When we talk of bias we tend to mean bias due to factors that affect the “answer” to a given question. But a type of bias can arise when a question is posed in a way that it predisposes to a certain result, say by comparing an optimal dose of medicine A with a sub-optimal dose of medicine B, or comparing medicine A with medicine C when it is likely to fare less well against medicine B. Conventional tools for the assessment of methodological quality of individual trials are adept at picking up the former type of bias, but discerning the latter usually requires a broader view based on medical knowledge. The CLAHRC WM Director co-authored a paper, led by Fujian Song of East Anglia showing how network meta-analysis can explore this second type of bias.
A further example of use of network meta-analysis to explore this form of bias due to choice of a sub-optimal comparator comes from a recent study comparing reductions in LDL cholesterol in industry-funded versus publicly-funded RCTs of various statins. The combined result across 183 RCTs failed to show a difference in end-points between statins funded in these two ways when the playing field was levelled by using network meta-analysis to compare optimal doses. The authors observed no obvious effects of various attributes of methodological quality on study outcome.
— Richard Lilford, CLAHRC WM Director
- Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J Clin Epidemiol. 2008; 61(5): 455-63.
- Naci H, Dias S, Ades AE. Industry sponsorship bias in research findings: a network meta-analysis of LDL cholesterol reduction in randomised trials of statins. BMJ. 2014; 349: g5741.