Interphase Clinical Trials: Phase III/Phase IV

In this paper we investigate ways in which the results of a controlled Phase III clinical trial can be used in subsequent Phase IV, and possibly further Phase III studies. Specifically we are interested in; 1) developing particular hypothesis relating to a modified study population, 2) studying how changes in the particularities of the Phase III study group can influence certain outcome variables of interest and 3) using the results of the Phase III study applied to specific target groups, having particular characteristics, to updating observations from the Phase III study with information obtained at a later stage. These issues are all concerned with the way in which we can exploit information from a Phase III trial, information that is of high quality but not necessarily directly related to the way in which many post Phase III studies, focussing on different patient population groups, are carried out. Since it is often these post Phase III studies that have the strongest influence on clinical practice we aim to develop a framework around which the post Phase III studies might be structured.     

Multiple Linear Regression on Canonical Correlation Variables

When the explanatory variables of a linear model are split into two groups, two notions of collinearity are defined: a collinearity between the variables of each group, of which the mean is called residual collinearity, and a collinearity between the two groups called explained collinearity. Canonical correlation analysis provides information about the collinearity: large canonical correlation coefficients correspond to some small eigenvalues and eigenvectors of the correlation matrix and characterise the explained collinearity. Other small eigenvalues of this matrix correspond to the residual collinearity. A selection of predictors can be performed from the canonical correlation variables, according to their partial correlation coefficient with the explained variable. In the proposed application, the results obtained by the selection of canonical variables are better than those given by classical regression and by principal component regression.     

Characterizing complex mixed-species bird flocks using an objective method for determining species participation

We developed sampling methods to characterize the participation of bird species in foraging flocks led by the Eastern Tufted Titmouse (Baeolophus bicolor) in North-central Florida during winter, because standard field methods, developed primarily for permanent resident Neotropical flocks, were intractable in our system. During January–February 2004 and November 2004–March 2005, we observed 55 mixed-species flocks, recorded 40 potential flocking species [mean of 12.4 species (SD = 3.8; range 3–20), 26.3 individuals (SD = 12.2; range 8–60), and 3.1 titmice (SD = 1.4; range 1–7), per flock]. Twenty-six species were observed frequently enough (>10% of observations) to be included in analyses. We paired 60-min flock observations with 10-min point counts conducted in locations used by flocks, but after flocks had moved more than 100 m away. This method yielded a measure of flocking propensity: the ratio of the number of individuals observed in the flock versus during the point count for each species. We used regression tree (RT) analysis to classify species into groupings according to their levels of flock participation, and to investigate relationships between flocking propensity and various environmental and social factors that we measured. Our analysis identified three clear species groups; “Nuclear/Regular Associate” (12 spp.; high/moderate), “Occasional Associate” (four spp.; moderate/low), and “Non-joiner/Accidental” (ten spp.; low/no flocking propensity). Groupings were similar to schemes produced via more time-intensive field methods. In order to contextualize grouping categories, we conducted a review of flocking group definitions and relevant autecological information (e.g., interspecific sociality) about our study species. We found this method to be useful for geographically extensive sampling of species’ participation in mixed-species flocks, despite high inter-flock variability in species composition and limited labor.     

Assessing Nonsuperiority,Noninferiority, or Equivalence When Comparing Two Regression Models Over a Restricted Covariate Region

Summary In many scientific problems the purpose of the comparison of two regression models, which describe the relationship between a same response variable and several same covariates for two different groups, is to demonstrate that one model is no higher than the other by a negligible amount, or to demonstrate that the models have only negligible differences and so they can be regarded as describing practically the same relationship between the response variable and the covariates. In this article, methods based on one‐sided pointwise confidence bands are proposed for assessing the nonsuperiority of one model to the other and for assessing the equivalence of two regression models. Examples from QT/QTc study and from drug stability study are used to illustrate the methods.     

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

Fire is a common disturbance in the North American boreal forest that influences ecosystem structure and function. The temporal and spatial dynamics of fire are likely to be altered as climate continues to change. In this study, we ask the question: how will area burned in boreal North America by wildfire respond to future changes in climate? To evaluate this question, we developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5° (latitude × longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was substantially more predictable in the western portion of boreal North America than in eastern Canada. Burned area was also not very predictable in areas of substantial topographic relief and in areas along the transition between boreal forest and tundra. At the scale of Alaska and western Canada, the empirical fire models explain on the order of 82% of the variation in annual area burned for the period 1960–2002. July temperature was the most frequently occurring predictor across all models, but the fuel moisture codes for the months June through August (as a group) entered the models as the most important predictors of annual area burned. To predict changes in the temporal and spatial dynamics of fire under future climate, the empirical fire models used output from the Canadian Climate Center CGCM2 global climate model to predict annual area burned through the year 2100 across Alaska and western Canada. Relative to 1991–2000, the results suggest that average area burned per decade will double by 2041–2050 and will increase on the order of 3.5–5.5 times by the last decade of the 21st century. To improve the ability to better predict wildfire across Alaska and Canada, future research should focus on incorporating additional effects of long‐term and successional vegetation changes on area burned to account more fully for interactions among fire, climate, and vegetation dynamics.     

综合性医院平均住院日绩效指标集的构建与应用

目的 构建以平均住院日为核心的绩效指标集,在此基础上,制定绩效目标及关键控制举措,并跟踪实际的执行效果。方法 收集2008年12月—2013年6月共计55个月的重点科室平均住院日数据,运用相关性分析和多元线性回归提取影响平均住院日的显著因素,根据模型结论,研究分析绩效控制目标和举措并进行应用。结果 现阶段有效缩短平均住院日应主要解决“收容收治比及床位资源的利用效率”两对关键矛盾,模型应用后,医院院整体平均住院日由原来的15.7天降低为11.6天,重点科室平均住院日由19.2天降低为14.6天。结论 平均住院日绩效指标集的应用有助于优化医院资源配置,达到显著提高整体运营效率的目的。     

原发性肾病综合征患儿尿蛋白转阴时间的影响因素分析

目的:探讨儿童原发性肾病综合征(primarynephrotic syndrome,PNS)经激素治疗后尿蛋白转阴时间的影响因素。方法:回顾性分析68例初发PNS患儿的临床资料,通过激素治疗后,按照尿蛋白转阴时间进行分组,对患儿特征、化验指标进行单因素分析和多因素非条件Logistic回归分析。结果:单因素分析显示水肿至口服激素时间、水肿程度、父母认知程度、有无合并感染、入院时血浆IgM水平、白蛋白水平、胶体渗透压等指标在不同预后组间有显著性差异(P0.05)。多因素分析显示水肿至口服激素时间长、父母认知程度差、血浆胶体渗透压低可视为尿蛋白转阴时间大于1周的危险因素。结论:水肿至口服激素时间长、父母认知程度差、血浆胶体渗透压低为PNS患儿尿蛋白转阴时间长于1周的危险因素,与患儿的不良预后相关,临床应予以高度重视。     

糖尿病合并肺结核患者诱导耐药性危险因素的回归分析

目的:研究2型糖尿病(Type 2 Diabetes,T2DM)合并肺结核(Tuberculosis,TB)患者诱导耐药性危险因素的回归分析。方法:从2012年3月到2013年3月,于我院共计有124例患者被确诊为肺结核,将其作为研究对象。根据患者是否合并有2型糖尿病,将其分成观察组(49例)及对照组(75例)。对全部患者进行耐药性实验,分别经单因素分析及Logistic回归性分析寻找诱导耐药性的危险因素。结果:观察组在治疗过程中断、有吸烟习惯、依从性差、病程≥1年、HbAlc值≥6.5%等方面所占比例显著高于对照组,差异均有统计学意义(均P0.05)。由多因素分析可知,治疗过程中断、有吸烟习惯、依从性差、病程≥1年、HbAlc值≥6.5%等均为糖尿病合并肺结核患者的危险因素。结论:T2DM合并TB患者诱导耐药性的危险因素较多,临床应重点关注,并采取相应措施,从而为临床治疗提供更为有利的条件。     

掌骨X线测量推断身高的研究

随机选取在校健康大学生186人(男90人, 女96人, 年龄18—27岁), 拍摄双手后前位372侧X线片, 测量身高和掌骨的长与宽。通过掌骨和身高的测量, 分析掌骨各参数与身高的关系, 为人类学和法医学的身高推断积累研究资料。掌骨长与身高的相关性明显高于掌骨宽, 其中男性第2掌骨长与身高相关性最高, 其线性回归方程为Ym=1097.320+9.337X; 女性第3掌骨长与身高的相关性最高, 其线性回归方程为Yf=1016.752+9.878X。男性第2掌骨长和女性第3掌骨长与身高的相关性最高, 可分别作为推断身高的依据。