Quantifying the exact allocation ratios in randomized clinical trials with biased coin randomization

The biased coin randomization approach is frequently adopted in randomized clinical trials to control the balance in overall treatment assignments. In this article, an algorithm is developed to theoretically determine the exact allocation ratios for all the patients that will be enrolled in a randomized clinical trial with biased coin randomization based on the order in which they are randomized. Our results show that the exact allocation ratios can significantly deviate from the ratio as specified for the trial, which poses challenges to the enrollment of the trial and to the interpretation of the results. In order to maintain a constant allocation ratio throughout the trial, a modification of the widely adopted permuted block randomization is proposed, which is shown to achieve better balance for not only the overall treatment assignments but also the baseline stratification variables that are desired to be balanced at the end of the trial.     

Constrained poststratification

Adjustment for covariates (or poststratification) is frequently used in the analysis of randomized clinical trials. The purpose of such analysis is mainly to eliminate some residual bias resulting from any imbalance between treatment groups for some important covariates. Usually, covariate effect is modeled with the data at hand. In this paper, we present a new method of poststratification ("constrained poststratification") which consists of estimating the prognostic significance of covariates in a large historical data base, transferring the model's coefficients into the (smaller) randomized trial data set, and estimating treatment effects conditional on this a priori information. In a simulated experiment, constrained poststratification allowed not only reduction of the bias but also enhancement of the efficiency of the estimation of treatment effect.     

Detecting an interaction between treatment and a continuous covariate: A comparison of two approaches

In clinical trials, there is considerable interest in investigating whether a treatment effect is similar in all patients, or that some prognostic variable indicates a differential response to treatment. To examine this, a continuous predictor is usually categorized into groups according to one or more cutpoints. The treatment/covariate interaction is then analyzed in factorial fashion using multiplicative terms. The use of cutpoints raises several difficult issues for the analyst. It is preferable to keep continuous variables continuous in such a model. To achieve this, the MFP algorithm for multivariable model-building with fractional polynomials was recently extended to a new algorithm called multivariable fractional polynomial interaction (MFPI). With the latter, covariates may be binary, categorical or continuous, and cutpoints are avoided. MFPI is compared with a graphical technique, the subpopulation treatment-effect pattern plot or subpopulation treatment effect pattern plot (STEPP). Differences between MFPI and STEPP are illustrated by re-analysis of a randomized trial in kidney cancer. The stability of the two procedures is investigated by using the bootstrap. The Type I error probability of MFPI to ‘detect’ spurious interactions is estimated by simulation. MFPI and STEPP are found to exhibit similar treatment/covariate interactions. The tail-oriented variant of STEPP is found to give more stable and interpretable results than the sliding window variant. The type 1 error probabilty of MFPI is found to be close to its nominal value.     

THE EQUAL PROBABILITY TEST AND OPTIMAL ALLOCATION OF SUBJECTS TO THE GROUPS WHEN COMPARING A NEW DRUG AND A PLACEBO

It is shown that the Fisher-Irwin “usual” (nonrandomized) conditional test for differences between proportions, using independent binomial samples, is very conservative (in the sense that the actual significance level attributable to an outcome is often one-fourth to one-half of the anticipated value) and hence is not appropriate for comparing a new drug and a placebo when the response is quantal and the sample sizes are small. Instead of this test and choice of the significance level (which is, in practice, made by an arbitrary conventional choice), we suggest the equal probability test representing a randomized analog of the “usual” conditional test and determining the critical region so that the errors of the first and second kind are equal. An optimal allocation of subjects to the groups for testing the difference between the probabilities of a response of the two groups is investigated when we are limited to the total number of subjects available for the drug trial. We choose an allocation of subjects to the groups that minimizes the upper bound of the unconditional error of the first kind and hence is greatly preferred over equal allocation of subjects to the groups. The above procedure can be also used for determination of the minimal total number of subjects (for comparative trials under optimal allocation of subjects to the groups) required for detecting the difference between the probabilities of a response of the two groups with the specified upper bound of type 1 error. Numerical examples illustrating the use of the proposed procedures are included.     

运用EXCEL函数进行完全随机试验设计

完全随机设计的实质是将供试单位随机分组该文通过对完全随机设计实质的分析，对运用EXCEL进行完全随机试验设计的方法进行了介绍。结果认为只要每一独立供试单位在组间随机排列，始终贯穿随机精神以最大限度地减少系统误差，灵活利用Excel的随机函数如RANDOM、RANDOMBETWEEN等和排序按钮，用任何一种方法都可以快速的进行完全随机试验设计。     

Estimation of the proportion ratio under a simple crossover trial

The proportion ratio (PR) of patient response is one of the most commonly used indices for measuring the relative treatment effect in a randomized clinical trial (RCT). Assuming a random effect multiplicative risk model, we develop two point estimators and three interval estimators in closed forms for the PR under a simple crossover RCT. On the basis of Monte Carlo simulation, we evaluate the performance of these estimators in a variety of situations. We note that the point estimator using a ratio of two arithmetic means of patient response probabilities over the two groups (distinguished by the order of treatment-received sequences) is generally preferable to the corresponding one using a ratio of two geometric means of patient response probabilities. We note that the three interval estimators developed in this paper can actually perform well with respect to the coverage probability when the number of patients per group is moderate or large. We further note that the interval estimator based on the ratio of two arithmetic means of patient response probabilities with the logarithmic transformation is probably the best among the three interval estimators discussed here. We use a simple crossover trial studying the suitability of two new inhalation devices for patients who were using a standard inhaler device delivering Salbutamol published elsewhere to illustrate the use of these estimators.     

RandoWeb, an online randomization tool for clinical trials

This article describes RandoWeb, a generic tool allowing online randomizations for clinical trials. It allows some users to specify the randomization methods to use, patient data to collect or compute, and other users authorized to access the tool and their rights. Randomization can be defined through a randomization list or by using a dynamic method such as minimization. The randomization lists can be stratified and use block decomposition to maintain the best possible balance between the arms of the trial, even for the case when a trial is stopped prematurely. The block definition used by RandoWeb is slightly different from the classical one and, in particular, allows blocks with non-integer sizes. In RandoWeb, patients can be assigned up to three numbers: a record number, most often externally defined; a randomization number mainly useful for unblinding; and a treatment number used for the management of undifferentiated medicine packaging.     

Risk of mortality after acute myocardial infarction: Performance of model updating methods for application in different geographical regions

Using the large Hirulog and Early Reperfusion or Occlusion (HERO-2) trial a parsimonious multivariable model for 30-day mortality risk assessment in acute myocardial infarction (AMI) was developed. HERO-2 was an international randomized trial of two antithrombotic therapies-unfractionated heparin and bivalirudin-for the treatment of AMI. This trial recruited 17 073 patients from 46 countries from Europe, North and Latin America and Asia, including Australia, New Zealand and Russia. An important issue in applying findings from randomized clinical trials is the procedure to estimate risk among members of other patient populations. Methods for updating risk models for AMI are compared. Simple re-calibration (re-estimation of the intercept and slope of the linear predictor within regions) and model revision (re-estimation of all regression coefficients within regions) with and without shrinkage were compared to a global additive model with a built-in region effect. The relative performance of these methods in the different geographical regions, which vary in sample size, is of primary interest. Model revision only provided a slight improvement in predictive performance when applied with shrinkage in the smallest region Asia (N=756). In conclusion, a global model with regional re-calibration is adequate: region-specific coefficients did not provide worthwhile improvements in any region.     

TERMTrial--terminology-based documentation systems for cooperative clinical trials

Within cooperative groups of multi-center clinical trials a standardized documentation is a prerequisite for communication and sharing of data. Standardizing documentation systems means standardizing the underlying terminology. The management and consistent application of terminology systems is a difficult and fault-prone task, which should be supported by appropriate software tools. Today, documentation systems for clinical trials are often implemented as so-called Remote-Data-Entry-Systems (RDE-systems). Although there are many commercial systems, which support the development of RDE-systems there is none offering a comprehensive terminological support. Therefore, we developed the software system TERMTrial which consists of a component for the definition and management of terminology systems for cooperative groups of clinical trials and two components for the terminology-based automatic generation of trial databases and terminology-based interactive design of electronic case report forms (eCRFs). TERMTrial combines the advantages of remote data entry with a comprehensive terminological control.     

采用改进被囊群算法的多冷水机组负荷分配优化

为了降低中央空调系统的运行能耗,针对多冷水机组负荷分配优化问题,提出一种随机森林特征优选结合核函数极限学习机的冷水机组能效预测模型,通过剔除冗余特征提高预测精度;然后提出一种混合策略改进的被囊群算法,融合鲸鱼螺旋搜索策略改进个体更新方式,引入非线性动态权重平衡全局探索和局部开发,使用空翻扰动策略避免陷入局部最优;最后在能效模型的基础上,采用改进被囊群算法对多冷水机组负荷分配进行优化。实验结果表明,随机森林特征优选的方法可以有效的提高能效预测模型的准确度;改进被囊群算法通过优化机组的启停状态和负荷率可以有效发挥系统的节能潜力,与原有方法相比能耗降低约6%。说明该方法适用于多冷水机组的负荷分配优化问题。     

Sample size calculation for non-compliance randomized trials with repeated measurements in binary data

When we have difficulty in recruiting patients into a randomized clinical trial (RCT), we may consider taking more than one measurement per patient to reduce the number of patients needed to achieve a desired power. In this paper, we consider a double blind RCT with two courses of treatment per patient. At each course, a patient assigned to the experimental treatment could switch to receive the placebo if the patient declined his/her assigned (experimental) treatment, and a patient assigned to the placebo could switch to receive the experimental treatment if the patient refused his/her assigned (placebo) treatment as well. Sample size calculation without accounting for this non-compliance can be inadequate when we apply the standard procedure of intention-to-treat analysis for non-compliance trials to test no treatment effect. Based on the simple additive risk model proposed elsewhere, we have incorporated the initial probability of compliance, the dependence of patient's selection of a treatment on his/her previous response, and the variation of probabilities of response between patients into sample size determination. We have included a quantitative discussion that provides an insight into the effect of various parameters on the minimum required sample size. We have also noted the situation in which taking repeated measurements per patient can be most effective to reduce the number of patients needed to maintain a given power.     

A SAS macro for a clustered logrank test

The clustered logrank test is a nonparametric method of significance testing for correlated survival data. Examples of its application include cluster randomized trials where groups of patients rather than individuals are randomized to either a treatment or a control intervention. We describe a SAS macro that implements the 2-sample clustered logrank test for data where the entire cluster is randomized to the same treatment group. We discuss the theory and applications behind this test as well as details of the SAS code.     

A SAS macro for sample size re-estimation

The assessment of sample size in clinical trials comparing means requires a variance estimate of the main efficacy variable. If no reliable information about the variance of the key response is available at the beginning of a clinical trial, the use of data from the first 'few' patients entered in the trial ('internal pilot') may be appropriate to estimate the variance and thus to recalculate the required sample size. A SAS macro that implements the EM algorithm for carrying out and simulating such interim power evaluations without unblinding the treatment status is presented.     

一种自适应的动态网格任务调度算法

GRACE网格资源框架是一个分布式、可计算的经济学体系框架,针对框架中分配网格资源问题,引入近视算法,提出了一种自适应的动态网格任务调度算法。该算法通过在调度过程中动态监测系统的负载平衡度,自适应地选择任务调度策略。经模拟试验证明,该调度算法提高了任务的调度成功率。     

一种基于市场机制的随机服务系统模型

在市场机制的网格环境中对大量的用户进行有效的排队是很有必要的。本文以随机服务系统理论为基础针对网格计算提出了一种基于市场机制的资源分配系统的接受用户服务的模型。该模型可以提高计算网格资源的随机服务系统性能及其运行的优化和缓解网络堵塞和系统忙,可以提高网格资源分配的均衡和网格系统的运行效率。     

An experimental study on the role of graphical information about hand movement when interacting with objects in virtual reality environments

In this series of experiments, we investigated whether a crude representation of the hand that was extinguished at movement onset improved performance when compared to a no-feedback situation. Subjects performed simple reach to grasp movements in a virtual environment in two experiments. In Experiment 1, trials were blocked so that subjects were aware that a graphical representation of the hand would either be available throughout the movement (FA), be removed at movement onset (FAB), or not be available (NF). In Experiment 2, trials were randomized so that subjects were unaware of whether feedback would be available throughout the trial or removed at movement onset. Our results indicated that when subjects were aware of the availability of graphical feedback, the FAB condition improved performance compared to the NF condition. Furthermore, movement time was similar in the two feedback available conditions (FA, FAB). In contrast, for the randomized trial presentation, the positive influence of the FAB condition was diminished. These results suggest that visual feedback available prior to movement onset can be used to calibrate the proprioceptive system and improve performance over a no feedback situation. These results can be applied by designers of virtual environments to solve problems related to occlusion of important environmental information by the hand as users reach to grasp and manipulate objects.     

Truncated group sequential test for clinical trials

Although a sequential test, in general, requires a smaller sample size on the average, most clinical trials are not designed in a sequential approach. One of the reasons is that the sample size by a sequential test can exceed that required by an equivalent fixed-sample size test. Truncation of a triangular sequential design is considered in this paper. It is intended so that the maximum sample size for the test becomes approximately equal to the sample size for the fixed-sample test of about the same power. The method is intended for one-sided group sequential tests where the treatment group is compared to the control group for the difference in proportions. The method is illustrated using a clinical trial of head injury patients. Comparisons with other group sequential tests suggested that the proposed method may provide a more efficient test in clinical trials.     

Optimizing parameters in clinical trials with a randomized start or withdrawal design

Disease-modifying (DM) trials on chronic diseases such as Alzheimer’s disease (AD) require a randomized start or withdrawal design. The analysis and optimization of such trials remain poorly understood, even for the simplest scenario in which only three repeated efficacy assessments are planned for each subject: one at the baseline, one at the end of the trial, and the other at the time when the treatments are switched. Under the assumption that the repeated measures across subjects follow a trivariate distribution whose mean and covariance matrix exist, the DM efficacy hypothesis is formulated by comparing the change of efficacy outcome between treatment arms with and without a treatment switch. Using a minimax criterion, a methodology is developed to optimally determine the sample size allocations to individual treatment arms as well as the optimum time when treatments are switched. The sensitivity of the optimum designs with respect to various model parameters is further assessed. An intersection–union test (IUT) is proposed to test the DM hypothesis, and determine the asymptotic size and the power of the IUT. Finally, the proposed methodology is demonstrated by using reported statistics on the placebo arms from several recently published symptomatic trials on AD to estimate necessary parameters and then deriving the optimum sample sizes and the time of treatment switch for future DM trials on AD.     

Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model

The reduction of viral load is frequently used as a primary endpoint in HIV clinical trials. Nonlinear mixed-effects models are thus proposed to model this decrease of the viral load after initiation of treatment and to evaluate the intra- and inter-patient variability. However, left censoring due to quantification limits in the viral load measurement is an additional challenge in the analysis of longitudinal HIV data. An extension of the stochastic approximation expectation-maximization (SAEM) algorithm is proposed to estimate parameters of these models. This algorithm includes the simulation of the left-censored data in a right-truncated Gaussian distribution. Simulation results show that the proposed estimates are less biased than the usual naive methods of handling such data: omission of all censored data points, or imputation of half the quantification limit to the first point below the limit and omission of the following points. The viral load measurements obtained in the TRIANON-ANRS81 clinical trial are analyzed with this method and a significant difference is found between the two treatment groups of this trial.     

Giving and receiving emotional support online: Communication competence as a moderator of psychosocial benefits for women with breast cancer

This study examines the moderating role of emotional communication competence in the relationship between Computer-Mediated Social Support (CMSS) group participation, specifically giving and receiving emotional support, and psychological health outcomes. Data were collected as part of randomized clinical trials for women diagnosed with breast cancer within the last two months. Expression and reception of emotional support was assessed by tracking and coding the 18,064 messages that 236 patients posted and read in CMSS groups. The final data used in the analysis was created by merging (a) computer-aided content analysis of discussion posts, (b) action log data analysis of system usage, and (c) baseline and 6-month surveys collected to assess change. Results of this study demonstrate that emotional communication competence moderates the effects of expression and reception of emotional support on psychological quality of life and breast cancer-related concerns in both desired and undesired ways. Giving and receiving emotional support in CMSS groups has positive effects on emotional well-being for breast cancer patients with higher emotional communication, while the same exchanges have detrimental impacts on emotional well-being for those with lower emotional communication competence. The theoretical and practical implications for future research are discussed.