KEY CONSIDERATIONS FOR THE COLLECTION OF PATIENT REPORTED OUTCOME (PRO) DATA IN REAL WORLD (RW) STUDIES

Author(s)

Rylands AJ, Boxell E, Bottomley CJ
pH Associates (an OPEN Health company), Marlow, UK

BACKGROUND: The collection of patient reported outcome (PRO) data in the real world (RW) is of growing importance to key patient and industry stakeholders who attribute value to data collected from patients outside of highly selected clinical trial populations, in observational, and often more clinically realistic populations. Regulatory authority guidelines informing researchers on the design and conduct of PRO assessment (e.g. FDA PRO guidelines) focus on randomised clinical trials and the recommendations are not always proportionate to RW study settings. AIM: This work aims to summarise the key considerations for researchers collecting PRO data in RW studies. KEY CONSIDERATIONS include; a.) Study design: the choice of a retrospective or prospective design, and potentially the amount of missing data, will be influenced by whether PRO data are routinely collected in clinical practice; b.) Patient recruitment: the level of contact patients have with healthcare services will impact recruitment methodologies and may affect levels of patient engagement with the study; c.) Data collection: RW studies cannot influence the scheduling of clinic visits which is likely to impact the timing and method of PRO measurement; d.) Minimisation of patient burden: the frequency of PRO measurement and subsequent follow-up must be limited to the necessary minimum in RW studies so as not to impact routine clinical care. CONCLUSIONS: Whilst authors of the International Society for Quality of Life Research (ISOQOL) have published a User's Guide for Implementing PROs Assessment in Clinical Practice, there are no specific guidelines for optimising the design and conduct of patient centred research studies in RW settings. We propose that a specific set of guidelines should be developed.

Conference/Value in Health Info

2018-11, ISPOR Europe 2018, Barcelona, Spain

Value in Health, Vol. 21, S3 (October 2018)

Code

PCP65

Topic

Methodological & Statistical Research

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference

Disease

Multiple Diseases

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