outcome variable, ethical considerations may lead to a stipulated requirement for data monitoring of mortality. This monitoring has frequently taken the form of limiting interim analyses to be few in number, and specifying an extreme p-value of, for example, p<0⋅001 or p<0⋅01 as grounds for early termination of the trial. Group-sequential methods are also used. However, none of these approaches formally assesses the impact that the results of a clinical trial may have upon clinical practice. Thus a trial might be terminated early because of apparent treatment benefits, but might fail to influence sceptical clinicians to modify their future treatment policy. We discuss the application of Bayesian methods, including the use of uninformative, sceptical and enthusiastic priors, and demonstrate that the necessary calculations are both straightforward to perform and easy to interpret statistically and clinically. Methods are illustrated with interim analyses of a clinical trial in oesophageal cancer. © 1997 by John Wiley & Sons, Ltd.     

METHODOLOGY OF CLINICAL DRUG TRIALS

2nd revised English edition. Alain Spriet, Thérèse Dupin-Spriet and Pierre Simon, translated by Robert Coluzzi, John Young, Richard Edelstein and Michael Weintraub, Karger, Basel, 1994. No. of pages: 269. Price: $113.75. ISBN 3-8055-5856-2     

STATISTICAL REPORTING OF CLINICAL TRIALS WITH INDIVIDUAL CHANGES FROM ALLOCATED TREATMENT

We consider clinical trials in which information is available about subjects' treatment changes after randomization. To understand whether any difference between randomized groups in the intention-to-treat analysis can be explained by such treatment changes, we need analysis strategies which take account of treatment actually received. Selection bias is then a potentially serious problem. We relate risk in a time-dependent proportional hazards model to current treatment, with treatment combinations coded in two alternative ways. To reduce selection bias, treatment history (number of treatments dropped) and baseline covariates can be added to the model. Including current risk markers would also reduce selection bias but makes interpretation difficult. The methods are illustrated using data from the British Medical Research Council (MRC) elderly hypertension trial, with time to cardiovascular death as an outcome. Results for the comparison of diuretic and beta-blocker treatment are similar in all analyses, suggesting that selection bias is small and adding support to the hypothesis that the observed treatment differences are due to the randomized treatments themselves.     

多系统萎缩58例临床分析

[目的]分析多系统萎缩(MSA)的临床特征. [方法]回顾性分析58例临床诊断MSA患者的临床资料. [结果]男性36例,女性22例,发病年龄33～75(55.8±9.3)岁.30(51.7%)例以行走不稳、言语不清等小脑症状为首发症状,22(37.9%)例以自主神经功能障碍为首发症状.37(63.8%)例MSA患者出现3个以上系统同时受累.MSA患者中40(70%)例表现有小脑萎缩;MSA-P患者中4例表现为延髓、脑桥和小脑萎缩. [结论]MSA是一种累及神经系统多部位的变性疾病,男性多于女性,中老年起病,缓慢进展,首发症状以小脑症状最多,其次为自主神经功能障碍.多数患者出现3个以上系统同时受累.MRI表现多以小脑萎缩为主,MSA-P患者也可表现为延髓脑桥小脑萎缩,对MSA诊断有肯定意义.本病暂无特殊治疗,预后差,但有些药物可改善部分患者临床症状.     

CONTINUOUS AND GROUP SEQUENTIAL CONDITIONAL PROBABILITY RATIO TESTS FOR PHASE II CLINICAL TRIALS

We present continuous and group sequential designs for phase II clinical trials based on the sequential conditional probability ratio test (SCPRT). The SCPRT is derived from a conditional likelihood ratio, where the conditioning is on what the corresponding (reference) fixed sample size test (RFSST) would achieve. In other words, we obtain the sequential design by controlling the maximum probability that the SCPRT does not agree with the RFSST. We shall discuss the difference between SCPRT and stochastic curtailment which also uses the concept of conditional distribution. We show that the power function of the SCPRT is virtually the same as that of the RFSST and its average sample numbers (ASNs) are close to those of Wald's sequential probability ratio test (SPRT), whereas its maximum sample size is no greater than that of the RFSST. Thus the SCPRT has all the desirable properties, such as allowing the use of the RFSST at the last analysis, of the Fleming procedure for phase II trials. The SCPRT, however, preserves the power function of the RFSST better and gives us the option for continuous monitoring. Our recommendation, therefore, is to use a group SCPRT boundary (for interim analyses performed as scheduled) embedded in a continuous SCPRT boundary (for unplanned interim analyses and analyses at times based on data trends). We provide as well a bias-adjusted estimator of the success rate after sequential stopping. We illustrate the method with several examples. The method applies to any single-arm clinical trial with binary endpoints, such as the classic paired design.     

GROUP SEQUENTIAL MONITORING OF DISTRIBUTION-FREE ANALYSES OF REPEATED MEASURES

In many clinical trials the principal analysis consists of a 1 degree of freedom test based on an aggregate summary statistic for a set of repeated measures. Various methods have been proposed for the marginal analysis of such repeated measures that entail estimates of a measure of treatment group difference (the treatment effect) at each of K repeated measures and a consistent estimate of the covariance matrix, where asymptotically these estimates are normally distributed. One can then obtain an overall large sample 1-d.f. test of group differences, such as by taking the average of these K estimates. These methods include the Wei–Lachin family of multivariate rank tests and a corresponding multivariate analysis using the Mann–Whitney difference estimator as a measure of treatment group differences. Other methods, such as O'Brien's non-parametric test, are based on a single summary score for each patient, such as the within-patient mean value. These, and other such methods, allow for some observations to be missing at random. Herein I employ sequential data augmentation to conduct group sequential analyses using a 1 degree of freedom test from a multivariate Mann–Whitney analysis and for the O'Brien rank test. Su and Lachin used this method to perform group sequential analyses of a vector of Hodges–Lehmann estimators. By augmentating the data from the sequential looks in a single analysis, one obtains an estimate of the covariance of the estimates at each look, from which one obtains an estimate of the correlations among the sequential 1-d.f. test statistics. I describe a simple secant algorithm to determine the group sequential boundaries based on recursive integration of the standard multivariate normal distribution with the estimated correlation matrix. Although the boundary obtains readily using the method of Slud and Wei, the more flexible method of Lan and DeMets may be preferred. The true information fraction at each look, needed to apply the spending function method of Lan and DeMets, however, is unknown. Thus, I also describe the use of a surrogate measure of information. © 1997 by John Wiley & Sons, Ltd.     

POWER OF LOGRANK TEST AND COX REGRESSION MODEL IN CLINICAL TRIALS WITH HETEROGENEOUS SAMPLES

This paper evaluates the loss of power of the simple and stratified logrank tests due to heterogeneity of patients in clinical trials and proposes a flexible and efficient method of estimating treatment effects adjusting for prognostic factors. The results of the paper are based on the analyses of survival data from a large clinical trial which includes more than 6000 cancer patients. Major findings from the simulation study on power are: (i) for a heterogeneous sample, such as advanced cancer patients, a simple logrank test can yield misleading results and should not be used; (ii) the stratified logrank test may suffer some power loss when many prognostic factors need to be considered and the number of patients within stratum is small. To address the problems due to heterogeneity, the Cox regression method with a special hazard model is recommended. We illustrate the method using data from a gastric cancer clinical trial. © 1997 by John Wiley & Sons, Ltd.     

重度颅脑损伤合并多发性损伤57例临床分析

重度颅脑损伤合并严重多发性损伤因其损伤范围广，波及多个重要器官，失血量大，器官代偿能力减弱，在抢救过程中往往出现多种生理功能紊乱，病情变化快。伤后能否及时得到有效诊断和治疗，是抢救成功关键所在。三家医院自1991年1月一2007年3月共收治该类病人57例，现将治疗体会总结如下。