Biomedical subjects

Valerii Fedorov

Publications and source records attributed to Valerii Fedorov.

5 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↗

PubMed2005

The design of multicentre trials.

The analysis of data collected in multicentre trials offers challenges because the data from the individual centres must be combined in some way to give an overall evaluation of the differences between the treatments in the trial. We propose that the combined response to treatment (CRT) be used as this overall measure. The definition and estimation of the CRT can be derived from either a fixed-effects or a random-effects model. For the latter we introduce the ECRT--the expected combined response to treatment. We describe and compare both types of model and express our preference for the random-effects model. We stress that the number of patients enrolled at a centre is a random variable and show that this source of randomness inflates the variance of the estimated ECRT. Variability in enrolment rates over the centres further inflates this variance. A simple conclusion from our results is that if variability in the treatment and centre effects, in the enrolment time, in the number of patients enrolled at a centre and in the enrolment rates is not properly accounted for, then an underpowered trial may result. Using properties of estimators generated by the random-effects model we propose methods for determining the optimal number of centres and total number of patients to enrol in a trial to minimize a loss function that accounts for centre and patient costs and loss of revenue. We discuss variants of the loss function and corresponding optimization problems for different types of enrolment. We end the paper with brief generalizations of the developed techniques to the case where the response is binary.

Depression↗

PubMed2004

Estimation of the treatment difference in multicenter trials.

The three fixed effects estimators of a treatment,difference are compared under conditions of random enrollment in a multicenter clinical trial. These comparisons are performed by assuming five different enrollment schemes. The estimators are compared via simulation using their expected mean squared errors. Unlike previous discussions of these three estimators, we take explicit account of the effect of centers that fail to enroll patients to one or both treatment arms. Within each center, we assume enrollment follows a Poisson process and consider the two situations in which the mean rate of this process is the same in every center and in which the mean rates are sampled from a gamma distribution. The effect of patient dropout is studied as well as the effect of increasing the number of centers. Simulations show that for many sound scenarios, the simpler estimator corresponding to the simplest model works better, even for the cases when data are generated by more complex models.

Algorithms↗

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↗