Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care

Learning in disrupted projects: on the nature of corporate and personal learning

The majority of large, complex projects suffer disruptions. These can have unexpected and significant impacts on a project, resulting in excessive time and cost overruns. Disrupted projects therefore require careful management in order to minimize the impact of a disruption. One element of a project for which disruptions will have a particular impact is on any anticipated learning gains. Loss of learning from disruptions can often be very significant. This lost learning may be from individual workers (personal) or by the organization (corporate). An understanding of the impact of a particular disruption on learning is required to enable effective management of that disruption. This paper argues that improved management of learning in disrupted projects may come from the disaggregation of personal and corporate learning from the typically used aggregated learning curve presumptions. The literature is reviewed to explore whether a method of disaggregation can be determined from existing propositions about the behaviour of learning curves. The explorations demonstrate that the disaggregation of a learning curve is complex. The contribution of this paper derives from developing an understanding of the role of asymptotes in constructing learning curves, and the nature of the interaction between personal and corporate learning. These two aspects are argued to be crucial aspects in managing the impact of disruptions on learning. The main motivation for this work is to provide managers with an understanding of the disaggregation process, in order that they can think through the impact of a disruption on learning in their own projects