Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study

Background Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival. Methods Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20–84 years diagnosed during 1997–2007 from Queensland, Australia. Results Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients. Conclusions With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings

Using performance measurement models for benefit realization with enterprise systems: The Queensland Government approach (Case Study)

A range of influences, technical and organizational, has encouraged the wide spread adaption of Enterprise Systems (ES). Nevertheless, there is a growing consensus that Enterprise Systems have in the many cases failed to provide the expected benefits to organizations. This paper presents ongoing research, which analyzes the benefits realization approach of the Queensland Government. This approach applies a modified Balance Scorecard. First, history and background of Queensland Government’s Enterprise Systems initiative is introduced. Second, the most common reasons for ES under performance are related. Third, relevant performance measurement models and the Balanced Scorecard in particular are discussed. Finally, the Queensland Government initiative is evaluated in light of this overview of current work in the area. In the current and future work, the authors aim to use their active involvement in Queensland Government’s benefits realization initiative for an Action Research based project investigating the appropriateness of the Balanced Scorecard for the purposes of Enterprise Systems benefits realization.