03-09-2015 | Special Section: PROs in Non-Standard Settings (by invitation only) | Uitgave 3/2016
Mode of administration does not cause bias in patient-reported outcome results: a meta-analysis
- Tijdschrift:
- Quality of Life Research > Uitgave 3/2016
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Electronic supplementary material
The online version of this article (doi:10.1007/s11136-015-1110-8) contains supplementary material, which is available to authorized users.
Abstract
Purpose
Technological advances in recent decades have led to the availability of new modes to administer patient-reported outcomes (PROs). To aid selecting optimal modes of administration (MOA), we undertook a systematic review to determine whether differences in bias (both size and direction) exist among modes.
Methods
We searched five electronic databases from 2004 (date of last comprehensive review on this topic) to April 2014, cross-referenced and searched reference lists. Studies that compared two or more MOA for a health-related PRO measure in adult samples were included. Two reviewers independently applied inclusion and quality criteria and extracted findings. Meta-analyses and meta-regressions were conducted using random-effects models.
Results
Of 5100 papers screened, 222 were considered potentially relevant and 56 met eligibility criteria. No evidence of bias was found for: (1) paper versus electronic self-complete; and (2) self-complete versus assisted MOA. Heterogeneity for paper versus electronic comparison was explained by type of construct (i.e. physical vs. psychological). Heterogeneity for self-completion versus assisted modes was in part explained by setting (clinic vs. home); the largest bias was introduced when assisted completion occurred in the clinic and follow-up was by self-completion (either electronic or paper) in the home.
Conclusions
Self-complete paper and electronic MOA can be used interchangeably for research in clinic and home settings. Self-completion and assisted completion produce equivalent scores overall, although heterogeneity may be induced by setting. These results support the use of mixed MOAs within a research study, which may be a useful strategy for reducing missing PRO data.