The use of hospital episode statistics (HES) records alongside clinical trials

Abstract

Introduction: Economic evaluation alongside trials is often based on data collected through self-reported questionnaires. Consequently, there are consistent problems with incomplete (missing) data irrespective of how well data collection methods are designed. Missing data poses a significant problem when conducting economic analyses of clinical trials, potentially leading to incorrect conclusions being drawn concerning an interventions' cost-effectiveness. Trial design: Trials which collect economic data through multiple sources, such as hospital records in addition to patient self-reports, present an efficient and relatively simple means of investigating the impact different sources of data may have on the outcome of the economic evaluation of a trial. The present study uses data from a large randomised controlled trial of a family based intervention which collected health care resource use via both self-report, as well as through Hospital Episode Statistics (HES) data, sourced from the Health and Social Care Information Centre (HSCIC), to explore one method of dealing with missing data. Discussion: Multiple Imputation (MI) is the recommended statistical approach for evaluating the impact of missing data. Using data from the randomised controlled trial, we are able to compare two datasets; one derived via multiple imputation of self-reported data which contained missing values, and the other being HES data, which is assumed to be an accurate and objective representation of actual health care resource use. Conclusion: These data sets allow us to assess the effectiveness of multiple imputation at estimating the 'truth' and explore how the different methods of data collection can impact on final cost-effectiveness results.