Validation and adjustment of the mathematical prediction model for human sweat rate responses to outdoor environmental conditions

Under outdoor conditions this model was over estimating sweat loss response in shaded (low solar radiation) environments, and underestimating the response when solar radiation was high (open field areas). The present study was conducted in order to adjust the model to be applicable under outdoor environmental conditions. Four groups of fit acclimated subjects participated in the study. They were exposed to three climatic conditions (30 degrees, 65% rh; 31 degrees C, 40% rh; and 40 degrees C, 20% rh) and three levels of metabolic rate (100, 300 and 450 W) in shaded and sunny areas while wearing shorts, cotton fatigues (BDUs) or protective garments. The original predictive equation for sweat loss was adjusted for the outdoor conditions by evaluating separately the radiative heat exchange, short-wave absorption in the body and long-wave emission from the body to the atmosphere and integrating them in the required evaporation component (Ereq) of the model, as follows: Hr = 1.5SL0.6/I(T) (watt) H1 = 0.047Me.th/I(T) (watt), where SL is solar radiation (W.m-2), Me.th is the Stephan Boltzman constant, and I(T) is the effective clothing insulation coefficient. This adjustment revealed a high correlation between the measured and expected values of sweat loss (r = 0.99, p < 0.0001).     

A generalized F mixture model for cure rate estimation

Cure rate estimation is an important issue in clinical trials for diseases such as lymphoma and breast cancer and mixture models are the main statistical methods. In the last decade, mixture models under different distributions, such as exponential, Weibull, log-normal and Gompertz, have been discussed and used. However, these models involve stronger distributional assumptions than is desirable and inferences may not be robust to departures from these assumptions. In this paper, a mixture model is proposed using the generalized F distribution family. Although this family is seldom used because of computational difficulties, it has the advantage of being very flexible and including many commonly used distributions as special cases. The generalised F mixture model can relax the usual stronger distributional assumptions and allow the analyst to uncover structure in the data that might otherwise have been missed. This is illustrated by fitting the model to data from large-scale clinical trials with long follow-up of lymphoma patients. Computational problems with the model and model selection methods are discussed. Comparison of maximum likelihood estimates with those obtained from mixture models under other distributions are included.