Biomedical subjects

Vladimir Dragalin

Publications and source records attributed to Vladimir Dragalin.

4 recordsLinked to original sources

PubMed2006

Design of multi-centre trials with binary response.

We propose a correlated beta-binomial model for the binary response in multi-centre trials. The likelihood function in this case has a closed-form and we avoid multivariate numerical integrations in determining the maximum likelihood estimator. Based on derived asymptotic variance-covariance matrix of the MLE, we obtain relatively simple formulae that relate the number of centres, the total number of patients and the precision of the parameter estimate.

Biometry↗

PubMed2006

Adaptive designs in clinical drug development--an Executive Summary of the PhRMA Working Group.

A PhRMA Working Group on adaptive clinical trial designs has been formed to investigate and facilitate opportunities for wider acceptance and usage of adaptive designs and related methodologies. A White Paper summarizing the findings of the group is in preparation; this article is an Executive Summary for that full White Paper, and summarizes the findings and recommendations of the group. Logistic, operational, procedural, and statistical challenges associated with adaptive designs are addressed. Three particular areas where it is felt that adaptive designs can be utilized beneficially are discussed: dose finding, seamless Phase II/III trials designs, and sample size reestimation.

Biomedical Research↗

PubMed2003

Kullback-Leibler divergence for evaluating bioequivalence.

In this paper we propose a methodology for evaluating the bioequivalence of two formulations of a drug that encompasses not only average bioequivalence (ABE), but also the more recently introduced measures of population bioequivalence (PBE) and individual bioequivalence (IBE). The latter two measures are concerned with prescribability (PBE) and switchability (IBE). The main idea is to use the Kullback-Leibler divergence (KLD) as a measure of discrepancy between the distributions of the two formulations. Two formulations are declared bioequivalent if the upper bound of a level-alpha confidence interval for the KLD is less than a given goalpost to be set by a regulator. This new methodology overcomes many of the disadvantages of the corresponding measures recommended by the FDA. In particular the KLD: (i) possesses the natural hierarchical property that IBE => PBE => ABE; (ii) satisfies the properties of a true distance metric; (iii) is invariant to monotonic transformations of the data; (iv) generalizes easily to the multivariate case where equivalence on more than one parameter (for example, AUC, C(max) and T(max)) is required; and (v) is applicable over a wide range of distributions of the response variable (for example, those in the exponential family). The performance of the KLD relative to the metric proposed in guidance by the FDA for the evaluation of individual bioequivalence is evaluated using a simulation study. Previously published retrospective analyses using the FDA-proposed metric are contrasted with those based on the KLD. It is concluded that the KLD is a viable alternative to the FDA-proposed metric and that its mathematical and statistical properties make it a readily interpretable measure of the differences between formulations.

Area Under Curve↗

PubMed2002

Statistical approaches to establishing vaccine safety.

In a vaccine safety trial, the primary interest is to demonstrate that the vaccine is sufficiently safe, rejecting the null hypothesis that the relative risk of an adverse event attributable to the new vaccine is above a prespecified value, greater than one. We evaluate the exact probability of type I error of the likelihood score test, with sample size determined by normal approximation, by enumeration of the binomial outcomes in the rejection region and show that it exceeds the nominal level. In the case of rare adverse events, we recommend the Poisson approximation as an alternative and develop the corresponding conditional and unconditional tests. We give sample size and power calculations for these tests. We also propose optimal randomization strategies which either (i) minimize the total number of adverse cases or (ii) minimize the expected number of subjects when the vaccine is unsafe. We illustrate the proposed methods using a hypothetical vaccine safety study.

Humans↗