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.     

Augmented p-rep designs

Early generation variety trials are very important in plant and tree breeding programs. Typically many entries are tested, often with very little resources available. Unreplicated trials using control plots are popular and it is common to repeat the trials at a number of locations. An alternative is to use p-rep designs, where a proportion of the test entries are replicated at each location; this can obviate the need for control plots. α-Designs are commonly used for replicated variety trials and we show how these can be adapted to produce efficient p-rep designs.     

Response Adaptive Designs with a Variance‐penalized Criterion

We consider a response adaptive design of clinical trials with a variance‐penalized criterion. It is shown that this criterion evaluates the performance of a response adaptive design based on both the number of patients assigned to the better treatment and the power of the statistical test. A new proportion of treatment allocation is proposed and the doubly biased coin procedure is used to target the proposed proportion. Under reasonable assumptions, the proposed design is demonstrated to generate an asymptotic variance of allocation proportions, which is smaller than that of the drop‐the‐loser design. Simulation comparisons of the proposed design with some existing designs are presented.     

基于药物毒性反应等级的up-and-down自适应设计

Ⅰ期临床试验主要关心药物的毒性,目的是在给定的剂量水平中寻找最大耐受剂量（MTD）．本文提出了基于药物毒性反应等级的up-and-down自适应设计,调查了该设计在各种变化的剂量一毒性关系下的运作特征．结果表明：该设计方法对提高建议MTD的精确度,以及保护病人,防止病人暴露在较高毒性剂量下方面,对Ⅰ期临床试验的设计实现了有意义的改善．     

A comparison of methods for constructing confidence intervals after phase II/III clinical trials

Recently, in order to accelerate drug development, trials that use adaptive seamless designs such as phase II/III clinical trials have been proposed. Phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages. Using stage 1 data, an interim analysis is performed to answer phase II objectives and after collection of stage 2 data, a final confirmatory analysis is performed to answer phase III objectives. In this paper we consider phase II/III clinical trials in which, at stage 1, several experimental treatments are compared to a control and the apparently most effective experimental treatment is selected to continue to stage 2. Although these trials are attractive because the confirmatory analysis includes phase II data from stage 1, the inference methods used for trials that compare a single experimental treatment to a control and do not have an interim analysis are no longer appropriate. Several methods for analysing phase II/III clinical trials have been developed. These methods are recent and so there is little literature on extensive comparisons of their characteristics. In this paper we review and compare the various methods available for constructing confidence intervals after phase II/III clinical trials.     

Evaluation of experimental designs and spatial analyses in wheat breeding trials

Thirty-three wheat breeding trials were conducted from 1994 to 1996 in the Northern Grains Region (QLD and Northern NSW) of Australia to evaluate the influence of experimental designs and spatial analyses on the estimation of genotype effects for yield and their impact on selection decisions. The relative efficiency of the alternative designs and analyses was best measured by the average standard error of difference between line means. Both more effective designs and spatial analyses significantly improved the efficiency relative to the randomised complete block model, with the preferred model (which combined the design information and spatial trends) giving an average relative efficiency of 138% over all 33 trials. When the Czekanowski similarity coefficient was used, none of the studied models were in full agreement with the randomised complete block model in the selection of the top lines. The agreement was influenced by selection proportions. Hence, the use of these methodologies can impact on the selection decisions in plant breeding. Received: 17 December 1998 / Accepted: 29 July 1999     

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics

Due to increasing discoveries of biomarkers and observed diversity among patients, there is growing interest in personalized medicine for the purpose of increasing the well‐being of patients (ethics) and extending human life. In fact, these biomarkers and observed heterogeneity among patients are useful covariates that can be used to achieve the ethical goals of clinical trials and improving the efficiency of statistical inference. Covariate‐adjusted response‐adaptive (CARA) design was developed to use information in such covariates in randomization to maximize the well‐being of participating patients as well as increase the efficiency of statistical inference at the end of a clinical trial. In this paper, we establish conditions for consistency and asymptotic normality of maximum likelihood (ML) estimators of generalized linear models (GLM) for a general class of adaptive designs. We prove that the ML estimators are consistent and asymptotically follow a multivariate Gaussian distribution. The efficiency of the estimators and the performance of response‐adaptive (RA), CARA, and completely randomized (CR) designs are examined based on the well‐being of patients under a logit model with categorical covariates. Results from our simulation studies and application to data from a clinical trial on stroke prevention in atrial fibrillation (SPAF) show that RA designs lead to ethically desirable outcomes as well as higher statistical efficiency compared to CARA designs if there is no treatment by covariate interaction in an ideal model. CARA designs were however more ethical than RA designs when there was significant interaction.     

An Approximation to the Bayes Optimal Multi-Stage Design in the Colton Model for Clinical Trials

A simple and operationally convenient approximation is proposed for the Bayes optimal multistage design within the Colton decision-theoretic model for the comparison of two medical treatments. The two- and three-stage designs are developed in full; the latter is found to be superior to several existing designs including some involving sequential adaptive sampling.     

A comparison of methods for adaptive treatment selection

Traditionally drug development is generally divided into three phases which have different aims and objectives. Recently so-called adaptive seamless designs that allow combination of the objectives of different development phases into a single trial have gained much interest. Adaptive trials combining treatment selection typical for Phase II and confirmation of efficacy as in Phase III are referred to as adaptive seamless Phase II/III designs and are considered in this paper. We compared four methods for adaptive treatment selection, namely the classical Dunnett test, an adaptive version of the Dunnett test based on the conditional error approach, the combination test approach, and an approach within the classical group-sequential framework. The latter two approaches have only recently been published. In a simulation study we found that no one method dominates the others in terms of power apart from the adaptive Dunnett test that dominates the classical Dunnett by construction. Furthermore, scenarios under which one approach outperforms others are described.     

Bayesian enrichment strategies for randomized discontinuation trials

We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.     

Blinded sample size reestimation in non-inferiority trials with binary endpoints

Sample size calculations in the planning of clinical trials depend on good estimates of the model parameters involved. When the estimates of these parameters have a high degree of uncertainty attached to them, it is advantageous to reestimate the sample size after an internal pilot study. For non-inferiority trials with binary outcome we compare the performance of Type I error rate and power between fixed-size designs and designs with sample size reestimation. The latter design shows itself to be effective in correcting sample size and power of the tests when misspecification of nuisance parameters occurs with the former design.     

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.     

Sequential designs for ordinal phase I clinical trials

Sequential designs for phase I clinical trials which incorporate maximum likelihood estimates (MLE) as data accrue are inherently problematic because of limited data for estimation early on. We address this problem for small phase I clinical trials with ordinal responses. In particular, we explore the problem of the nonexistence of the MLE of the logistic parameters under a proportional odds model with one predictor. We incorporate the probability of an undetermined MLE as a restriction, as well as ethical considerations, into a proposed sequential optimal approach, which consists of a start‐up design, a follow‐on design and a sequential dose‐finding design. Comparisons with nonparametric sequential designs are also performed based on simulation studies with parameters drawn from a real data set.     

The design and analysis of breeders' trials of sugar beet (Beta vulgaris) using two-dimensional blocking structures

A method for generating two-dimensional blocking designs to fit any shape and size of experimental area is presented. The method takes an alpha design appropriate to the experimental area and imposes additional blocking perpendicular to the alpha blocks. Improvements to the design are then made by repositioning entries within the alpha blocks. Although this method of construction is less sophisticated than for other two-dimensional designs, the designs are particularly suited to large scale breeders trials where no alternative two-dimensional design may exist. The designs have been used for 3 yr in a sugar beet breeding programme, and have given improvements in efficiency over one-dimensional alpha designs.     

Cylindrically Rotatable Designs of Type 3: Further Considerations

The cylindrically rotatable designs of type 3 are generalised to consider the factors split up into any number of groups. The conditions which must be satisfied by such a design are derived from the moment generating function of the design. A method of construction of second-order cylindrically rotatable designs of type 3 with three or more groups of factors is provided.     

The placebo response in clinical trials: more questions than answers

Meta-analyses and re-analyses of trial data have not been able to answer some of the essential questions that would allow prediction of placebo responses in clinical trials. We will confront these questions with current empirical evidence. The most important question asks whether the placebo response rates in the drug arm and in the placebo arm are equal. This 'additive model' is a general assumption in almost all placebo-controlled drug trials but has rarely been tested. Secondly, we would like to address whether the placebo response is a function of the likelihood of receiving drug/placebo. Evidence suggests that the number of study arms in a trial may determine the size of the placebo and the drug response. Thirdly, we ask what the size of the placebo response is in 'comparator' studies with a direct comparison of a (novel) drug against another drug. Meta-analytic and experimental evidence suggests that comparator studies may produce higher placebo response rates when compared with placebo-controlled trials. Finally, we address the placebo response rate outside the laboratory and outside of trials in clinical routine. This question poses a serious challenge whether the drug response in trials can be taken as evidence of drug effects in clinical routine.     

Incorporating Individual and Collective Ethics into Phase I Cancer Trial Designs

Summary A general framework is proposed for Bayesian model based designs of Phase I cancer trials, in which a general criterion for coherence (Cheung, 2005, Biometrika 92 , 863–873) of a design is also developed. This framework can incorporate both “individual” and “collective” ethics into the design of the trial. We propose a new design that minimizes a risk function composed of two terms, with one representing the individual risk of the current dose and the other representing the collective risk. The performance of this design, which is measured in terms of the accuracy of the estimated target dose at the end of the trial, the toxicity and overdose rates, and certain loss functions reflecting the individual and collective ethics, is studied and compared with existing Bayesian model based designs and is shown to have better performance than existing designs.     

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.     

Dose finding for continuous and ordinal outcomes with a monotone objective function: a unified approach

Summary .  In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. In this article, we describe a dose-finding design that can be used in any of the dose-finding trials described above, trials where the target dose is defined as the dose at which a certain monotone function of the dose is a prespecified value. At each step of the proposed design, the normalized difference between the current dose and the target is computed. If that difference is close to zero, the dose is repeated. Otherwise, the dose is increased or decreased, depending on the sign of the difference.     

Construction of resolvable spatial row-column designs

Resolvable row-column designs are widely used in field trials to control variation and improve the precision of treatment comparisons. Further gains can often be made by using a spatial model or a combination of spatial and incomplete blocking components. Martin, Eccleston, and Gleeson presented some general principles for the construction of robust spatial block designs which were addressed by spatial designs based on the linear variance (LV) model. In this article we define the two-dimensional form of the LV model and investigate extensions of the Martin et al. principles for the construction of resolvable spatial row-column designs. The computer construction of efficient spatial designs is discussed and some comparisons made with designs constructed assuming an autoregressive variance structure.