Byron Jones
Pfizer
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Publication
Featured researches published by Byron Jones.
Drug Information Journal | 2005
Scott D. Patterson; Byron Jones; Névine Zariffa
The International Conference on Harmonisation E14 (2004) calls for public comment by statisticians on the practical implications of the proposed guidance to monitor for QTc prolongation. Of particular interest is consideration of the statistical properties of various end points proposed for measurement. Methods of analysis and statistical inference are developed for the QTc end points considered.
Pharmaceutical Statistics | 2012
Scott Patterson; Byron Jones
The two one-sided test procedure (TOST) has been used for average bioequivalence testing since 1992 and is required when marketing new formulations of an approved drug. TOST is known to require comparatively large numbers of subjects to demonstrate bioequivalence for highly variable drugs, defined as those drugs having intra-subject coefficients of variation greater than 30%. However, TOST has been shown to protect public health when multiple generic formulations enter the marketplace following patent expiration. Recently, scaled average bioequivalence (SABE) has been proposed as an alternative statistical analysis procedure for such products by multiple regulatory agencies. SABE testing requires that a three-period partial replicate cross-over or full replicate cross-over design be used. Following a brief summary of SABE analysis methods applied to existing data, we will consider three statistical ramifications of the proposed additional decision rules and the potential impact of implementation of scaled average bioequivalence in the marketplace using simulation. It is found that a constraint being applied is biased, that bias may also result from the common problem of missing data and that the SABE methods allow for much greater changes in exposure when generic-generic switching occurs in the marketplace.
Journal of Biopharmaceutical Statistics | 2004
Valerii V. Fedorov; Byron Jones; Matthew Jones; Anatoly Zhigljavsky
Abstract 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.
Communications in Statistics-theory and Methods | 2005
Valerii V. Fedorov; Byron Jones; C. Matthew Jones; Anatoly Zhigljavsky
ABSTRACT We consider the problem of analyzing multi-center clinical trials when the number of patients at each center and on each treatment arm is random and follows the Poisson distribution. Theoretical approximations are made for the first two moments of the mean square errors (MSEs) for three different estimators of treatment effect difference that are commonly used in multi-center clinical trials. To construct these approximations, approximations are needed for the harmonic mean and negative moments of the Poisson distribution. This is achieved through the use of recurrence relations. The accuracy of the approximations for the moments of the MSEs were then validated through comparing the theoretical values to those obtained from a simulation study under two different enrollment environments.
Journal of the National Cancer Institute | 2008
Byron Jones; Scott Haughie
Barton et al. ( 1 ) presented the results of the analyses of various endpoints from a twoperiod crossover trial to compare active drug and placebo for the treatment of loss of libido in female cancer survivors. The authors stated that they had used a methodology for their analyses that “encompasses the state of the science for crossover studies.” However, the results given in their table 2, and the subsequent conclusions made, are clearly not based on the correct crossover trial analyses. The correct methodology is described in Chapter 2 of the book by Jones and Kenward ( 2 ). Curiously, it is stated by Barton et al. that they have followed such methodology, referring to it in their article as the “sums and differences” analysis. However, what the authors have reported in their table 2 are P values for the comparison of drug and placebo in each period. Such comparisons are based on the variability between subjects and are therefore not correct for the analysis of a crossover trial. The correct analysis is based on the within-subject differences between the second and fi rst period measurements [see Section 2.3 of ( 2 )]. The authors also advocate using the test for a carryover difference as a preliminary test for deciding if the test for drug vs placebo should be based either 1) on only the data from the fi rst period or 2) on the data from both periods. This procedure is seriously fl awed, as pointed out by Freeman ( 3 ) and further illustrated in Section 2.7 of ( 2 ).
Communications in Statistics-theory and Methods | 2007
Valerii V. Fedorov; Byron Jones; Vippal Savani; Anatoly Zhigljavsky
The design and analysis of multicenter trials based on a random effects model is well developed for a continuous response, but is less well developed for a binary response. Here we describe a random effects model for a binary response for two treatments and show how maximum likelihood estimates for the unknown treatment difference can be derived using a novel approximation to the likelihood. The suggested approximation is easy to use and seems to be better suited to the problem than the Laplace approximation and the approximation based on adaptive Gaussian quadratures. We also derive an approximation for the Fisher information matrix of the treatment parameters. The results extend those previously reviewed by Agresti and Hartzel (2000).
Journal of Statistical Planning and Inference | 2008
S.T. Bate; Byron Jones
Journal of Statistical Planning and Inference | 2006
S.T. Bate; Byron Jones
Significance | 2008
Byron Jones
Significance | 2008
Byron Jones; Scott Haughie