Resistance training induced increase in VO2max in young and older subjects

It is an undeniable fact that resistance training (RT) is a potent stimulus for muscle hypertrophy and strength gain, but it is less understood whether RT can increase maximal aerobic capacity (VO₂max). The purpose of this brief review is to discuss whether or not RT enhances VO₂max in young (20–40 years) and older subjects (>60 years). Only 3 of 17 studies involving young subjects have indicated significant increases in VO₂max following RT, while six of nine studies in older subjects have reported significant improvements in VO₂max following RT. There was a significant negative correlation between the initial VO₂max and RT-induced change in VO₂max. This result suggests that RT-induced increase in VO₂max is dependent upon the subject’s initial VO₂max. The RT-induced increase in VO₂max may be elicited when their initial relative VO₂max is lower than 25 ml/kg/min for older subjects and lower than 40 ml/kg/min for young subjects. Thus, RT can be expected to improve concurrently both muscular and cardiovascular fitnesses within a single mode of RT when young and old persons have initially low fitness levels.     

Preference Aggregation With Incomplete Information

We show in an environment of incomplete information that monotonicity and the Pareto property applied only when there is common knowledge of Pareto dominance imply (i) there must exist a common prior over the smallest common knowledge event, and (ii) aggregation must be ex ante and ex post utilitarian with respect to that common prior and individual von Neumann–Morgenstern utility indices.     

An adaptive model switching approach for phase I dose‐finding trials

Model‐based phase I dose‐finding designs rely on a single model throughout the study for estimating the maximum tolerated dose (MTD). Thus, one major concern is about the choice of the most suitable model to be used. This is important because the dose allocation process and the MTD estimation depend on whether or not the model is reliable, or whether or not it gives a better fit to toxicity data. The aim of our work was to propose a method that would remove the need for a model choice prior to the trial onset and then allow it sequentially at each patient's inclusion. In this paper, we described model checking approach based on the posterior predictive check and model comparison approach based on the deviance information criterion, in order to identify a more reliable or better model during the course of a trial and to support clinical decision making. Further, we presented two model switching designs for a phase I cancer trial that were based on the aforementioned approaches, and performed a comparison between designs with or without model switching, through a simulation study. The results showed that the proposed designs had the advantage of decreasing certain risks, such as those of poor dose allocation and failure to find the MTD, which could occur if the model is misspecified. Copyright © 2013 John Wiley & Sons, Ltd.     

On Testing Sample Selection Bias Under the Multicollinearity Problem

ABSTRACT

This paper reviews and extends the literature on the finite sample behavior of tests for sample selection bias. Monte Carlo results show that, when the “multicollinearity problem” identified by Nawata (1993 Nawata , K. ( 1993 ). A note on the estimation of models with sample-selection biases . Economics Letters 42 : 15 – 24 . [CSA] [CROSSREF] [Crossref], [Web of Science ®] , [Google Scholar]) is severe, (i) the t-test based on the Heckman–Greene variance estimator can be unreliable, (ii) the Likelihood Ratio test remains powerful, and (iii) nonnormality can be interpreted as severe sample selection bias by Maximum Likelihood methods, leading to negative Wald statistics. We also confirm previous findings (Leung and Yu, 1996 Leung , S. F. , Yu , S. ( 1996 ). On the choice between sample selection and two-part models . Journal of Econometrics 72 : 197 – 229 . [CSA] [CROSSREF] [Crossref], [Web of Science ®] , [Google Scholar]) that the standard regression-based t-test (Heckman, 1979 Heckman , J. J. ( 1979 ). Sample selection bias as a specification error . Econometrica 47 : 153 – 161 . [CSA] [Crossref], [Web of Science ®] , [Google Scholar]) and the asymptotically efficient Lagrange Multiplier test (Melino, 1982 Melino , A. ( 1982 ). Testing for sample selection bias . Review of Economic Studies 49 : 151 – 153 . [CSA] [Crossref], [Web of Science ®] , [Google Scholar]), are robust to nonnormality but have very little power.