Decision-theoretic designs for phase II clinical trials allowing for competing studies

This article describes an approach to optimal design of phase II clinical trials using Bayesian decision theory. The method proposed extends that suggested by Stallard (1998, Biometrics54, 279–294) in which designs were obtained to maximize a gain function including the cost of drug development and the benefit from a successful therapy. Here, the approach is extended by the consideration of other potential therapies, the development of which is competing for the same limited resources. The resulting optimal designs are shown to have frequentist properties much more similar to those traditionally used in phase II trials.

Meta-analysis of individual patient data from randomised trials: a review of methods used in practice

Background: Meta-analyses based on individual patient data (IPD) are regarded as the gold standard for systematic reviews. However, the methods used for analysing and presenting results from IPD meta-analyses have received little discussion. Methods We review 44 IPD meta-analyses published during the years 1999–2001. We summarize whether they obtained all the data they sought, what types of approaches were used in the analysis, including assumptions of common or random effects, and how they examined the effects of covariates. Results: Twenty-four out of 44 analyses focused on time-to-event outcomes, and most analyses (28) estimated treatment effects within each trial and then combined the results assuming a common treatment effect across trials. Three analyses failed to stratify by trial, analysing the data is if they came from a single mega-trial. Only nine analyses used random effects methods. Covariate-treatment interactions were generally investigated by subgrouping patients. Seven of the meta-analyses included data from less than 80% of the randomized patients sought, but did not address the resulting potential biases. Conclusions: Although IPD meta-analyses have many advantages in assessing the effects of health care, there are several aspects that could be further developed to make fuller use of the potential of these time-consuming projects. In particular, IPD could be used to more fully investigate the influence of covariates on heterogeneity of treatment effects, both within and between trials. The impact of heterogeneity, or use of random effects, are seldom discussed. There is thus considerable scope for enhancing the methods of analysis and presentation of IPD meta-analysis.

Statistical design and analysis of pharmacogenetic trials

Pharmacogenetic trials investigate the effect of genotype on treatment response. When there are two or more treatment groups and two or more genetic groups, investigation of gene-treatment interactions is of key interest. However, calculation of the power to detect such interactions is complicated because this depends not only on the treatment effect size within each genetic group, but also on the number of genetic groups, the size of each genetic group, and the type of genetic effect that is both present and tested for. The scale chosen to measure the magnitude of an interaction can also be problematic, especially for the binary case. Elston et al. proposed a test for detecting the presence of gene-treatment interactions for binary responses, and gave appropriate power calculations. This paper shows how the same approach can also be used for normally distributed responses. We also propose a method for analysing and performing sample size calculations based on a generalized linear model (GLM) approach. The power of the Elston et al. and GLM approaches are compared for the binary and normal case using several illustrative examples. While more sensitive to errors in model specification than the Elston et al. approach, the GLM approach is much more flexible and in many cases more powerful. Copyright © 2005 John Wiley & Sons, Ltd.

An adaptive group sequential design for phase II/III clinical trials that select a single treatment from several

There is increasing interest in combining Phases II and III of clinical development into a single trial in which one of a small number of competing experimental treatments is ultimately selected and where a valid comparison is made between this treatment and the control treatment. Such a trial usually proceeds in stages, with the least promising experimental treatments dropped as soon as possible. In this paper we present a highly flexible design that uses adaptive group sequential methodology to monitor an order statistic. By using this approach, it is possible to design a trial which can have any number of stages, begins with any number of experimental treatments, and permits any number of these to continue at any stage. The test statistic used is based upon efficient scores, so the method can be easily applied to binary, ordinal, failure time, or normally distributed outcomes. The method is illustrated with an example, and simulations are conducted to investigate its type I error rate and power under a range of scenarios.

The development of indigo reduction methods and pre-reduced indigo products

The continuing importance of blue denim maintains indigo as an important vat dye industrially. In this review, we examine the various methods that have been used in the past and are currently used to reduce and dissolve indigo for dyeing. We discuss recent insights into the bacterial fermentation technology, the advantages and disadvantages of the direct chemical methods that have predominated for the last century and potentially cleaner technologies of catalytic hydrogenation and electrochemistry, which are becoming increasingly important. With considerations of environmental impact high on the dyeing industry's agenda, we also discuss the developments that have led to the production of pre-reduced indigo.

Field evaluation of systemic inducing resistance chemicals at different growth stages for the control of apple (Venturia inaequalis) and pear (Venturia pirina) scab

Two field trials were conducted using established apple (Malus cv. Golden Delicious) and pear (Pyrus communis 'Williams' Bon Chretien') to assess the efficacy of three commercially available systemic inducing resistance (SIR) products, Messenger (a.i. Harpin protein), Phoenix (a.i. Potassium phosphite) and Rigel (a.i. Salicylic acid derivative) applied at four different growth stages of tree development (bud break, green cluster, 90% petal fall, early fruitlet) against the foliar pathogens Venturia inaequalis and Venturia pirina which cause apple and pear scab respectively. A conventional synthetic fungicide (penconazole) used within the UK for apple and pear scab control was included for comparison. Little efficacy as scab protectants was demonstrated when each SIR product and penconazole was applied at only two growth stages (bud break, green cluster). However when the above compounds were applied at three or more growth stages efficacy as scab protectants was confirmed. The synthetic fungicide penconazole provided greatest protection against apple and pear scab in both the 2006 and 2007 field trials. There was little difference in the magnitude of scab protection conferred by each SIR agent. Results suggest application of at least three sprays during bud break to early fruitlet formation with an appropriate SIR agent may provide a useful addition to existing methods of apple and pear scab management under field conditions. (C) 2009 Published by Elsevier Ltd.

Active-control trials with binary data: a comparison of methods for testing superiority or non-inferiority using the odds ratio

This paper considers methods for testing for superiority or non-inferiority in active-control trials with binary data, when the relative treatment effect is expressed as an odds ratio. Three asymptotic tests for the log-odds ratio based on the unconditional binary likelihood are presented, namely the likelihood ratio, Wald and score tests. All three tests can be implemented straightforwardly in standard statistical software packages, as can the corresponding confidence intervals. Simulations indicate that the three alternatives are similar in terms of the Type I error, with values close to the nominal level. However, when the non-inferiority margin becomes large, the score test slightly exceeds the nominal level. In general, the highest power is obtained from the score test, although all three tests are similar and the observed differences in power are not of practical importance. Copyright (C) 2007 John Wiley & Sons, Ltd.

UV irradiance as a major influence on growth, development and secondary products of commercial importance in Lollo Rosso lettuce 'Revolution' grown under polyethylene films

The growth and production of anthocyanin, flavonoid and phenolic compounds were evaluated in Lollo Rosso lettuce 'Revolution' grown continuously under films varying in their ability to transmit LTV radiation (completely transparent to IN, transparent above 320, 350, 370 and 3 80 nm and completely opaque to LTV radiation). Plants were grown from seed under UV transparent and UV blocking films and destructively harvested 3-4 weeks after transplanting. Plants under a complete UV blocking film (UV400) produced up to 2.2 times more total above ground dry weight than plants under the UV transparent film. In contrast, anthocyanin content in plants under the UV blocking film was approximately eight times lower than in plants under a UV transparent film. Furthermore, there was a curvilinear relationship between the anthocyanin content and LTV wavelength cutoff such that above 370 run there was no further reduction in anthocyanin content. Fluorescence measurements indicated that photosynthetic performance index was 15% higher under the presence of UVB and UVA (UV280) than under the presence of UVA (UV320) and 53% higher than in the absence of UV radiation suggesting protection of the photosynthetic apparatus possibly by phenolic compounds. These findings are of particular importance as the potential of UV transmitting films to increase secondary compounds may offer the opportunity to produce plants commercially with increased health benefits compared to those grown under conventional films.

Bayesian sample size for exploratory clinical trials incorporating historical data

This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence either that an experimental treatment is better than a control or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving 'non-informative' prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p(0). Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored. Copyright (C) 2007 John Wiley & Sons, Ltd.

Bayesian design and conduct of phase II single-arm clinical trials with binary outcomes: a tutorial

The aim of phase II single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase trials as they take into account information that accrues during a trial. Predictive probabilities are then updated and so become more accurate as the trial progresses. Suitable priors can act as pseudo samples, which make small sample clinical trials more informative. Thus patients have better chances to receive better treatments. The goal of this paper is to provide a tutorial for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, real data from three clinical trials are presented as examples to illustrate how to conduct a Bayesian approach for phase II single-arm clinical trials with binary outcomes.

A comparison of three different models for estimating relative risk in meta-analysis of clinical trials under unobserved heterogeneity

We focus on the comparison of three statistical models used to estimate the treatment effect in metaanalysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model. Copyright (C) 2006 John Wiley & Sons, Ltd.

Modeling cumulative evidence for freedom from disease with applications to BSE surveillance trials

This investigation deals with the question of when a particular population can be considered to be disease-free. The motivation is the case of BSE where specific birth cohorts may present distinct disease-free subpopulations. The specific objective is to develop a statistical approach suitable for documenting freedom of disease, in particular, freedom from BSE in birth cohorts. The approach is based upon a geometric waiting time distribution for the occurrence of positive surveillance results and formalizes the relationship between design prevalence, cumulative sample size and statistical power. The simple geometric waiting time model is further modified to account for the diagnostic sensitivity and specificity associated with the detection of disease. This is exemplified for BSE using two different models for the diagnostic sensitivity. The model is furthermore modified in such a way that a set of different values for the design prevalence in the surveillance streams can be accommodated (prevalence heterogeneity) and a general expression for the power function is developed. For illustration, numerical results for BSE suggest that currently (data status September 2004) a birth cohort of Danish cattle born after March 1999 is free from BSE with probability (power) of 0.8746 or 0.8509, depending on the choice of a model for the diagnostic sensitivity.

Metabolism of Maillard reaction products by the human gut microbiota - implications for health

The human colonic microbiota imparts metabolic versatility on the colon, interacts at many levels in healthy intestinal and systemic metabolism, and plays protective roles in chronic disease and acute infection. Colonic bacterial metabolism is largely dependant on dietary residues from the upper gut. Carbohydrates, resistant to digestion, drive colonic bacterial fermentation and the resulting end products are considered beneficial. Many colonic species ferment proteins but the end products are not always beneficial and include toxic compounds, such as amines and phenols. Most components of a typical Western diet are heat processed. The Maillard reaction, involving food protein and sugar, is a complex network of reactions occurring during thermal processing. The resultant modified protein resists digestion in the small intestine but is available for colonic bacterial fermentation. Little is known about the fate of the modified protein but some Maillard reaction products (MRP) are biologically active by, e.g. altering bacterial population levels within the colon or, upon absorption, interacting with human disease mechanisms by induction of inflammatory responses. This review presents current understanding of the interactions between MRP and intestinal bacteria. Recent scientific advances offering the possibility of elucidating the consequences of microbe-MRP interactions within the gut are discussed.

Developing protocols for nursery based scheduling trials

Most existing crop scheduling models are cultivar specific and are developed using academic resources. As such they rarely meet the particular needs of a grower. A series of protocols have been created to generate effective schedules for a changing product range using data generated on site at a commercial nursery. A screening programme has been developed to help determine a cultivar's photoperiod sensitivity and vernalisation requirement. Experimental conditions were obtained using a cold store facility set to 5degreesC and photoperiod cloches. Eight and 16 hour photoperiod treatments were achieved at low cost by growing plants in cloches of opaque plastic with a motorised rolling screen. Natural light conditions were extended where necessary using a high pressure sodium lamp. Batches of plants were grown according to different schedules based on these treatments. The screening programme found Coreopsis grandiflora 'Flying Saucers' to be a long day plant. Data to form the basis of graphical tracks was taken using variations on commercial schedules. The work provides a nursery based approach to the continuous improvement of crop scheduling practises.

Rapid parallel expression in E-coli and insect cells: Analysis of five lef gene products of the Autographa californica multiple nuclear polyhedrosis virus (AcMNPV)

A number of strategies are emerging for the high throughput (HTP) expression of recombinant proteins to enable structural and functional study. Here we describe a workable HTP strategy based on parallel protein expression in E. coli and insect cells. Using this system we provide comparative expression data for five proteins derived from the Autographa californica polyhedrosis virus genome that vary in amino acid composition and in molecular weight. Although the proteins are part of a set of factors known to be required for viral late gene expression, the precise function of three of the five, late expression factors (lefs) 6, 7 and 10, is unknown. Rapid expression and characterisation has allowed the determination of their ability to bind DNA and shown a cellular location consistent with their properties. Our data point to the utility of a parallel expression strategy to rapidly obtain workable protein expression levels from many open reading frames (ORFs).