Validity arguments for patient-reported outcomes: justifying the intended interpretation and use of data

Contrary to common usage in the health sciences, the term "valid" refers not to the properties of a measurement instrument but to the extent to which data-derived inferences are appropriate, meaningful, and useful for intended decision making. The aim of this study was to determine how validity testing theory (the Standards for Educational and Psychological Testing) and methodology (Kane's argument-based approach to validation) from education and psychology can be applied to validation practices for patient-reported outcomes that are measured by instruments that assess theoretical constructs in health.

The Health Literacy Questionnaire (HLQ) was used as an example of a theory-based self-report assessment for the purposes of this study. Kane's five inferences (scoring, generalisation, extrapolation, theory-based interpretation, and implications) for theoretical constructs were applied to the general interpretive argument for the HLQ. Existing validity evidence for the HLQ was identified and collated (as per the Standards recommendation) through a literature review and mapped to the five inferences. Evaluation of the evidence was not within the scope of this study.

The general HLQ interpretive argument was built to demonstrate Kane's five inferences (and associated warrants and assumptions) for theoretical constructs, and which connect raw data to the intended interpretation and use of the data. The literature review identified 11 HLQ articles from which 57 sources of validity evidence were extracted and mapped to the general interpretive argument.

Kane's five inferences and associated warrants and assumptions were demonstrated in relation to the HLQ. However, the process developed in this study is likely to be suitable for validation planning for other measurement instruments. Systematic and transparent validation planning and the generation (or, as in this study, collation) of relevant validity evidence supports developers and users of PRO instruments to determine the extent to which inferences about data are appropriate, meaningful and useful (i.e., valid) for intended decisions about the health and care of individuals, groups and populations.