旋转磁场治疗印^60CO辐射造血损伤小鼠生存分析的实验研究

目的：初步探讨旋转恒定磁场治疗急性骨髓型放射病的效果。方法：BALB／C小鼠按体重随机分为磁疗组和对照组，每组再各分为4组．分别接受0Gy、6．0Gy、8．0Gy、10．0Gy ^60COγ射线全身辐射，照后，对照组不作任何处理，磁疗组接受磁场处理30d，每天2次，每次1．5h，旋转磁场强度为0．6T，比较两组小鼠30d的存活率和存活期。结果：单纯磁场处理对正常小鼠生存状态及存活率无明显影响；10．0Gy组和8．0Gy组小鼠生存率磁疗组与对照组之间比较均无统计学差异（P〉0．05）；6．0Gy组生存率磁疗组和对照组之间比较有统计学差异（P〈0．05），其磁疗组30d平均存活率为71．43％，平均存活期为（24．93±8．43）d，对照组30d平均存活率21．4l％．平均存活期为（17．07±7．70）d。结论：旋转恒定磁场不能提高10．0Gy及8．0Gy剂量所致极重度急性骨髓型放射病小鼠的生存率，但对6．0Gy所致重度急性骨髓型放射病有明显的保护作用，从而为旋转恒定磁场应用于临床治疗重度急性骨髓型放射损伤提供了实验依据。     

The Mizon–Richard Encompassing Test for the Cox and Aalen Additive Hazards Models

Summary .   The Cox hazards model ( Cox, 1972 , Journal of the Royal Statistical Society, Series B 34, 187–220) for survival data is routinely used in many applied fields, sometimes, however, with too little emphasis on the fit of the model. A useful alternative to the Cox model is the Aalen additive hazards model ( Aalen, 1980 , in Lecture Notes in Statistics-2 , 1–25) that can easily accommodate time changing covariate effects. It is of interest to decide which of the two models that are most appropriate to apply in a given application. This is a nontrivial problem as these two classes of models are nonnested except only for special cases. In this article we explore the Mizon–Richard encompassing test for this particular problem. It turns out that it corresponds to fitting of the Aalen model to the martingale residuals obtained from the Cox regression analysis. We also consider a variant of this method, which relates to the proportional excess model ( Martinussen and Scheike, 2002 , Biometrika 89, 283–298). Large sample properties of the suggested methods under the two rival models are derived. The finite-sample properties of the proposed procedures are assessed through a simulation study. The methods are further applied to the well-known primary biliary cirrhosis data set.     

Interactions of target population size,population parameters,and program management on viability of captive populations

When established conservation programs expand and evolve, management practices may become inconsistent with program goals. In the past decade, the American Zoo and Aquarium Association expanded species conservation programs by increasing the number of Species Survival Plans (SSP) and establishing more than 300 new Population Management Plan (PMP) programs. However, limited space in captive breeding facilities forces a competition among SSPs and less intensively managed PMPs. Regional Collection Plans establish priorities and allocate space accordingly by setting target population size for each species; species of high conservation priority (SSPs) are allocated space at the expense of lower priority species (PMPs). Because population size and genetic composition interact to impact population viability, target population size is a significant factor to a population’s prospects for long‐term survival. We examined four population parameters (current population size, target population size, current gene diversity, and mean generation time) for 46 mammalian SSPs and 17 PMPs. Relative to SSPs, PMPs combine smaller current and target population sizes, lower levels of current gene diversity, and shorter mean generation times than SSPs. Thus, the average PMP population can expect to lose gene diversity more rapidly than the average SSP population. PMPs are projected to lose 10% or more of their founding gene diversity, within only 2 years. In contrast, the average SSP population is projected to lose 10% in 40 years. Populations with small current or target population sizes require intensive management to avoid extinction. More intensive genetic management of populations typically designated as PMPs, through recruitment of potential founders and equalization of founder representation, could increase gene diversity and improve viability. Less rigorous population management should be reserved for populations whose long‐term survival is either secure or that can be readily replenished from the wild. Because PMP populations need intense genetic management similar to that currently in effect for SSPs, there should be neither a management‐level distinction between programs nor an arbitrary difference in space allocated to programs. Zoo Biol 20:169–183, 2001. © 2001 Wiley‐Liss, Inc.     

The Accelerated Failure Time Model Under Biased Sampling

Summary Chen (2009, Biometrics) studies the semi‐parametric accelerated failure time model for data that are size biased. Chen considers only the uncensored case and uses hazard‐based estimation methods originally developed for censored observations. However, for uncensored data, a simple linear regression on the log scale is more natural and provides better estimators.     

The genetic structure of a tribal population,the Yanomama Indians. XII. Biodemographic studies

The Yanomama Indians of Southern Vanezuela and Northern Brazil are one of the largest, relatively unacculturated tribes of the tropical rain forest. Over a period of eight years data have been collected from a considerable portion of their territory on estimated age, sex ratio, fertility rates (as determined by physical examination and urine tests), and infant death rates. Although it has been impossible to collect direct data on infanticide, this subject can be approached indirectly through distortions of the sex ratio and anecdotal information. Some historical data are also available as a basis for estimating tribal expansion in the past 100 years. With this material it has been possible to construct Life Tables for the Yanomama, and to explore the results of various perturbations of the input parameters. Data are also presented on patterns of mating and reproduction: number of spouses, mean and variance in number of surviving children, frequency of “extra-marital conceptions” based on the results of extensive blood group typings, and consanguinity rates as determined by observation and computer simulation. Although we do not present the Yanomama as typical, these data are seen as providing a basis for more realistic population models than have existed in the past. In addition, the data provide a basis for relatively precise estimates of such demographic measures as Fisher's Reproductive Value, Crow's Index of Total Selection, and Weiss' Index of Growth Regulation.