Methods for handling missing data and covariate measurement error in individual participant data meta-analysis

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In recent decades there has been a concerted drive towards ensuring medicine is evidence based, meaning that decisions about patient care and public health are made in light of the current best available evidence. Central to establishing what constitutes the best available evidence in regards to a particular clinical or public health question is the process of evidence synthesis. For clinical questions which can be numerically quantified, the primary tool for synthesizing evidence is meta-analysis, which involves taking the results from previous studies and combining them to give a single summary estimate of the quantity of interest.The gold standard approach to meta-analysis involves collating the individual participant data (IPD) from all of the previously conducted relevant studies and analysing the resulting combined dataset. Pooling the individual level data confers a number of advantages compared to the traditional meta-analysis approach which involves combining the overall results of studies (as opposed to analysing their original, individual level data). These advantages include the ability to make statistical adjustments for a consistent set of variables, exploration of whether treatment effects vary between different groups of patients, and the ability to investigate the shape of relationships between variables.However, there are a number of issues which threaten the potential of IPD meta-analysis. Principal among these are issues caused by missing data and measurement error. Missing data occur for two reasons in IPD meta-analyses. The first is when some studies did not collect data on one or more variables which are of interest, such that the values of these variables are missing for all participants in these studies. The second occurs when, for a variety of reasons, some participants have missing values despite the fact the study intended to collect the variable. Missing data cause results to be less precise and possible biased. Measurement error occurs when variables of interest can only be measured imprecisely. If ignored, measurement error also causes biases in results.The proposed research seeks to develop new statistical methods to deal with these two issues. By doing so, they will enable researchers to obtain more precise and less biased estimates from IPD meta-analyses, thereby giving more accurate answers to important clinical and public health questions. New methods will be published in scientific journals, and methods implemented into statistical software packages to enable them to be used by researchers. This will help enable medical practitioners and public health experts to base their decisions and policies on the best available evidence, thus improving health outcomes for patients and the population more generally.The work will be conducted by the Fellowship applicant.