Comparison of Four New General Classes of Search Designs

A factor screening experiment identifies a few important factors from a large list of factors that potentially influence the response. If a list consists of m factors each at three levels, a design is a subset of all possible 3 ^m runs. This paper considers the problem of finding designs with small numbers of runs, using the search linear model introduced in Srivastava (1975). The paper presents four new general classes of these 'search designs', each with 2 m −1 runs, which permit, at most, two important factors out of m factors to be searched for and identified. The paper compares the designs for 4 ≤ m ≤ 10, using arithmetic and geometric means of the determinants, traces and maximum characteristic roots of particular matrices. Two of the designs are found to be superior in all six criteria studied. The four designs are identical for m = 3 and this design is an optimal design in the class of all search designs under the six criteria. The four designs are also identical for m = 4 under some row and column permutations.     

阿兰·奈斯论深层生态学的哲学基础

在阿兰·奈斯看来 ,深层生态学既是一种社会思潮 ,又是以这种思潮为指导的一场旨在改变现行实践模式的社会运动。作为一种社会思潮 ,它是一个由一系列基本原理构成的演绎性的逻辑体系。奈斯认为 ,深层生态学的理论内核是八大原理 ,而在其背后 ,还有更为基本的前提与终极规范 ,即各种生态智慧。而奈斯本人的生态智慧的终极规范是 (大 )自我实现     

T.S艾略特之"阿尔佛雷特的爱之歌"与新批评主义

Ｔ .Ｓ .Ｅｌｉｏｔ -ａＣｅｌｅｂｒａｔｅｄＡｍｅｒｉｅａｎＰｏ ｅｔ,ＤｒａｍａｔｉｓｔａｎｄｃｒｉｔｉｃＴ .Ｓ .Ｅｌｉｏｔ (1 888— 1 965 ) ,ａｃｅｌｅｂｒａｔｅｄＡｍｅｒｉ ｃａｎｐｏｅｔ,ｄｒａｍａｔｉｓｔａｎｄｃｒｉｔｉｃ,ｒｅｃｅｉｖｅｄａｎｅｘｃｅｌｌｅｎｔｅｄｕｃａｔｉｏｎｏｆｃｌａｓｓｉｃａｌｌｉｔｅｒａｔｕｒｅｗｈｅｎｈｅｗａｓｙｏｕｎｇ .ＨｅｆｉｎｉｓｈｅｄｈｉｓｓｔｕｄｉｅｓｉｎＨａｒｖａｒｄｕｎｄｅｒｔｈｅｇｕｉｄａｎｃｅｏｆｔｈｅｗｅｌｌ-ｋｎｏｗｎｓｃｈｏｌａｒｓＩｒｖｉｎ…     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

Efficient designs based on orthogonal arrays of type I and type II for experiments using units ordered over time or space

For a wide variety of applications, experiments are based on units ordered over time or space. Models for these experiments generally may include one or more of: correlations, systematic trends, carryover effects and interference effects. Since the standard optimal block designs may not be efficient in these situations, orthogonal arrays of type I and type II, which were introduced in 1961 by C.R. Rao [Combinatorial arrangements analogous to orthogonal arrays, Sankhya A 23 (1961) 283–286], have been recently used to construct optimal and efficient designs for many of these experiments. Results in this area are unified and the salient features are outlined.     

The potential of integrated modelling in conservation biology: A case study of the black‐footed albatross (Phoebastria nigripes)

Conservation biology aims at assessing the status of a population, based on information which is often incomplete. Integrated population modelling based on state‐space models appears to be a powerful and relevant way of combining into a single likelihood several types of information such as capture‐recapture data and population surveys. In this paper, the authors describe the principles of integrated population modelling and they evaluate its performance for conservation biology based on a case study, that of the black‐footed albatross, a northern Pacific albatross species suspected to be impacted by longline fishing     

湖北省高校科技成果转化的现状调查与分析

通过对高校科技工作管理者、高校教师以及部分企业的调研发现,目前影响湖北省高校科技成果转化的因素主要是政府管理、资金投入、高校观念转变与机制创新、企业重视等方面,并提出相应的对策与建议.     

Parent‐Adolescent Communication About Sex: Retrospective Reports by Latino College Students

Latina female (n= 97) and Latino male (n= 69) college students (M age = 21.4 years) completed self‐report surveys regarding family of origin experiences, including sexual communication with parents while growing up. Latino parents of this comparatively highly educated sample tended to use direct rather than indirect strategies for communicating about sexuality with their children. Young women reported higher levels of sexual communication with mothers while growing up than did young men, and respondents reported less communication with fathers than mothers. Among young women, sexual communication with mother was positively associated with non‐Mexican origin and negatively associated with having older brothers living at home. In contrast, maternal education was positively associated with mother‐son communication about sex. Paternal education and the absence of older brothers positively predicted communication with both sons and daughters. The analyses provide novel information regarding sexual communication in Latino families and suggest directions for future research.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.