Abstract

BackgroundA Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. Using prostate cancer as a case study, we identified, summarized, and critically appraised published COS development studies and assessed the degree of overlap between them and selected real-world data (RWD) sources.MethodsWe conducted a scoping review of the Core Outcome Measures in Effectiveness Trials (COMET) Initiative database to identify all COS studies developed for prostate cancer. Several characteristics (i.e., study type, methods for consensus, type of participants, outcomes included in COS and corresponding measurement instruments, timing, and sources) were extracted from the studies; outcomes were classified according to a predefined 38-item taxonomy. The study methodology was assessed based on the recent COS-STAndards for Development (COS-STAD) recommendations. A ‘mapping’ exercise was conducted between the COS identified and RWD routinely collected in selected European countries.ResultsEleven COS development studies published between 1995 and 2017 were retrieved, of which 8 were classified as ‘COS for clinical trials and clinical research’, 2 as ‘COS for practice’ and 1 as ‘COS patient reported outcomes’. Recommended outcomes were mainly categorized into ‘mortality and survival’ (17%), ‘outcomes related to neoplasm’ (18%), and ‘renal and urinary outcomes’ (13%) with no relevant differences among COS study types. The studies generally fulfilled the criteria for the COS-STAD ‘scope specification’ domain but not the ‘stakeholders involved’ and ‘consensus process’ domains. About 72% overlap existed between COS and linked administrative data sources, with important gaps. Linking with patient registries improved coverage (85%), but was sometimes limited to smaller follow-up patient groups.ConclusionsThis scoping review identified few COS development studies in prostate cancer, some quite dated and with a growing level of methodological quality over time. This study revealed promising overlap between COS and RWD sources, though with important limitations; linking established, national patient registries to administrative data provide the best means to additionally capture patient-reported and some clinical outcomes over time. Thus, increasing the combination of different data sources and the interoperability of systems to follow larger patient groups in RWD is required.

Highlights

  • A Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition

  • Data extraction: study characteristics and methodology From a total of 19 studies retrieved from the Core Outcome Measures in Effectiveness Trials (COMET) database under the ‘prostate cancer’ disease name, 1 was removed as a duplicate, 1 was unpublished, 4 were excluded because they were classified by COMET as ‘systematic reviews’ and 2 were excluded because classified as ‘recommendations’

  • Synthesis of results This research aimed at identifying COS development studies in prostate cancer, critically appraising their methodological quality, and exploring the extent to which recommended COS are measurable in available real-world data (RWD) sources through a mapping exercise

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Summary

Introduction

A Core Outcomes Set (COS) is an agreed minimum set of outcomes that should be reported in all clinical studies related to a specific condition. There has been a rapid acceleration in the use of real-world data (RWD) in clinical research and practice. The United States (US) Food and Drug Administration (FDA) reports a similar definition [2]. Among these sources, longitudinal databases, and especially EHRs, provide detailed records for high numbers of patients, and they continue to grow in size, clinical detail, and accessibility through data linkage, standardization, and sharing. Despite the growing use of real-world evidence to support broader use of effective therapies and to contribute useful information about treatment effectiveness, just because RWD exist does not mean that those data will be useful for every research question. The utility of RWD data can generally be improved by understanding how well available data characterizes the outcomes of interest, recognizing that information recorded in structured fields are easier to find and analyse than unstructured notes, which may not even be accessible to researchers [5]

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