HMD: Missing data in propensity score analyses of Electronic Health Records Data

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Electronic storage and linkage of routinely-collected health data has opened up substantial opportunities to address important questions, not least those relating to the possible harms and benefits of long term medication use. Such information is important to patients and health care professionals alike. Indeed, the expectation that EHR and related data will be used to measure medication effects is now written into EU legislation. Thus we expect the use of electronic health records for research will increase dramatically.Using data taken from electronic health records to investigate medication effects raises substantial challenges. In particular, patients who are prescribed a particular medication will tend to be very different from those who are not. Disentangling these patient differences from effects of the medication is a key aim of observational epidemiology. This process of disentangling, which is challenging even when information concerning patient characteristics (such as their cholesterol level or age) is available, is greatly complicated when some information is unavailable. 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 measure 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. Methods for accounting for missing patient information within a propensity score analysis, however, remain poorly understood.Failure to adequately handle missing information in an investigation of medication effects could lead to incorrect conclusions regarding the benefits, or harms, of the medication. In order to avoid this, it is vital to develop appropriate ways of dealing with missing information within propensity score analyses. There is an established literature concerning how to handle missing information within other types of analyses, particularly those focused around modelling the outcome as a function of the patient characteristics. However, the way in which the patient characteristics are used in this outcome modelling approach and propensity score analyses differs in practically important ways. Thus the way in which missing data should be handled cannot be directly learnt from our experiences within the outcome regression modelling context. Our proposal aims to develop guidelines for researchers undertaking these analyses to help them select an appropriate method for handling their missing data, and to understand the assumptions under which their conclusions regarding the effects of the medication are valid. As part of this, we will take sophisticated statistical methods for handling missing data that have proved themselves outside the propensity score setting, such as multiple imputation, and develop and apply them in a way that is consistent with the goals and structure of propensity score analyses.Because medication use of a particular patient will often change over time, as will many of the patient's characteristics, it is often desirable to take this into account in the statistical analysis. This can be done through the application of an extension of the propensity score approach, called marginal structural models. A final aspect of our proposal, therefore, seeks to understand how to extend our proposed missing data methods to this setting. Through our broad based dissemination strategy (described elsewhere) our work will be relevant to a broad range of quantitative researchers in medical and social science, in academic, pharmaceutical, regulatory and policy settings.