Journal of biopharmaceutical statistics
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A generic template for clinical trials simulations that are typically required by statisticians is developed. Realistic clinical trials data sets are created using a unifying model that allows general correlation structures for endpoint*timepoint data and nonnormal distributions (including time-to-event), and computationally efficient algorithms are presented. ⋯ A grid-enabled SAS-based system has been developed to implement this model; details are presented summarizing the system development. An example illustrating use of the system is given.
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While randomized, well-controlled, clinical trials have been viewed as the gold standard in the evaluation of medical products, it is not uncommon for medical device clinical studies to depart from the paradigm of randomized trials, due to ethical or practical reasons. In nonrandomized studies, the advantages of well-designed and conducted randomized clinical trials are no longer available, and consequently the statistical inference obtained from such studies may carry a lower level of scientific assurance, compared to randomized trials. This paper provides a brief overview of nonrandomized medical device clinical studies in terms of design and statistical analysis as well as regulatory issues, including some challenges that frequently arise in those endeavors.
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Analysis of repeated binary measurements presents a challenge in terms of the correlation between measurements within an individual and a mixed-effects modelling approach has been used for the analysis of such data. Sample size calculation is an important part of clinical trial design and it is often based on the method of analysis. We present a method for calculating the sample size for repeated binary pharmacodynamic measurements based on analysis by mixed-effects modelling and using a logit transformation. ⋯ The proposed method has been assessed via simulation of a linear model and estimation using NONMEM. The results showed good agreement between nominal power and power estimated from the NONMEM simulations. The results also showed that sample size increases with increased variability at a rate that depends on the difference in parameter estimates between groups, and designs that involve sampling based on an optimal design can help to reduce cost.
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The Wilcoxon-Mann-Whitney (WMW) test is the most commonly used nonparametric method to compare two treatments when the underlying distribution of the outcome variable is not normally distributed. In the presence of stratum effects, the van Elteren (vE) test, a stratified WMW test, can be used to adjust for the stratum effect. We provide guidance on how to choose between the two tests in the design phase of clinical trials and in the analysis of clinical data. ⋯ In comparing powers, we found that the WMW test is better when the stratum effects are small, whereas the vE test is better when the stratum effects are large. Finally, when the stratum effects are moderate, the decision depends on the shape of the distribution and the ratio of the number of strata and the number of subjects. In this case, results presented in this article or from similar simulations may be used to determine which test is better.
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Comparative Study
Experience with reviewing Bayesian medical device trials.
The purpose of this paper is to present a statistical reviewer's perspective on some technical aspects of reviewing Bayesian medical device trials submitted to the Food and Drug Administration. The discussion reflects the experiences of the authors and should not be misconstrued as official guidance by the FDA. ⋯ In addition to Bayesian analysis of trials, Bayesian trial design and Bayesian monitoring are discussed. Analyses were implemented in WinBUGS (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml), with the code provided.