A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information.

The generalized estimating equation (GEE) approach to the analysis of longitudinal data has many attractive robustness properties and can provide a 'population average' characterization of interest, for example, to clinicians who have to treat patients on the basis of their observed characteristics. However, these methods have limitations which restrict their usefulness in both the social and the medical sciences. This conclusion is based on the premise that the main motivations for longitudinal analysis are insight into microlevel dynamics and improved control for omitted or unmeasured variables. We claim that to address these issues a properly formulated random-effects model is required. In addition to a theoretical assessment of some of the issues, we illustrate this by reanalysing data on polyp counts. In this example, the covariates include a base-line outcome, and the effectiveness of the treatment seems to vary by base-line. We compare the random-effects approach with the GEE approach and conclude that the GEE approach is inappropriate for assessing the treatment effects for these data

A comparison of GEE and random effects models for distinguishing heterogeneity, nonstationarity and state dependence in a collection of short binary event series

GEE transition models and Markov random effect models are applied to a simple panel data set on depression. In each case, the precise specifications adopted were derived from the authors’ interpretation of best practice in the literature. The two approaches result in quite different inference on the three process characteristics of interest: state dependence, heterogeneity and nonstationarity. The design of the analyses permits indirect goodness of fit measures to be derived for the GEE models and these indicate serious deficiencies in this approach. It is shown through simulation and further analyses of the depression data that these deficiencies may be corrected by including the initial observation properly in the analyses and by adopting an appropriate variance-covariance structure. The former problem is widely understood in random effects modelling and is relatively straightforward to address within GEE. The latter problem is more difficult because, without model selection or goodness of fit measures generally available for GEE models, it is not clear how one may select empirically between alternative variance-covariance structures. Inappropriate variance-covariance specifications prejudice consistent estimation of state dependence and nonstationarity. </jats:p

A family of hypothesis tests for a collection of short event series with an application to female employment participation

Data comprising a collection of short event series are increasingly encountered in social science research. Such series may be expected to be heterogeneous and nonstationoary precluding conventional inferential methods. Tests are presented for homogeneity, nonstationarity, and zero order, with appropriate controls. The test procedures are based upon the subdivision of each series into a 'conditioning sequence' and an 'experimental observation'. The tests are applied to data on labour force participation by married women.

Individual and geographical variation in longitudinal voting data for England 1964 - 1970

A two-state Markov model of individual voting transitions is developed which allows voters to differ with respect to their voting propensities and loyalties. Such a model allows the estimation of various process parameters without the specification errors common in other models. Additional parameters are introduced to allow for the effects of a third party, considerably extending the scope of application. The model is applied to the electoral voting histories for 1964, 1966, and 1970 of 671 individuals from sixty-seven English constituencies.

Using the Sakai Collaborative Toolkit in e-Research Applications

The Sakai Project ( http://www.sakaiproject.org ) is developing a collaborative environment that provides capabilities that span teaching and learning as well as e-Research applications. By exploiting the significant requirements overlap in the collaboration space between these areas, the Sakai community can harness significant resources to develop an increasingly rich set of collaborative tools. While collaboration is a significant element of many e-Research projects, there are many other important elements including portals, data repositories, compute resources, special software, data sources, desktop applications, and content management/e-Publication. The successful e-Research projects will find ways to harness all of these elements to advance their science in the most effective manner. It is critical to realize that there is not a single software product that can meet the requirements for such a rich e-Research effort. Realizing that multiple elements must be integrated together for best effect leads us to focus on understanding the nature of integration and working together to improve the cross-application integration. This leads us not to drive towards a single toolkit (such as Sakai or Globus), but instead to a meta-toolkit containing well-integrated applications. When considering a technology for use, perhaps the most important aspect of that technology is how well it integrates with other technologies. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56162/1/1115_ftp.pd