A Review of: “Analysis of Clinical Trials Using SAS: A Practical Guide,by A. Dmitrienko,G. Molenberghs,C. Chuang-Stein,and W. Offen”

A testing procedure is proposed to assess the consistency of noninferiority from a collection of trials based on simultaneous t lower confidence bounds or Scheffé's lower confidence bounds. Methods for simultaneous inferences on pairwise or many-to-one comparisons among multiple noninferiority trials are also discussed. To avoid bias due to subjective trial exclusion a tuning parameter k is embedded into the testing procedure to provide flexibility to quantify the “consistency of noninferiority” when the total number of trials is large. The size and power of the proposed test are discussed. The method is illustrated using simulations and real data analysis.     

A Review of: “Interpreting and Reporting Clinical Trials: A Guide to the CONSORT Statement and the Principles of Randomised Controlled Trials,by A. Keech,V. Gebski,and R. Pike (eds.)”

Traditionally, Phase II trials have been conducted as single-arm trials to compare the response probabilities between an experimental therapy and a historical control. Historical control data, however, often have a small sample size, are collected from a different patient population, or use a different response assessment method, so that a direct comparison between a historical control and an experimental therapy may be severely biased. Randomized Phase II trials entering patients prospectively to both experimental and control arms have been proposed to avoid any bias in such cases. The small sample sizes for typical Phase II clinical trials imply that the use of exact statistical methods for their design and analysis is appropriate. In this article, we propose two-stage randomized Phase II trials based on Fisher’s exact test, which does not require specification of the response probability of the control arm for testing. Through numerical studies, we observe that the proposed method controls the type I error accurately and maintains a high power. If we specify the response probabilities of the two arms under the alternative hypothesis, we can identify good randomized Phase II trial designs by adopting the Simon’s minimax and optimal design concepts that were developed for single-arm Phase II trials.     

A Review of: “Design and Analysis of Vaccine Studies,by M. E. Halloran,I. M. Longini,Jr., and C. J. Struchiner,”

Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.     

Statistical Monitoring of Clinical Trials: Fundamentals for Investigators,L. A. Moyé

We consider collecting the measurements of a gold standard and two methods with error from each site of subjects. We propose an asymptotic test statistic to compare the concordance rates of two methods with the gold standard and a closed-form sample size formula. Through simulations, we show that the test statistic accurately controls the type I error in small sample sizes, and the sample size formula accurately maintains the power in various settings. The proposed test statistic and the sample size formula can be easily modified for other indices for agreement such as sensitivity and specificity. A real eye study is taken as an example.     

Minor Saponins from the Leaves of Panax ginseng C.A. Meyer

<正> Five minor compounds isolated from the leaves of Panax ginseng C. A. Meyer were characterized as 20(R)-protopanaxatriol (1), daucosterin (2), 3β, 12β-dihydroxy-dammar-20 (22), 24-diene-3-O-β-D-glucopyranoside (3), 20 (R)-protopanaxadiol-3-O-β-D-glucopyranoside (4) and ginsenoside-Rh_2 (5), respectively, on the basis of spectral analyses and chemical evidence. The two new saponins, 3 and 4, were named as ginsenoside-Rh_3 and 20(R)-ginsenoside-Rh_2.Nine other major saponins obtained simultaneously were identical with ginsenoside-Rh_1(6),-Rg_3 (7), -Rg_2 (8), -Rg_1 (9),-Re(10),-Rd (11), -Rc (12), -Rb_2(13) and Rb_1 (14), respectively.