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.     

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.     

Generations,Identities, and the Collective Memory of Che Guevara1

Has the image of Che Guevara lost its power to evoke radical politics in the face of pervasive commodification? The commercialization of this 1960s political icon has called into question the power of the market to shape collective memories. Meanwhile, antisystemic movements of the left continue to erect his image at protest events. In light of this contest over how Che Guevara is remembered, we investigate, using data from a survey of Spanish citizens, who is most likely to recall him. We find qualified support for the theory of generational imprinting—Che is more often recalled by those generations who saw him rise to prominence during their formative years, although prominent as a collective symbol rather than as a living person. Our results also corroborate the claim that historical figures or events are more salient for, and therefore more likely to be remembered by, some subgenerational units than others. Thus, although the younger generations are in general more likely than their elders to recall Che, he is most frequently remembered by the highly educated leftists who espouse postmaterialist and posttraditionalist values and identify more with their local regions than with the nation of Spain. These patterns suggest that, in contrast to the dire predictions of mass culture theorists, the memory of Che Guevara has become increasingly tied to markers of social, ethnic‐regional, and political identity.     

Adaptive tests of regression functions via multiscale generalized likelihood ratios

Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. She derives the asymptotic joint distribution of the component tests and that of the proposed test under the null hypothesis. She also develops a simple method of selecting the smoothing parameters for the proposed test and presents two approximate methods for obtaining its P‐value. Finally, she evaluates the proposed test through simulations and illustrates its application to a set of real data.     

Gender differences in social workers’ assessments and help-giving strategies towards single parents

The study investigates Swedish male and female social workers’ assessments and help-giving strategies towards single parents applying for income support. The study was carried out with the help of a vignette distributed as a mail questionnaire. Results show that social workers tend to conform to assessments and help-giving strategies that are consistent with expectations linked to their own gender role.     

Privatization of refuse removal and labor costs

We examine the labor-cost savings associated with privatization by comparing earnings and employment trends of public and private sector refuse workers. Findings suggest that high union earnings for workers in the public sector are a source of labor-cost savings in the refuse industry. Evidence on job changers does not indicate that earnings for this group of workers are a compensating differential. Metropolitan area employment findings suggest that municipalities are less likely to use union refuse workers in the public sector when a relatively small percentage of area residents belong to a union. The authors thank Jacqueline Agesa, Keith Bender, Maria Crawford, and Richard Perlman for valuable suggestions. Research assistance from Eric Blackburn is greatly appreciated.     

Empirical Non-Parametric Control Charts: Estimation Effects and Corrections

Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider non- parametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.