Optimizing the use of patient data to improve outcomes for patients: narcotics for chronic noncancer pain

Abstract

Randomized trials can provide important direction to clinical decision-making; however, their strength of inferences may be weakened by methodological limitations, the extent that their reported outcomes fail to address patient-important end points and by failing to report results that provide interpretable estimates of magnitude of effect. Strategies that investigators can use to address interpretability include reporting mean differences between groups in relation to the minimal important difference and reporting the proportion of patients who benefit from treatment and the associated number needed to treat. These strategies also apply to reporting pooled estimates from meta-analyses, even when studies use different instruments to measure the same construct. We illustrate these techniques using, as an example, current evidence for the use of opioids in chronic noncancer pain.

Keywords::

• minimal important difference
• number needed to treat
• opioid
• pain
• systematic review

Acknowledgements

The Medically Unexplained Syndromes Study Group is comprised of the following individuals: Maziar Badii, Arthur Barsky, Jason W Busse, John Dufton, Nelson Greidanus, Gordon H Guyatt, Catherine Krasnik, Victor M Montori, Edward Mills, Roohi Qureshi, Steven Reid and Ping Wu.

Financial & competing interests disclosure

Jason W Busse is funded by a New Investigator Award from the Canadian Institutes of Health Research and Canadian Chiropractic Research Foundation. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.