临床试验中中心效应的评价及处理方法

探讨多中心临床试验中，中心效应的评价与处理方法，采用Breslow-Day检验对有效率的中心间差异进行评价，采用CMH方法对组间有效率及疗效等级进行分析，采用logistic回归方法对中心效应及有效率或疗效等级同时进行评价。结果提示，Breslow-Day检验只能对有效率的中心间差异进行评价。而不能对疗效等级的中心差异进行评价，CMH方法不能同时考虑其它协变量的影响。     

关于混杂效应、交互效应的探讨

目的:探讨在实际处理数据时对混杂效应、交互效应的处理方法。方法:通过具体实例说明混杂效应、交互效应的概念及正确应用。结果:判断变量间是否存在交互效应,需要在模型中纳入变量间的乘积项并通过统计学检验来评价;判断某变量是否为混杂因素,需比较模型中没有纳入该变量时得到的粗估计值与该变量纳入模型时得到的校正估计值的差别是否具有实际意义的不同,而不是通过统计学检验来评价;结论:在对数据进行统计分析时,当某变量可能与其它变量存在交互作用,同时又考虑其可能为混杂因素时,应先考虑其是否存在可能的交互作用,因为研究因素的效应随其他变量的取值不同而变化,如交互作用无统计学意义,进一步评价其是否为混杂因素。     

临床试验中率差及其置信区间的估计方法

目的探讨临床试验二分类数据率差及其置信区间的估计方法。方法分别介绍不存在协变量、只存在一个协变量、存在多个协变量三种情况时,率差及置信区间的估计方法,然后利用实例进行说明。结果当不存在协变量时,可用两组反应率估计值进行计算;当只存在一个分层因素时,针对不同情况可选择CMH、逆方差、最小风险加权方法进行估计;当存在多个协变量时,可结合logistic回归模型和delta方法、bootstrap方法计算。结论临床试验二分类数据率差及其置信区间的准确估计,需要结合试验设计,考虑是否需要校正协变量以及协变量的数量进而选择不同的统计方法。     

基于非线性混合效应模型法建立的拉莫三嗪群体药动学模型研究进展

目的 探讨拉莫三嗪在不同群体中的群体药动学特征及其影响因素,为建立更精准的群体药动学模型提供参考。方法 系统检索PubMed、Embase、Web of Science、Science Direct及Cochrane Library数据库中采用非线性混合效应模型(NONMEM)法在人体内进行的拉莫三嗪群体药动学的研究,时间为1995年1月—2021年7月。结果 共纳入研究20项,其中17项研究将拉莫三嗪的药动学特征描述为一室模型结构。伴随抗癫痫药物、体质量和基因多态性被认为是影响拉莫三嗪清除的3个最常见显著协变量,其他显著协变量研究较少。结论 在建立新的拉莫三嗪群体药动学模型时,应考虑合用其他抗癫痫药物、体质量、基因型等重要协变量因素;对于妊娠妇女等特殊人群,应纳入特殊人群的生理病理特征作为协变量因素,同时进行内部验证和外部验证增加模型的普适性。     

交叉设计多中心临床试验的混合效应模型

目的 :探讨交叉设计多中心临床试验资料的分析方法。方法 :采用混合效应的一般线性模型和混合效应的广义线性模型。结果 :将个体作为随机效应来估计时 ,可以增加误差自由度 ,提高估计精度 ,同时可以考虑中心效应、患者的年龄、性别、基线等协变量的影响 ,并且对于缺失数据在不丢失信息的情况下照样能进行分析。结论 :在交叉设计多中心临床试验资料的分析中 ,混合效应的一般线性模型适用于连续性结果变量的分析 ,混合效应的广义线性模型适用于分类结果变量的分析。     

药物个体效应及其图形化评价

目的通过对个体效应进行模型化定量描述并予图形化评价,以便于直观理解,指导个体化用药。方法用数学模型剖析个体效应与个体差异的构成,与此相关的协变量效应和随机误差同时涉及,同时予以图形化表达。结果个体效应由群体效应、协变量效应、个体间随机效应和个体内随机效应组成。目前的数学方法已将此区分,并用参数定量表达;其过程需要各类图形法作为工具协助,同时结果也可用不同类型图形表达。结论获取个体效应是为了个体化用药,对此概念的充分认识和定量表达,需借助模型化手段和图形化工具。     

ELISA法检测乙肝表面抗原出现假阳性结果分析

目前临床检测乙肝表面抗原（HBsAg）常用的方法为酶联免疫吸附试验（ELISA），该试验方法自1971年间世以来，以敏感度高、特异性强、操作简便、记录易保存等特点，被广泛应用。但由于ELISA法操作过程中要求较高，一旦操作不规范，常常会出现拖带污染，从而导致假阳性结果。     

化学致畸物危险度评价中的Nested logistic模型及其应用

评价化学致畸物的危险度时，定量方面很重要的是对致畸实验数据进行拟合，得到该致畸物的剂量-反应模型及其曲线，并确定该致畸物的基准剂量值（Bmd）。致畸实验数据有一个固有特点——窝别效应，即相对于不同窝出生的仔畜，同窝出生仔畜的反应更趋于相似。1986年Kupper等将窝别效应考虑进模型，并把窝仔数作为协变量引入模型，使窝仔之间成为条件性的独立，也去除了不同窝仔数的影响，使得模型模拟结果更准确，更合理。巢式模型是化学致畸物危险度评价模型中的一种，专为解决致畸实验中窝别效应的问题而设计。     

关于新药临床试验中控制混杂因素的几点意见

目的:通过探讨新药临床试验总结分析阶段几种控制混杂因素的统计方法,强调控制混杂因素对于新药临床试验的重要性.方法:通过实例说明几种方法的用途及注意事项,阐述各种方法控制中心效应、协变量等对主要结果指标的影响.结果与结论:针对资料的特点,选用适合的统计学方法控制混杂因素对主要结果指标分析的影响,尽可能使药物疗效真实的呈现.     

用SAS软件实现高维列联表资料的统计学分析：CMH x2检验

CMH统计分析是在MH统计分析方法的基础上发展并提出的,用于分层分析,即控制非试验因素后对保留下来的由原因变量与结果变量构成的2×2、R×2、2×C、R×C二维列联表资料的统计学处理.这种方法可以控制一个或多个原因变量,而考察关注的一个原因变量对结果变量的影响.本文结合实例,对高维列联表资料和用SAS软件进行统计学处理的方法进行了介绍,并给出了结果解释.     

Nonparametric analysis of covariance for hypothesis testing with logrank and Wilcoxon scores and survival-rate estimation in a randomized clinical trial.

Many clinical trials have time-to-event variables as principal response criteria. When adjustment for covariates is of some importance, the relative role of methods for such analysis may be of some concern. For the Wilcoxon and logrank tests, there is an issue of how covariance adjustment can be nonparametric in the sense of not involving any further assumptions beyond those of the logrank and Wilcoxon test. Also of particular interest in a clinical trial is the estimation of the difference between survival probabilities for the treatment groups at several points in time. As with the Wilcoxon and logrank tests, there is no well known nonparametric way to incorporate covariate adjustment into such estimation of treatment effects for survival rates. We propose a method that enables covariate adjustment for hypothesis testing with logrank or Wilcoxon scores. Related extensions for applying covariate adjustment to estimation of treatment effects are provided for differences in survival-rate counterparts to Kaplan-Meier survival rates. The results represent differences in population average survival rates with adjustment for random imbalance of covariates between treatment groups. The methods are illustrated with a clinical trial example.     

Randomization-based adjustment of multiple treatment hazard ratios for covariates with missing data

ABSTRACT

Clinical trials are designed to compare treatment effects when applied to samples from the same population. Randomization is used so that the samples are not biased with respect to baseline covariates that may influence the efficacy of the treatment. We develop randomization-based covariance adjustment methodology to estimate the log hazard ratios and their confidence intervals of multiple treatments in a randomized clinical trial with time-to-event outcomes and missingness among the baseline covariates. The randomization-based covariance adjustment method is a computationally straight-forward method for handling missing baseline covariate values.     

Clinical trials: how to assess confounding and why so

Background: In large randomized controlled trials the risk of random imbalance of the covariates is mostly negligible. However, with smaller studies it may be substantial. In the latter situation assessment and adjustment for confounders is a requirement in order to reduce a biased assessment of the treatment comparison. Objective: In the current paper three methods for confounding assessment and adjustment are reviewed for a nonmathematical readership. Methods: First method, subclassification: the study population is divided into subclasses with the same subclass characteristic, then, treatment efficacy is assessed per subclass, and, finally, a weighted average is calculated. Second method, regression modeling: in a multivariable regression model with treatment efficacy as independent and treatment modality as dependent variable, the covariates at risk of confounding are added as additional dependent variables to the model. An analysis adjusted for confounders is obtained by removing the covariates that are not statistically significant. Third method, propensity scores: each patient is assigned several odds ratios (ORs), which are his/her probability, based on his/her covariate value of receiving a particular treatment modality. A propensity score per patient is calculated by multiplying all of the statistically significant ORs. These propensity scores are, then, applied for confounding adjustment using either subclassification or regression analysis. Conclusions: The advantages of the first method include that empty subclasses in the treatment comparison are readily visualized, and that subclassification does not rely on a linear or any other regression model. A disadvantage is, that it can only be applied for a single confounder at a time. The advantage of the second method is, that multiple variables can be included in the model. However, the number of covariates is limited by the sample size of the trial. An advantage of the third method is, that it is generally more reliable and powerful with multiple covariates than regression modeling. However, irrelevant covariates and very large / small ORs reduce power and reliability of the assessment. The above methods can not be used for the assessment of interaction in the data.     

Two new covariate adjustment methods for non-inferiority assessment of binary clinical trials data

In clinical trials, examining the adjusted treatment difference has become the preferred way to establish non-inferiority (NI) in cases involving a binary endpoint. However, current methods are inadequate in the area of covariate adjustment. In this paper, we introduce two new methods, nonparametric and parametric, of using the probability and probability (P-P) curve to address the issue of unadjusted categorical covariates in the traditional assessment of NI in clinical trials. We also show that the area under the P-P curve is a valid alternative for assessing NI using the adjusted treatment difference, and we compute this area using Mann-Whitney nonparametric statistics. Our simulation studies demonstrate that our proposed methods can not only control type I error at a predefined significance level but also achieve higher statistical power than those of traditional parametric and nonparametric methods that overlook covariate adjustment, especially when covariates are unbalanced in the two treatment groups. We illustrate the effectiveness of our methodology with data from clinical trials of a therapy for coronary heart disease.     

Randomization-Based Adjustment of Treatment Hazard Ratio for Covariates With Missing Data

Clinical trials are designed to evaluate treatment effects while taking into account how covariates such as age and gender may influence the comparison between treatments. Including covariates in the model to evaluate treatment effects on time to event outcomes presents complications for a regulatory clinical trial because the covariates may need to meet modeling assumptions. We provide methodology to estimate the hazard ratio for treatments in a randomized trial with time to event outcomes and missingness among the baseline covariates by adjusting for the covariates in a randomization-based way. Such adjustment for covariates is an attractive methodology in the regulatory setting as it requires only minimal assumptions. The method is illustrated for data from an oncology clinical trial. Its application is computationally straightforward for managing missing data among the baseline covariates, and its results for the illustrative clinical trial are similar to those from multiple imputation for missing covariate data.     

A case study of the effect of covariate adjustment in a sequential survival clinical trial

Recent literature has discussed the value of adjustment for important covariates in models involving nonnormal data. It is generally concluded that, when performing traditional "fixed sample size" clinical trials, covariate adjustment influences the magnitude of the treatment effect but has little effect on precision of the estimate. In fact, it may actually reduce it. In this paper, we investigate the effect of covariate adjustment in the sequential setting via a case study of a survival clinical trial. Sequential and fixed sample analyses are compared, with and without covariate adjustment of the treatment effect. It was found that conclusions similar to those for the fixed sample size case also apply in the sequential case, but that incorporation of covariate information can present added complications in this setting.     

A CASE STUDY OF THE EFFECT OF COVARIATE ADJUSTMENT IN A SEQUENTIAL SURVIVAL CLINICAL TRIAL

Recent literature has discussed the value of adjustment for important covariates in models involving nonnormal data. It is generally concluded that, when performing traditional “fixed sample size” clinical trials, covariate adjustment influences the magnitude of the treatment effect but has little effect on precision of the estimate. In fact, it may actually reduce it. In this paper, we investigate the effect of covariate adjustment in the sequential setting via a case study of a survival clinical trial. Sequential and fixed sample analyses are compared, with and without covariate adjustment of the treatment effect. It was found that conclusions similar to those for the fixed sample size case also apply in the sequential case, but that incorporation of covariate information can present added complications in this setting.     

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This article uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be prespecified. Unplanned adjusted analyses should be considered secondary. Results suggest that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored.     

COVARIATE-ADJUSTED RESPONSE-ADAPTIVE DESIGNS FOR BINARY RESPONSE

An adaptive allocation design for phase III clinical trials that incorporates covariates is described. The allocation scheme maps the covariate-adjusted odds ratio from a logistic regression model onto [0, 1]. Simulations assume that both staggered entry and time to response are random and follow a known probability distribution that can depend on the treatment assigned, the patient's response, a covariate, or a time trend. Confidence intervals on the covariate-adjusted odds ratio is slightly anticonservative for the adaptive design under the null hypothesis, but power is similar to equal allocation under various alternatives for n = 200. For similar power, the net savings in terms of expected number of treatment failures is modest, but enough to make this design attractive for certain studies where known covariates are expected to be important and stratification is not desired, and treatment failures have a high ethical cost.     

Covariate-adjusted response-adaptive designs for binary response

An adaptive allocation design for phase III clinical trials that incorporates covariates is described. The allocation scheme maps the covariate-adjusted odds ratio from a logistic regression model onto [0, 1]. Simulations assume that both staggered entry and time to response are random and follow a known probability distribution that can depend on the treatment assigned, the patient's response, a covariate, or a time trend. Confidence intervals on the covariate-adjusted odds ratio is slightly anticonservative for the adaptive design under the null hypothesis, but power is similar to equal allocation under various alternatives for n = 200. For similar power, the net savings in terms of expected number of treatment failures is modest, but enough to make this design attractive for certain studies where known covariates are expected to be important and stratification is not desired, and treatment failures have a high ethical cost.