Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.     

Sample sizes for estimation in clinical research

A sample size justification should be given for all clinical investigations. However, sometimes the objective of a trial is to estimate an effect with a view to planning a later definitive study. This paper describes the calculations for designing studies where one wishes to adopt an estimation approach through using confidence intervals around the overall response. Calculations are given for data anticipated to take a Normal form. Copyright © 2004 John Wiley & Sons, Ltd.     

Bayesian analysis of mixture modelling using the multivariate t distribution

A finite mixture model using the multivariate t distribution has been shown as a robust extension of normal mixtures. In this paper, we present a Bayesian approach for inference about parameters of t-mixture models. The specifications of prior distributions are weakly informative to avoid causing nonintegrable posterior distributions. We present two efficient EM-type algorithms for computing the joint posterior mode with the observed data and an incomplete future vector as the sample. Markov chain Monte Carlo sampling schemes are also developed to obtain the target posterior distribution of parameters. The advantages of Bayesian approach over the maximum likelihood method are demonstrated via a set of real data.     

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.     

蛋白组学方法应用于人肺癌组织蛋白表达的研究

采用蛋白组学方法,利用4例中国肺癌患者的切除肺癌组织测定了磷癌(SQCLC)的蛋白表达 从双向电泳(2-DEgel)切下53个点,49个蛋白与数据库搜索结果吻合。在鉴定的24个蛋白中,17个蛋白有报道是与肺癌相关的蛋白其7个蛋白与人类其它癌症相关。所建立的蛋白组学方法能够用于建立一种基于组织的化验方法,发现肺癌患者组织中表达的蛋白标记物以用于癌症的分子学诊断。     

山西省发展小杂粮的营销对策

小杂粮是山西省的特色农业产品,早以质量优、营养丰富而闻名全国。但就山西省而言,由于市场化、组织化程度较低,产品营销一直是钳制生产发展的瓶颈因素。因此,在发展小杂粮生产的问题上,我们首先要考虑的是;如何使小杂粮产品适应市场需求,顺利实现其价值。也就是先要有一个明确的营销思路,以避免生产决策的失误。只有选择适宜的目标市场、准确地进行市场定位、建立通畅的分销渠道等,才能促进小杂粮生产健康发展。     

Stjepan Metrović and Michel Maffesoli's 'implosive' defence of the Durkheimian tradition: theoretical convergences around Baudrillard's thesis on the 'end' of the social

This paper critically examines Metrovi? and Maffesoli's attempt to understand postmodernity through Émile Durkheim's nonrational link between society, religion and morality. Metrovi? (1991, 1997) and Maffesoli's (1996) work draws upon this emotional element when attempting to refute Baudrillard's (1983) cognitively focused, if implicit, critique of the Durkheimian tradition. Despite their best intentions, Metrovi? and Maffesoli still fail to exploit the partialities of Baudrillard's critique to the full. While both have some appreciation of the link between emotion and religion as found in Durkheim's The Elementary Forms of Religious Life (1912) they, nevertheless, fall short of grasping its full conceptual importance. This leads them to an implicit acceptance of Baudrillard's thesis on the ‘end’ of the social. This pitfall could have been avoided if Metrovi? and Maffesoli had built their respective analyses of the postmodern condition on a closer reading of The Elementary Forms. Reading this text alongside those other insights on emotion and social life as contained within Durkheim and Mauss's Primitive Classification (1903) and Talcott Parsons's subsequent writings on the sociological problem of religion, would have helped to distance the work of Metrovi? and Maffesoli from that of Baudrillard, and allowed them to offer a stronger and more comprehensive defence of the said tradition.     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.