Issues in applying recent CPMP ‘Points to Consider’ and FDA guidance documents with biostatistical implications

The International Conference on Harmonisation guideline ‘Statistical Principles for Clinical Trials’ was adopted by the Committee for Proprietary Medicinal Products (CPMP) in March 1998, and consequently is operational in Europe. Since then more detailed guidance on selected topics has been issued by the CPMP in the form of ‘Points to Consider’ documents. The intent of these was to give guidance particularly to non‐statistical reviewers within regulatory authorities, although of course they also provide a good source of information for pharmaceutical industry statisticians. In addition, the Food and Drug Administration has recently issued a draft guideline on data monitoring committees. In November 2002 a one‐day discussion forum was held in London by Statisticians in the Pharmaceutical Industry (PSI). The aim of the meeting was to discuss how statisticians were responding to some of the issues covered in these new guidelines, and to document consensus views where they existed. The forum was attended by industry, academic and regulatory statisticians. This paper outlines the questions raised, resulting discussions and consensus views reached. It is clear from the guidelines and discussions at the workshop that the statistical analysis strategy must be planned during the design phase of a clinical trial and carefully documented. Once the study is complete the analysis strategy should be thoughtfully executed and the findings reported. Copyright © 2003 John Wiley & Sons, Ltd.     

H-likelihood: problems and solutions

In recent issues of this journal it has been asserted in two papers that the use of h-likelihood is wrong, in the sense of giving unsatisfactory estimates of some parameters for binary data (Kuk and Cheng, 1999; Waddington and Thompson, 2004) or theoretically unsound (Kuk and Cheng, 1999). We wish to refute both these assertions.     

Diagonalization matrix and its application in distribution theory

Some matrix representations of diverse diagonal arrays are studied in this work; the results allow new definitions of classes of elliptical distributions indexed by kernels mixing Hadamard and usual products. A number of applications are derived in the setting of prior densities from the Bayesian multivariate regression model and families of non-elliptical distributions, such as the matrix multivariate generalized Birnbaum–Saunders density. The philosophy of the research about matrix representations of quadratic and inverse quadratic forms can be extended as a methodology for exploring possible new applications in non-standard distributions, matrix transformations and inference.     

Cross-over trials in drug development: theory and practice

It is maintained that much research into the design and analysis of cross-over trials has been of little practical relevance to drug development. The point is illustrated using three topics: the AB/BA design, bioequivalence and multi-period designs in two treatments. It is suggested that statisticians should pay more attention to the work of fellow scientists, in particular, in the field of pharmacokinetics, and also that the philosophical–inferential base employed in examining cross-over trials has often been too narrow.     

Asymptotic expansions in non-linear renewal theory

We obtain first order asymptotic expansions for the distribution of the excess of a standard normal random walk over a curved boundary and the error probabilities of some repeated significance tests. The key step in the analysis is an asymptotic expansion for the conditional probability that the random walk has not crossed the boundary before the N step, given that it is near the boundary after the n^th step.     

Some applications of lagrangian distributions in queueing theory and epidemiology

In this note we demonstrate that the Lagrangian distributions have applications in queueing theory and theory of epidemics. The Lagrangian distribution appears as the distribution describing the number of customers served in a busy period under certain conditions. Also, the Lagrangian distribution describes the distribution of number of persons infected by a certain infectious disease.