Hip fracture in the elderly multidisciplinary rehabilitation (FEMuR) feasibility study: testing the use of routinely collected data for future health economic evaluations

Augmenting clinical trial economic analysis by linking cancer trial data to administrative data: current landscape and future opportunities

BACKGROUND

Economic analyses based on clinical trial data are costly and time consuming, and alternative methods for performing economic analyses should be explored.

OBJECTIVE AND METHODS

In this perspective, we examine the emerging role of administrative data for economic analyses in cancer.

RESULTS

Compared with routinely collected clinical trial data, routinely collected administrative data have several strengths including high capture rates for healthcare encounters, less resource utilisation, low rates of misclassification, long follow-up periods and the opportunity to collect data points not traditionally captured in clinical trials. However, there are also limitations including the need for accurate data linkage across multiple databases and systems, the costs and time associated with data linkage, the potential time lag between trial data collection and the availability of administrative data, and limited data on quality of life, toxicity and indirect costs. In this perspective, we identify important barriers and potential solutions to performing economic analyses for oncology using administrative data, and outline strategies to increase research in this field.

CONCLUSION

The use of routinely collected administrative data sets for economic analyses of clinical trials presents a unique opportunity that could complement and validate economic analyses based on trial-level data.

Pilot and feasibility studies for pragmatic trials have unique considerations and areas of uncertainty

BACKGROUND AND OBJECTIVE

Feasibility studies are increasingly being used to support the development of, and investigate uncertainties around, future large-scale trials. The future trial can be designed with either a pragmatic or explanatory mindset. Whereas pragmatic trials aim to inform the choice between different care options and thus, are designed to resemble conditions outside of a clinical trial environment, explanatory trials examine the benefit of a treatment under more controlled conditions. There is existing guidance for designing feasibility studies, but none that explicitly considers the goals of pragmatic designs. We aimed to identify unique areas of uncertainty that are relevant to planning a pragmatic trial.

RESULTS

We identified ten relevant domains, partly based on the pragmatic-explanatory continuum indicator summary-2 (PRECIS-2) framework, and describe potential questions of uncertainty within each: intervention development, research ethics, participant identification and eligibility, recruitment of individuals, setting, organization, flexibility of delivery, flexibility of adherence, follow-up, and importance of primary outcome to patients and decision-makers. We present examples to illustrate how uncertainty in these domains might be addressed within a feasibility study.

CONCLUSION

Researchers planning a feasibility study in advance of a pragmatic trial should consider feasibility objectives specifically relevant to areas of uncertainty for pragmatic trials.

Self-reported and routinely collected electronic healthcare resource-use data for trial-based economic evaluations: the current state of play in England and considerations for the future

BACKGROUND

Randomised controlled trials (RCTs) are generally regarded as the "gold standard" for providing quantifiable evidence around the effectiveness and cost-effectiveness of new healthcare technologies. In order to perform the economic evaluations associated with RCTs, there is a need for accessible and good quality resource-use data; for the purpose of discussion here, data that best reflect the care received. Traditionally, researchers have developed questionnaires for resource-use data collection. However, the evolution of routinely collected electronic data within care services provides new opportunities for collecting data without burdening patients or caregivers (e.g. clinicians). This paper describes the potential strengths and limitations of each data collection method and then discusses aspects for consideration before choosing which method to use.

MAIN TEXT

We describe electronic data sources (large observational datasets, commissioning data, and raw data extraction) that may be suitable data sources for informing clinical trials and the current status of self-reported instruments for measuring resource-use. We assess the methodological risks and benefits, and compare the two methodologies. We focus on healthcare resource-use; however, many of the considerations have relevance to clinical questions. Patient self-report forms a pragmatic and cheap method that is largely under the control of the researcher. However, there are known issues with the validity of the data collected, loss to follow-up may be high, and questionnaires suffer from missing data. Routinely collected electronic data may be more accurate and more practical if large numbers of patients are involved. However, datasets often incur a cost and researchers are bound by the time for data approval and extraction by the data holders.

CONCLUSIONS

Owing to the issues associated with electronic datasets, self-reported methods may currently be the preferred option. However, electronic hospital data are relatively more accessible, informative, standardised, and reliable. Therefore in trials where secondary care constitutes a major driver of patient care, detailed electronic data may be considered superior to self-reported methods; with the caveat of requiring data sharing agreements with third party providers and potentially time-consuming extraction periods. Self-reported methods will still be required when a 'societal' perspective (e.g. quantifying informal care) is desirable for the intended analysis.