6 Ways to interpret patient reported outcome data

Interpretation of data derived from a patient reported outcome measure (PROM) from a clinical trial or patient outcomes study can be challenging, particularly with regard to understanding the meaning of what a change or difference in a score means clinically.

Interpretation of PROM data is linked with both the objectives of the clinical trial and the constructs measured by the PROM and as a consequence should be a key factor in the development of any measurement strategy.

Understandably, optimising the patient’s health is of primary importance for the clinician and therefore, is more likely to look for improvements in health status. However, what might be of specific interest to the clinician in terms of outcome may not always correspond to what is of relevance to the patient.

Remembering that PROMs are assessing outcomes from the perspective of the patient, any change in the score may or may not be related to a clinical outcome.  As an example, patients with diabetes who have changed to a different insulin treatment considered more convenient – but, in all other respects is the same – are unlikely to report any improvement in clinical outcomes but, may well report that their QoL has been enhanced as a consequence of the greater flexibility in their lives.

Despite the many thousands of PROMs developed and their application, there is often lacking an understanding as to what a PROM score represents and what is a meaningful change in score1. Should the differences be big or small and what are the implications for clinical practice and research?

When large samples (macro level) of patients are studied, differences in PROM scores between patient groups might be numerically small but, highly statistically significant. However, data obtained at the macro level are difficult to apply at the individual patient (micro) level2.

Interpreting PROM scores is also something that cannot be established from a one-off study but, is based on a body of evidence developed over time through a variety of studies, and perspectives1. Nevertheless, there are a number of approaches that can aid the interpretation of PROM data which are summarised below1.

  • Minimal Important Difference (MID) – the smallest difference in a score that is considered to be worthwhile or important.
  • Known groups – the mean scores underlying particular clinical groups or clinical indicators which give rise to them and which can be used as a clinically based benchmark to compare other groups.
  • Normative and reference groups – mean scores from defined large populations to provide normative data – typical scores – called norms. Mean scores from a particular study can be compared with the population norms.
  • Statistical significance – The statistical significance of the probability of treatment (A) is better than treatment (B).
  • Effect size – A way of quantifying the difference between two groups of patients that has many advantages over the use of statistical significance alone and emphasises the size of the difference rather than confounding this with sample size.Cumulative distribution functions
  • Cumulative distribution functions (CDF) – The CDF shows a continuous plot of the proportion of patients at each point along the continuum of the scale score continuum experiencing change at that level or lower levels.

Other approaches include, reference to the PROMs content when interpreting its score as well as comparing them with known clinical parameters such as days in hospital and illness severity or the proportion of patients whose PROM score improve or get worse after intervention.

If you would like to know more about how to interpret PROM data or would like to discuss this issue contact: info@dhpresearch.com

References

1. Osoba D, King M (1995) Meaningful differences. In: Fayers P, Hays R, eds. assessing quality of life in clinical trials. 2nd ed Oxford University Press, Oxford: 243-258

2. Cella D, Eton DJ, Lai JS, Peterman AH, Merkel DE (2002) Combining anchor and distribution-based methods to derive minimal clinically important differences on   the Functional Assessment of Cancer Therapy (FACT) anemia and fatigue scales. Journal of Pain & Symptom Management 24, 547-561



Categories: Patient reported outcomes

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