Use Of Demand Models Research Articles

Estimating the Population Benefit of Radiotherapy: Using Demand Models to Estimate Achievable Cancer Outcomes

The measurement of population benefits is important for priority setting, economic evaluation and quality improvement. It also informs advocacy. In this article, the use of demand models to estimate the achievable benefit of cancer therapy is reviewed. Achievable benefit refers to the treatment benefit achievable under optimal conditions. The population benefit of radiotherapy has been used as an example. Demand models provide a means of estimating the optimal proportion of patients with treatment indications when guidelines are followed. They may be used to estimate achievable benefit. The choice of end point should reflect the range of benefits associated with the treatment of interest. In some cases, further model development is needed if a pre-existing demand model is used. The benefit of treatment for each indication is estimated using a systematic review process. The highest level of evidence is used to define the benefit for each indication. In cases where multiple sources of the same level and quality of evidence exist, a meta-analysis is carried out. Population-based effectiveness data sources are considered, but three major challenges to their use are: (i) generalisability of the observed outcomes, (ii) data resolution and (iii) confounding and bias. The population benefit determined from this process describes the population proportion achieving a benefit due to the use of guideline-based treatment, compared with no use of that treatment. Sensitivity analysis provides a means for modelling the effect of model uncertainties. The predominant uncertainty is most often due to uncertainty in indication proportion. Preference-sensitive treatment decisions are a common example. The described approach to estimating the achievable benefit of cancer therapy is robust to model uncertainties, rapidly adaptable and is transparent. However, estimates rely on the quality of model data sources and may be affected by model assumptions. Models should be developed for a broader range of modalities of cancer therapy and relevant end points.

A critical examination of models and projections of demand in water utility resource planning in England and Wales

Demand modelling plays a vital role in water resource management yet has rarely been critically reviewed. This paper adopts a critical realist framework for a historical analysis of demand modelling practices and their role in long-term water resource management in England and Wales from 1945 to 2010. It then focuses on recent domestic demand models in the English and Welsh private water sector. A critique of scientific realist assumptions regarding demand models is presented and the role of the current regulatory environment in encouraging a highly strategic use of demand models is discussed. Policy recommendations toward more effective modelling practices are made.

Incorporating demand uncertainty in water supply headworks simulation

Simulation is used to evaluate the future performance of water supply headworks systems. Typically, most of the uncertainties associated with the use of demand models are ignored in the simulation. A simulation methodology is presented which makes allowance for the uncertainties associated with the use of demand and streamflow models. The demand uncertainties considered are natural, climatic, socioeconomic, and model parameter uncertainty. The methodology is illustrated using two case studies which consider a simple stationary demand model and the widely used trend extrapolation demand model. The results show that ignoring demand uncertainties (as well as streamflow model parameter uncertainty) can significantly bias the expected reliability. This bias depends on the magnitude of demand uncertainty which in turn depends on model structure, length of data available for calibration, and uncertainty in future socioeconomic variables. Moreover, this bias tends to increase as streamflow variability decreases. To guard against overoptimistic assessment of system performance, it is suggested that allowance be made for demand uncertainty (as well as streamflow model parameter uncertainty) in headworks simulation.

Travel demand and response analysis—Some integrating themes

In spite of the intensive study of urban transportation and the use of demand models over two decades, we would argue that relatively little is known of the response of travellers—and more generally an urban system—to different policy measures. The failure to produce a testable body of knowledge associated with demand and response models has led, among other reasons, to a disintegration of faith in model based forecasts and a fragmentation of the field into many research areas which relate to the understanding of traveller behaviour and its adaptation in a changing environment. This is proving as stimulating to resarchers as it is frustrating to practitioners who seek to identify “best practise”. In this paper we examine certain integrating themes in the development, specification and use of models with particular reference to the critical environment within which both research and practise are conducted. The themes include: an identification of alternative models and methods of analysis for generating comparable information; the general relationship between descriptions, explanations and forecasts of travel behaviour; and the issue of model design. Within these general contexts we shall make special reference to aspects of British practice.