HOD2: Data driven semi-automated approaches to comparative effectiveness research using electronic health record data

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Electronic storage and linkage of routinely-collected health data has opened up substantial opportunities to assess the effectiveness of medications, potentially paving the way for more personalised treatment advice to be given to patients. The expectation that electronic health records and related data will be used to measure medication effects is now written into EU legislation. However, prominent examples in which analysis of these types of data have provided misleading results have led to questions about whether these data should be used to address such questions. More recently, data-driven approaches which attempt to harness the wealth of data available in electronic health records, to recapture information which is overlooked in traditional analyses, have been used to re-analyse studies in which misleading results were obtained. In these early examples, data-driven approaches have been able to retrieve valid conclusions regarding medication effects. These methods, therefore, offer great potential to overcome the limitations of previous methods, and pave the way for a semi-automated process of obtaining estimated medication effects from routinely-collected data.Our proposal focuses on data-driven methods based on the propensity score. A propensity score analysis is a statistical approach that is very useful in accounting for differing patient characteristics between patients prescribed a medication and those who are not, in order to allow a fair comparison between those two groups to determine the effects of the medication. By modelling the process of medication prescription, propensity score methods attempt to identify patients prescribed the medication and others who are not prescribed the medication who are otherwise comparable, and measures effects of the medication by comparing health outcomes between these patients. Typically, investigators performing the analysis will select which patient characteristics are relevant to the process of medication prescription. However, much of the information that investigators would like to include is not directly available in routinely-collected data; instead there is a large amount of information about the patients' previous medical history that might collectively be relevant to the medication prescription. The task of manually selecting relevant information, from the thousands of measurements available, is not an easy one. Thus data-driven approaches which select relevant information to include in the analysis, are necessary. Our proposal aims to explore the use of these data-driven approaches, with the aim of increasing the transparency of what might appear to be a "black box" approach, while focusing on automation of the process insofar as is possible. As part of this, we will develop a suite of visual plots that can be automatically generated in the analysis, which will provide investigators with an understanding of the key inner workings of the analysis. We will also identify the optimal data-driven methods to deploy in these analyses. Unlike traditional research data, routinely-collected data is not collected according to a regular schedule, so for the purposes of analysis information that investigators would like to include can often be missing from the patient's record. We will therefore explore how best to handle missing data within the data-driven analytic approaches discussed above. The final element of our project will be to disseminate the results of our work through a broad range of channels. Our work will be relevant to a broad spectrum of quantitative researchers in medical and social science, in academic, pharmaceutical, regulatory and policy settings.