School of Engineering

A major analytic challenge in epidemiological studies is the threat to validity and precision of conclusions raised by missing data. It is still commonly accepted practice to analyze data containing missing values by "complete-case" methods, where entire individuals are omitted from the analysis if they have a missing value on any of the variables required for the analysis in question. This approach can lead to biases in conclusions, by excluding individuals in whom patterns of association may be different than among those retained, and at best leads to loss of precision due to the reduction in sample size available for analysis. The method of multiple imputation is gaining popularity as an approach for dealing with missing data. It involves the production of multiple complete datasets based on a statistical model for the missing values given the observed data. Each of the imputed datasets is then analyzed using standard methods, and valid inferences are obtained by combin...

Abstract—The Australian Urban Research Infrastructure Network (AURIN) project (www. aurin. org. au) is tasked with developing an e-Infrastructure to support urban and built environment research across Australia. As identified in [1], this e-Infrastructure must ...