Evoked potentials in clinical trials for multiple sclerosis

BACKGROUND: Idiopathic restrictive cardiomyopathy is a rare disease characterized by diastolic dysfunction, and the pathogenesis of the stiff heart remains unclear. The purpose of this study was to analyze the subpopulation of collagen fibers and determine the expression of matrix metalloproteinase in restrictive cardiomyopathy. METHODS AND RESULTS: In endomyocardial biopsy specimens obtained from seven patients with restrictive cardiomyopathy, collagen fiber types I, III, and IV, and matrix metalloproteinase- and two were observed by light and electron microscopy, using monoclonal antibodies. Type I collagen was less prominent in the interstitium, whereas the immunoreactivity for type III collagen was marked. The immunoreactivity against matrix metalloproteinase-1 was observed along with types I and III collagen fibers and in the cytoplasm of some fibrocytes/fibroblasts. The matrix metalloproteinase-1 tended to increase when the reactivity against types I and III collagen was prominent. Both type IV collagen and matrix metalloproteinase-2 were observed along arterial walls and the basement membrane of cardiocytes. CONCLUSIONS: Increased type III collagen may play an important role as the cause of left ventricular stiffness in restrictive cardiomyopathy. The matrix metalloproteinase appeared to be involved in a cascade of collagen synthesis and the remodeling of the heart in patients with restrictive cardiomyopathy.     

Interferon beta treatment in multiple sclerosis: the European clinical trials

Studies have demonstrated the failure of gut barrier function in all cases of experimental acute diffuse peritonitis. Histobacterioscopy and electron microscopy showed the occurrence of bacteria under the basal membrane, in lymphatic and blood capillaries of the small intestinal villi. Experimental and clinical trials with blood sampling from different regions of the circulation have demonstrated the gut origin polymicrobial bacteremia in 66% of patients and in 75% of experimental animals with acute diffuse peritonitis.     

Clinical trials: are they really useful for clinical decision making?

For the first time salvarsan treatment in Cracow was successfully held in Opthalmology Clinic of Jagiellonian University by Prof. Boles?aw Wicherkiewicz, who used preparation 606 in three cases at ophthalmic complications caused by syphilis infection. He received this preparation directly from P. Ehrlich.     

Some comments on frequently used multiple endpoint adjustment methods in clinical trials

Confirmatory clinical trials often classify clinical response variables into primary and secondary endpoints. The presence of two or more primary endpoints in a clinical trial usually means that some adjustments of the observed p-values for multiplicity of tests may be required for the control of the type I error rate. In this paper, we discuss statistical concerns associated with some commonly used multiple endpoint adjustment procedures. We also present limited Monte Carlo simulation results to demonstrate the performance of selected p-value-based methods in protecting the type I error rate.     

A case for Bayesianism in clinical trials

This paper describes a Bayesian approach to the design and analysis of clinical trials, and compares it with the frequentist approach. Both approaches address learning under uncertainty. But they are different in a variety of ways. The Bayesian approach is more flexible. For example, accumulating data from a clinical trial can be used to update Bayesian measures, independent of the design of the trial. Frequentist measures are tied to the design, and interim analyses must be planned for frequentist measures to have meaning. Its flexibility makes the Bayesian approach ideal for analysing data from clinical trials. In carrying out a Bayesian analysis for inferring treatment effect, information from the clinical trial and other sources can be combined and used explicitly in drawing conclusions. Bayesians and frequentists address making decisions very differently. For example, when choosing or modifying the design of a clinical trial, Bayesians use all available information, including that which comes from the trial itself. The ability to calculate predictive probabilities for future observations is a distinct advantage of the Bayesian approach to designing clinical trials and other decisions. An important difference between Bayesian and frequentist thinking is the role of randomization.     

Sample size determination for phase II clinical trials based on Bayesian decision theory

This paper describes an application of Bayesian decision theory to the determination of sample size for phase II clinical studies. The approach uses the method of backward induction to obtain group sequential designs that are optimal with respect to some specified gain function. A gain function is proposed focussing on the financial costs of, and potential profits from, the drug development programme. On the basis of this gain function, the optimal procedure is also compared with an alternative Bayesian procedure proposed by Thall and Simon. The latter method, which tightly controls type I error rate, is shown to lead to an expected gain considerably smaller than that from the optimal test. Gain functions with respect to which Thall and Simon's boundary is optimal are sought and it is shown that these can only be of the form considered, that is, with constant cost for phase III study and cost of the phase II study proportional to the sample size, if potential profit increases over time.     

An overview of statistical methods for multiple failure time data in clinical trials

In a long term clinical trial to evaluate a new treatment, quite often each study subject may experience a number of 'failures' that correspond to repeated occurrences of the same type of event or events of entirely different natures during his/her follow-up period. To obtain efficient inference procedures for the therapeutic effect over time, it is desirable to utilize those multiple event times in the analysis. In this article, we review some useful procedures for analysing different kinds of multivariate failure time data. Specifically, we discuss the two-sample problems and the general regression problems with various survival models. We also give some recommendations of appropriate procedures for each type of multiple event data structure for practical usage.     

A modified balance-sheet procedure for decision making in therapy: Cost-cost comparisons.

This article describes a simple intervention that can be used when clients find it difficult to make a decision: to weigh the costs and ignore the benefits of any alternative choices considered. A critique of the literature shows the advantages of this approach over I. L. Janis and L. Mann's (1977) full-scale balance-sheet procedure. Three case examples are presented to demonstrate the effectiveness of the cost–cost comparison. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Some extensions and applications of a Bayesian strategy for monitoring multiple outcomes in clinical trials

We present some practical extensions and applications of a strategy proposed by Thall, Simon and Estey for designing and monitoring single-arm clinical trials with multiple outcomes. We show by application how the strategy may be applied to construct designs for phase IIA activity trials and phase II equivalence trials. We also show how it may be extended to incorporate the use of mixture priors in settings where a Dirichlet distribution does not adequately quantify prior experience, randomized phase II selection trials involving two or more experimental treatments, and trials with group-sequential monitoring for applications involving multiple institutions.     

Self-designing clinical trials

I present a method of sequential analysis for randomized clinical trials that allows use of all prior data in a trial to determine the use and weighting of subsequent observations. One continues to assign subjects until one has 'used up' all the variance of the test statistic. There are many strategies to determine the weights including Bayesian methods (though the proposal is a frequentist design). I explore further the self-designing aspect of the randomized trial to note that in some cases it makes good sense (i) to change the weighting on components of a multivariate endpoint, (ii) to add or drop treatment arms (especially in a parallel group dose ranging/efficacy/safety trial), (iii) to select sites to use as the trial goes on, (iv) to change the test statistic and (v) even to rethink the whole drug development paradigm to shorten drug development time while keeping current standards for the level of evidence necessary for approval.