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Multiple bias modelling for observational studies

Observational studies are subject to many potential sources of bias, such as unmeasured confounding, missing/mis-measured data, and various selection biases. We are developing a methodological framework using graphical models to represent different types of bias that may be present in different types of observational studies. This graphical framework will provide social scientists with a conceptual tool to help identify possible biases that may affect their study, and also with an analytic tool to carry out sensitivity analysis. Typically, the bias parameters in the model are not identified by the study data, and so information must be supplied from other sources, including external datasets or expert opinion. We have developed a method to adjust for selection bias in case control studies and we are also developing multiple bias models to study the effects of water chlorination by-products on low birth weight.

Duration: September 2005 to June 2008

Researchers: Nicky Best, Sylvia Richardson, Sara Geneletti, Lawrence McCandless, Nuoo-Ting Molitor



Combining individual and aggregate level data

Many sources of data such as the Census, ONS neighbourhood statistics, and the national births, deaths and other health data sets, provide information on the average health and social circumstances of the whole population, and of different sub-groups of the population. On the other hand, there are various UK survey and cohort data sets that provide detailed individual-level information about health, lifestyle, socioeconomic and other personal characteristics, but only on a small subset of individuals. We have developed hierarchical related regression methods for combining random samples of individual level data with aggregate data on the same variables. These can be used to reduce problems of ecological bias and have been successfully applied to study socio-demographic variations in the risk of self-reported limiting long-term illness and hospitalisation for cardiovascular disease. Ongoing applied work includes studies of the effects of air pollution on childhood leukaemia and low birth weight.  Software developed includes the ecoreg R package.

Duration: April 2005 to June 2008

Researchers: Nicky Best, Sylvia Richardson,  Nuoo-Ting Molitor, Chris Jackson (MRC)



Small area estimation

This work is being carried out in collaboration with ONS. The basic methodological problem is to estimate the value of a given indicator (e,g. income, crime rate, unemployment) for every small area, using data on the indicator from individual-level surveys in a partial sample of areas, plus relevant area-level covariates available for all areas from e.g. census and administrative sources. We are extending the existing estimation methods used by ONS to incorporate spatial and spatio-temporal dependence, and to compare likelihood and Bayesian methods for small area estimation. Software developed includes the SAE R package.

Duration: August 2005 to June 2008

Researchers: Nicky Best, Sylvia Richardson, Virgilio Gómez Rubio. Philip Clarke (ONS)