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.     

Facilitative Aspects of Field Research with Deviant Street Populations*

The authors examine facilitative aspects of field research with deviant street populations. Based on the authors' research with the homeless and with male street prostitutes, the facilitative aspects include access to settings, social relationships, acquisition of information, and the maintenance of researcher interest. The authors argue that in each of these aspects street deviants may in some ways be easier to study than other populations. The paper concludes with suggestions for maximizing the efficiency and quality of fieldwork with specific deviant populations.     

A study of simulated annealing and a revised cascade algorithm for image reconstruction

We describe an image reconstruction problem and the computational difficulties arising in determining the maximum a posteriori (MAP) estimate. Two algorithms for tackling the problem, iterated conditional modes (ICM) and simulated annealing, are usually applied pixel by pixel. The performance of this strategy can be poor, particularly for heavily degraded images, and as a potential improvement Jubb and Jennison (1991) suggest the cascade algorithm in which ICM is initially applied to coarser images formed by blocking squares of pixels. In this paper we attempt to resolve certain criticisms of cascade and present a version of the algorithm extended in definition and implementation. As an illustration we apply our new method to a synthetic aperture radar (SAR) image. We also carry out a study of simulated annealing, with and without cascade, applied to a more tractable minimization problem from which we gain insight into the properties of cascade algorithms.     

Creating offshore-ready it professionals: A global perspective and strong collaborative skills are needed

Outsourcing of IT functions has become a widespread corporate practice, which has naturally led to concerns among IT works about how this affects their jobs. The issue is complex, and many companies are bringing their IT functions back inhouse. In light of this complexity, what skills do IT workers need to be com-petitive? We address this question first by reviewing the literature and then by examining two corporate case studies that have dealt with outsourcing issues. Based on this view of outsourcing, we discuss the skills that can provide a com-petitive advantage in the current environment.     

Tasmania Together? A disability critique of a social plan

The 'Tasmania Together' social futures plan for Tasmania is critically examined. Authored by a Tasmanian Government appointed Community Leaders Group, on behalf of the people of Tasmania, the plan is supposed to deliver a better Tasmanian society by 2020, based upon community consultation. Rather than a step-forward in democracy, the process serves to remove the democratic rights of people, especially those who are disadvantaged. A critique from a disability perspective is offered, which suggests that this social plan constitutes a form of institutionalised disablism.     

Sociological computing: An opportunity missed?

Rapid advances in sociological computing are changing virtually every aspect of scholarly sociological work. These changes offer an opportunity for sociologists to improve the quality of their work and bring new insights and approaches to bear on important sociological problems. Nevertheless, sociologists display a profound ambivalence toward computer work by other sociologists. Left unchecked, this ambivalence threatens to turn sociological computing into an opportunity missed. Inadequate rewards, lack of training, and a general failure to understand the importance of computing in sociology threaten to stifle the development of quality soft-ware, push sociologists competent in computing into other fields, and jeopardize the quality of sociological scholarship. This article documents these ambivalent tendencies and proposes reasonable steps the discipline should take to assure that sociological computing does not become an opportunity missed.