On the test of liu and chow's procedure for assessing equivalence in variability of bioavailability

Similar to Schuirmann's two one-sided tests procedure for assessment of bioequivalence in average bioavailability (Schuirmann,), Liu and Chow proposed a two one-sided tests procedure for assessment of equivalence of variability of bioavailability. Their procedure is derived based on the correlation between crossover differences and subject totals. In this paper, we examined the performance of their test procedure in terms of its test size and power for various situations where the intersubject variability and the intrasubject variability of the test drug product are relatively larger, similar, and smaller than that of the intrasubject variability of the reference drug product.     

My Story: Blogs by Four Military Teens,by Sherman,M. D., & Sherman,D. M.

Abstract

GROUP WORK WITH POPULATIONS AT RISK. SECOND EDITION. Reviewed by Geoffrey L. Greif and Paul H. Ephross (Eds.) New York: Oxford University Press, 2005, 480 pp. reviewed by Barbara Muskat

TRANSFORMING THE LEGACY: COUPLE THERAPY WITH SURVIVORS OF CHILDHOOD TRAUMA. Reviewed by Kathryn Basham and Dennis Miehls. New York: Columbia University Press, 2004, 368 pp. reviewed by Rachel Birnbaum     

The Impact of Complementary Agglomeration and Multi‐unit Systems on New Product Introduction

This study explores the relationship between organizational agglomeration and new product introduction. It proposes that product‐complementary agglomeration increases the likelihood of new product introduction, but that the effect on new product introduction is non‐linear. In addition, the influence of agglomeration on new product introduction is contingent on organizational form (i.e. multi‐unit form or independent form). Using longitudinal data for the hospital industry in Taiwan from 1997 to 2002, we found that the relationship between product‐complementary agglomeration and new product introduction is an upward trending hooked curve. As the degree of complementary agglomeration increases, the likelihood of introducing new products also increases, but the rate of increase diminishes with the degree of complementary agglomeration. In addition, we also found that the positive effect of product‐complementary agglomeration on new product introduction is stronger for independent firms than for multi‐unit firms.     

Measuring robustness for weighted distributions: Bayesian perspective

There are many situations where the usual random sample from a population of interest is not available, due to the data having unequal probabilities of entering the sample. The method of weighted distributions models this ascertainment bias by adjusting the probabilities of actual occurrence of events to arrive at a specification of the probabilities of the events as observed and recorded. We consider two different classes of contaminated or mixture of weight functions, Γ _a ={w(x):w(x)=(1−ε)w ₀(x)+εq(x),q∈Q} and Γ _g ={w(x):w(x)=w ₀ ^1−ε (x)q ^ε(x),q∈Q} wherew ₀(x) is the elicited weighted function,Q is a class of positive functions and 0≤ε≤1 is a small number. Also, we study the local variation of ϕ-divergence over classes Γ _a and Γ _g . We devote on measuring robustness using divergence measures which is based on the Bayesian approach. Two examples will be studied.     

Random Level-Shift Time Series Models,ARIMA Approximations,and Level-Shift Detection

The main purpose of this article is to assess the performance of autoregressive integrated moving average (ARIMA) models when occasional level shifts occur in the time series under study. A random level-shift time series model that allows the level of the process to change occasionally is introduced. Between two consecutive changes, the process behaves like the usual autoregressive moving average (ARMA) process. In practice, a series generated from a random level-shift ARMA (RLARMA) model may be misspecified as an ARIMA process. The efficiency of this ARIMA approximation with respect to estimation of current level and forecasting is investigated. The results of examining a special case of an RLARMA model indicate that the ARIMA approximations are inadequate for estimating the current level, but they are robust for forecasting future observations except when there is a very low frequency of level shifts or when the series are highly negatively correlated. A level-shift detection procedure is presented to handle the low-frequency level-shift phenomena, and its usefulness in building models for forecasting is demonstrated.