Use of likelihood estimates for variances for the design and evaluation of multiregional clinical trials with heterogeneous variances.

Globalized drug development studies, such as multiregional clinical trials (MRCTs), have attracted much attention due to their ability to expedite drug development and shorten the time lag of drug release. While observing the overall effect of a new drug, the region-specific effects to support drug registration in constituent regions can also be evaluated. Several challenges arise in conducting MRCTs, such as the heterogeneity in the variability of the primary endpoint across regions. However, most of the existing statistical methods assume a common variability, which may not be valid in practice due to differences across regions (eg, diversities in ethnicity or disparities in medical culture/practice). We present a statistical method for the design and evaluation of MRCTs to consider the heterogeneous variability across regions. We assessed the overall sample size requirement and addressed the region-specific sample size determination to establish the consistency of treatment effects between the specific region and the entire group. We demonstrate the proposed approach with numerical examples.

Use of a tolerance interval approach as a statistical quality control tool for traditional Chinese medicine.

Raw materials for traditional Chinese medicine (TCM) are often from different resources and its final product may also be made by different sites. Therefore, variabilities from different resources such as site-to-site or within site component-to-component may be expected. Consequently, test for consistency in raw materials, in-process materials, and/or final product has become an important issue in the quality control (QC) process in TCM development. In this paper, a statistical QC process for raw materials and/or the final product of TCM is proposed based on a two sided [Formula: see text]-content, [Formula: see text]-confidence tolerance interval. More specifically, we construct the tolerance interval for a random-effects model to assess the QC of TCM products from different regions and possibly different product batches. The products can be claimed to be consistency when the constructed tolerance interval is within the permitted range. Given the region and batch effects, sample sizes can also be calculated to ensure the desired measure of goodness. An example is presented to illustrate the proposed approach.

A functional inference for multivariate current status data with mismeasured covariate.

Covariate measurement error problems have been recently studied for current status failure time data but not yet for multivariate current status data. Motivated by the three-hypers dataset from a health survey study, where the failure times for three-hypers (hyperglycemia, hypertension, hyperlipidemia) are subject to current status censoring and the covariate self-reported body mass index may be subject to measurement error, we propose a functional inference method under the proportional odds model for multivariate current status data with mismeasured covariates. The new proposal utilizes the working independence strategy to handle correlated current status observations from the same subject, as well as the conditional score approach to handle mismeasured covariate without specifying the covariate distribution. The asymptotic theory, together with a stable computation procedure combining the Newton-Raphson and self-consistency algorithms, is established for the proposed estimation method. We evaluate the method through simulation studies and illustrate it with three-hypers data.

Sample size estimation of multiregional clinical trials with heterogeneous variability across regions.

In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and shorten approval time, the design of multiregional clinical trials (MRCTs) incorporates subjects from many countries around the world under the same protocol. After showing the overall efficacy of a drug in all global regions, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each of them. Several statistical methods have been proposed for the design and evaluation of MRCTs. Most of these approaches, however, assume a common variability of the primary endpoint across regions. In practice, this assumption may not be true, due to differences across regions (e.g., differences in ethnic factors and/or medical culture/practice). In this article, we use a random-effect model for modeling heterogeneous variability across regions for the design and evaluation of MRCTs. We also address consideration on the determination of the number of subjects in a specific region to establish the consistency of treatment effects between the specific region and the entire group.

Translation in different diagnostic procedures---traditional Chinese medicine and Western medicine.

Recently, the modernization of traditional Chinese medicines (TCM) for treatment of patients with critical and/or life-threatening diseases has attracted much attention in the pharmaceutical industry. However, there exist essential differences in the evaluation of the efficacy and safety of a TCM as compared with a typical Western medicine (WM), even though they are for the same indication. Therefore, the modernization of a TCM should be based on a scientific evaluation of the safety and effectiveness of the TCM in terms of well-established quantitative criteria. We propose a study design to study the calibration and validation of the Chinese diagnostic procedure for evaluation of a TCM, with respect to a well-established clinical endpoint for evaluation of a WM. Statistical validation of such an instrument is essential to have an accurate and reliable clinical assessment of the performance of the TCM. Similar to the validation of a typical quality of life instrument, some validation performance characteristics such as validity, reliability, and ruggedness are considered. In this article, a design for validation of a standard quantitative instrument to be commonly employed for diagnosis of patient function/activity, performance, disease signs and symptoms, and disease status and severity based on Chinese diagnostic practice is proposed. Methods for statistical validation of the standard instrument are derived. More specifically, for validation of the TCM diagnostic instrument, we consider the following validation performance characteristics (parameters): validity (or accuracy), reliability (or precision), and ruggedness (interrater variability). A numerical example is given to illustrate the proposed methods for validation of the Chinese diagnostic procedure.

A Bayes regression approach to array-CGH data.

This paper develops a Bayes regression model having change points for the analysis of array-CGH data by utilizing not only the underlying spatial structure of the genomic alterations but also the observation that the noise associated with the ratio of the fluorescence intensities is bigger when the intensities get smaller. We show that this Bayes regression approach is particularly suitable for the analysis of cDNA microarray-CGH data, which are generally noisier than those using genomic clones. A simulation study and a real data analysis are included to illustrate this approach.