Economic Evaluations Alongside Efficient Study Designs Using Large Observational Datasets: the PLEASANT Trial Case Study

Abstract

BackgroundLarge observational datasets such as Clinical Practice Research Datalink (CPRD) provide opportunities to conduct clinical studies and economic evaluations with efficient designs.ObjectivesOur objectives were to report the economic evaluation methodology for a cluster randomised controlled trial (RCT) of a UK NHS-delivered public health intervention for children with asthma that was evaluated using CPRD and describe the impact of this methodology on results.MethodsCPRD identified eligible patients using predefined asthma diagnostic codes and captured 1-year pre- and post-intervention healthcare contacts (August 2012 to July 2014). Quality-adjusted life-years (QALYs) 4 months post-intervention were estimated by assigning utility values to exacerbation-related contacts; a systematic review identified these utility values because preference-based outcome measures were not collected. Bootstrapped costs were evaluated 12 months post-intervention, both with 1-year regression-based baseline adjustment (BA) and without BA (observed).ResultsOf 12,179 patients recruited, 8190 (intervention 3641; control 4549) were evaluated in the primary analysis, which included patients who received the protocol-defined intervention and for whom CPRD data were available. The intervention’s per-patient incremental QALY loss was 0.00017 (bias-corrected and accelerated 95% confidence intervals [BCa 95% CI] –0.00051 to 0.00018) and cost savings were £14.74 (observed; BCa 95% CI –75.86 to 45.19) or £36.07 (BA; BCa 95% CI –77.11 to 9.67), respectively. The probability of cost savings was much higher when accounting for BA versus observed costs due to baseline cost differences between trial arms (96.3 vs. 67.3%, respectively).ConclusionEconomic evaluations using data from a large observational database without any primary data collection is feasible, informative and potentially efficient.Clinical Trials Registration Number: ISRCTN03000938.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-016-0484-y) contains supplementary material, which is available to authorized users.

Resource use and cost of annual health checks in primary care for people with intellectual disabilities

“…Gathering data on patient services from government databases can also be a more efficient way to conduct economic evaluations of services than conducting full‐scale randomised control trials (Franklin et al . ).…”

“…In addition, as this is an observational study, the results could have been influenced by unobserved confounding factors contributing to the health care costs. Gathering data on patient services from government databases can also be a more efficient way to conduct economic evaluations of services than conducting full-scale randomised control trials (Franklin et al 2017).…”

An Educational Review About Using Cost Data for the Purpose of Cost-Effectiveness Analysis

“…For primary care, McDonnell et al [46] have amalgamated a list of UK datasets that may be of interest (not all contain resource-use information). An issue with electronic datasets is that they may only adequately record data for that particular service; thus, previous studies have described the need for linked data [47][48][49]. Asaria et al [50] have described key challenges and opportunities of using linked records to estimate healthcare costs.…”

“…There are also concerns with data quality and missingness within such large datasets that require assessment and validation [51,52], and the exchangeability of using person-reported or routine care data is also questionable as they may produce quite different estimates [53,54]. With the advent of routinely collected data, there is a growing interest in how to utilise these data in trials, observational studies, and modelling, the methodology for which will develop as these data become more accessible and reliable [48,55]. It is important to note that no data collection method can currently be considered the 'gold standard'.…”

“…They recommend the use of a regressionbased adjustment using baseline patient characteristics or, when these are not sufficiently present, baseline costs as a substitute. Franklin et al [48] have made the case that adjusting for baseline costs is important in their own right, not just as a substitute to patient characteristics. They also suggest that there may be bias related to the data recording mechanisms (routinely collected primary care data in this case study) and 'frequent attenders' (those who utilise services more than the 'norm'), which can be controlled for using a regression-based baseline adjustment.…”