On the COLTON Model for Clinical Trials with Delayed Observations-Dichotomous Responses

In the COLTON model for clinical trials a choice is to be made between two medical treatments where there is a known patient horizon N. In an earlier paper we studied the COLTON model under the additional assumption that there is a time lag between the administration of the treatments and the availability of the responses where the responses are normally distributed. Here we extend the results of the earlier paper to the case where responses are dichotomous. The relative performance of two simple procedures for dealing with the patients who arrive during the waiting period between the end of the administration of treatment in the trial phase and observation of the final trial response is discussed within a BAYESIAN framework.     

A note on the Design of Unreplicated Trials

One approach to the design of early generation trials is to have a number of replicated check varieties and a larger number of unreplicated or new varieties. For such trials we define the class of α–α‐designs as an extension to the augmented lattice square designs of Federer (2002). Efficient latinized α–α‐designs can be conveniently constructed using the design generation package CycDesigN.     

The Use of Adaptive Blocks in the Design of Clinical Trials

This paper introduces a class of data-dependent allocation rules for use in sequential clinical trials designed to choose the better of two competing treatments, or to decide that they are of equal efficacy. These readily understood and easily implemented rules are shown to reduce, substantially the number of tests with the poorer treatment for a broad category of experimental situations. Allocation rules of this type are applied both to trials with an instantaneous binomial response and to delayed response trials where interest centers on exponentially distributed survival time. In each case, a comparison of this design with alternative designs given in the literature shows that the proposed design is superior with respect to ease of application and is comparable to the alternatives regarding inferior treatment number and average sample number. In addition, the proposed rules mitigate many of the difficulties generally associated with adaptive assignment rules, such as selection and systematic bias.     

Conditional Markov Chain Design for Accrual Clinical Trials

Randomization in a comparative experiment has, as one aim, the control of bias in the initial selection of experimental units. When the experiment is a clinical trial employing the accrual of patients, two additional aims are the control of admission bias and control of chronologic bias. This can be accomplished by using a method of randomization, such as the “biased coin design” of Efron, which sequentially forces balance. As an extension of Efron's design, this paper develops a class of conditional Markov chain designs. The detailed randomization employed utilizes the sequential imbalances in the treatment allocation as states in a Markov process. Through the use of appropriate transition probabilities, a range of possible designs can be attained. An additional objective of physical randomization is to provide a model for data analysis. Such a randomization theoretic analysis is presented for the current designs. In addition, Monte Carlo sampling results are given to support the proposed normal theory approximation to the exact randomization distribution.     

Stopping Rules for Clinical Trials Implicitly Incorporating Safety Information

Clinical trials research is mainly conducted for the purpose of evaluating the relative efficacy of two or more treatments. However, a positive response due to treatment is not sufficient to put forward a new product because one must also demonstrate safety. In such cases, clinical trials which show a positive effect would need to accrue enough patients to also demonstrate that the new treatment is safe. It is our purpose to show how the efficacy and safety problems can be combined to yield a more practical clinical trial design. In this paper we propose an asymmetric stopping rule which allows the experimenter to terminate a clinical trial early for a sufficiently negative result and to continue to a specified number of patients otherwise. As it turns out, a few interim tests will have negligible effects on the overall significance level.     

临床试验进展：生物标志物助推临床开发决策

生物标志物对于疾病的鉴定、早期诊断和预防,以及治疗过程中的监控具有重要作用。寻找和发现有价值的生物标志物已成为目前研究的一个热点。汤森路透Cortellis 临床试验情报将生物标志物的发现与针对当前常见疾病的相关临床试验关联,提供描绘临床图景关键元素和当前趋势的专家分析,从而指导临床开发决策。     

临床试验进展：大数据方法在临床试验中的应用

药物研发的复杂性与日俱增,而大数据时代的到来使得临床试验的进展大大加快。本期“临床试验进展”讨论了皮肤病学新创试验中面向数据的亮点,探讨了现实采用的大数据方法,剖析了对风险导向监察的新兴方法学。此外还汇总了银屑病和特应性皮炎新疗法的临床研究报告,揭示了这些疾病的影响以及Ⅱ期和Ⅲ期研究中已经取得成功的候选药。     

临床试验进展: 适应性设计及透明性

临床试验中的适应性设计是根据累积信息来修正试验的一种设计方法,旨在使临床试验和临床开发计划效率更高,并为患者提供更加有效的治疗。此外,因信息隐匿造成试验失败和患者死亡,从而导致公众对医药行业的信任度下降,故关于公开临床数据的观点已开始朝着提升透明度方向调整。介绍3 类适应性设计方法的特点和应用,以及国外药政部门和制药企业对提高临床试验透明性的举措。     

临床试验进展：临床数据与实验室质量管理规范

将临床研究数据用于临床日常规范及健康相关决策的制定对于改善全球医疗保健至关重要。汤森路透Cortellis 临床试验情报对临床试验数据的应用价值及各国临床实验室质量管理规范的实施情况进行了介绍,提供描绘临床图景关键元素和当前趋势的专家分析,从而指导临床开发决策。     

A statistical framework for quantile equivalence clinical trials with application to pharmacokinetic studies that bridge from HIV-infected adults to children

SUMMARY: Bridging clinical trials are sometimes designed to evaluate whether a proposed dose for use in one population, for example, children, gives similar pharmacokinetic (PK) levels, or has similar effects on a surrogate marker as an established effective dose used in another population, for example, adults. For HIV bridging trials, because of the increased risk of viral resistance to drugs at low PK levels, the goal is often to determine whether the doses used in different populations result in similar percentages of patients with low PK levels. For example, it may be desired to evaluate that a proposed pediatric dose gives approximately 10% of children with PK levels below the 10th percentile of PK levels for the established adult dose. However, the 10th percentile for the adult dose is often imprecisely estimated in studies of relatively small size. Little attention has been given to the statistical framework for such bridging studies. In this article, a formal framework for the design and analysis of quantile-based bridging studies is proposed. The methodology is then developed for normally distributed outcome measures from both frequentist and Bayesian directions. Sample size and other design considerations are discussed.     

The Case For Randomized Clinical Trials on the Treatment of Obesity

Some say that randomized clinical trials on weight loss are unnecessary (“the benefits are ‘obvious’”) and others say that such trials are not feasible because too few participants will succeed in maintaining weight loss. Although the intermediate term benefits of weight loss are beyond dispute (lowering of blood pressure, lipids, blood sugar, etc), there is no proof that these benefits will translate into long term benefits, i.e., lower rates of cardiovascular disease and/or lower overall mortality. While this extrapolation may seem obvious, the clinical trials' literature is full of unexpected, adverse side effects of theoretically appealing therapies (e.g., higher mortality with clofibrate and higher cardiovascular disease rates with estrogen treatment in men). Although there is clearly a voluntary component to food ingestion, there are also powerful physiological forces at work which impact on energy balance. For example, individuals of similar height and weight may nevertheless have widely different daily energy expenditures and hence energy requirements. It has been shown in Pima Indians that those with low energy expenditure (i.e., those who are “fuel efficient”) are more prone to future weight gain than those with high energy expenditure. Also, reduced obese individuals have lower 24-hour energy expenditure than individuals who are spontaneously at the same lean weight It appears that this deficit in energy expenditure may last for several years, if not longer, implying that reduced obese individuals must exercise far greater vigilance over their caloric intake than their spontaneously lean peers. If they allow themselves to ingest the same number of calories as the latter, they are likely to regain weight, thereby exposing themselves to charges of overeating, even though their caloric intake does not exceed that of the spontaneously lean!. Epidemiologic data do not support a benefit of weight loss. Populations such as Mexican Americans, among whom obesity is more common than in the general population, do not have excess mortality past age 45. Life expectancy in the U.S. has improved steadily since the early 1970s, despite a rising prevalence of obesity. Lastly, prospective studies have suggested that people who lose weight die at a higher rate than those who maintain a stable weight. This effect persists even after controlling for latent, subclinical disease and cigarette smoking. Although none of the above considerations prove that voluntary weight loss is bad, they indicate that this treatment should lose its hitherto privileged status and be subjected to the rigors of clinical trials as have been treatments for hypercholesterolemia and hypertension.     

药物基因组学在新药临床试验及个体化用药中的应用

药物安全性和有效性评价是药物临床试验和个体化用药的核心,也是药物基因组学研究的主要内容。药物基因组学研究贯穿于药物 研发、上市评价和临床应用整个过程, 根据药物代谢酶、转运体、受体相关基因多态性对用药者进行分层分析,评价与药物体内的处置过程、 安全性、有效性个体差异的相关性。综述药物基因组学在新药临床试验、个体化用药中的应用研究新进展。     

A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials

Summary A surrogate marker (S) is a variable that can be measured earlier and often more easily than the true endpoint (T) in a clinical trial. Most previous research has been devoted to developing surrogacy measures to quantify how well S can replace T or examining the use of S in predicting the effect of a treatment (Z). However, the research often requires one to fit models for the distribution of T given S and Z. It is well known that such models do not have causal interpretations because the models condition on a postrandomization variable S. In this article, we directly model the relationship among T, S, and Z using a potential outcomes framework introduced by Frangakis and Rubin (2002, Biometrics 58 , 21–29). We propose a Bayesian estimation method to evaluate the causal probabilities associated with the cross‐classification of the potential outcomes of S and T when S and T are both binary. We use a log‐linear model to directly model the association between the potential outcomes of S and T through the odds ratios. The quantities derived from this approach always have causal interpretations. However, this causal model is not identifiable from the data without additional assumptions. To reduce the nonidentifiability problem and increase the precision of statistical inferences, we assume monotonicity and incorporate prior belief that is plausible in the surrogate context by using prior distributions. We also explore the relationship among the surrogacy measures based on traditional models and this counterfactual model. The method is applied to the data from a glaucoma treatment study.     

Optimal complete block designs to adjust for interplot competition with a covariance analysis

Optimal complete block designs are given for variety trials with interplot competition. In the paper, competition is modeled using height differences between neighboring varieties. When variety heights are known at the design stage, a simulated annealing algorithm is proposed. The optimal designs tend to reduce height differences between neighboring varieties. When variety heights are not known in advance, neighbor-balanced designs at distances 1 and 2 are shown to have optimality properties.     

Ethical Considerations in Clinical Trials: A Critique of the ICH‐GCP Guideline

This article examines issues relating to ethics decision‐making in clinical trials. The overriding concern is to ensure that the well being and the interests of human subjects are adequately safeguarded. In this respect, this article will embark on a critical analysis of the ICH‐GCP Guideline. The purpose of such an undertaking is to highlight areas of concern and the shortcomings of the existing ICH‐GCP Guideline. Particular emphasis is made on how ethics committees perform their duties and responsibilities in line with the principles outlined in the ICH‐GCP Guideline. This article will draw attention to the need for a new approach to addressing the weaknesses of the ICH‐GCP Guideline in its present form.     

Generalized Estimating Equations in Controlled Clinical Trials: Hypotheses Testing

Generalized estimating equations (GEE) for the analysis of clustered data have gained increasing popularity. Recently, the first monograph on this method has been published. GEE have been repeatedly applied in controlled clinical trials. They have, however, been generally used as secondary or supplementary analysis. Instead, the primary analysis was mostly based on a classical method that usually ignored the clustered – mostly longitudinal – nature of the data. In this paper, we discuss the applicability of GEE as primary analysis in controlled clinical trials. From theoretical results in the literature, we derive recommendations how GEE should be used in therapeutic studies for testing statistical hypotheses. We hope that our paper is the starting point for a thorough discussion on the most appropriate analysis of controlled clinical trials with clustered dependent variables. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Incorporating Prior Beliefs in Treatment Allocation for Clinical Trials

Recently Faraggi and Reiser (1991) introduced a general dynamic treatment allocation scheme which incorporates the permuted block allocation procedure and the Begg Iglewicz procedure as special cases. A drawback of previous allocation methods is that they did not allow incorporation of prior knowledge of the relative importance of the prognostic factors. In this paper we extend our allocation model to incorporate the weighting of prognostic factors. A simulation study examines the effects of this procedure and an example of its implementation is given.     

Decision-Theoretic Designs for Pre-Phase II Screening Trials in Oncology

A Bayesian decision-theoretic method is proposed for conducting small, randomized pre-phase II selection trials. The aim is to improve on the design of Thall and Estey (1993, Statistics in Medicine 12, 1197-1211). Designs are derived that optimize a gain function accounting for current and future patient gains, per-patient cost, and future treatment development cost. To reduce the computational burden associated with backward induction, myopic versions of the design that consider only one, two, or three future decisions at a time are also considered. The designs are compared in the context of a screening trial in acute myelogenous leukemia.