Table 3.

Approach to multiplicity due to multiple treatment comparisons

a) Review: multiplicity approach taken
	Formal adjustment	Hierarchical testing	Other approach	None
Related treatments	7/15a	1/15b	1/15c	6/15
Distinct treatments	2/8d	0/8	0/8	6/8
b) Survey: responses to posed scenarios
		Yes	No	Unsure
Would you consider adjusting for multiplicity arising from making multiple treatment comparisons?		24/27 (89%)	1/27 (4%)	2/27 (7%)
Consider a parallel group trial with three treatment arms, where all comparisons are of interest. Would you adjust for multiplicity in the following scenarios?
Two of the treatment arms are related, e.g. Group 1 = placebo, Group 2 = low drug dose, Group 3 = high drug dose		22/27 (81%)	1/27 (4%)	4/27 (15%)
The three treatment arms are unrelated, including one placebo arm, e.g. Group 1 = placebo, Group 2 = drug, Group 3 = exercise		16/27 (59%)	7/27 (26%)	4/27 (15%)
The three treatment arms are unrelated, but all are active treatments, e.g. Group 1 = drug, Group 2 = exercise, Group 3 = education		19/27 (70%)	6/27 (22%)	2/27 (7%)
Would you be more likely to adjust for multiplicity if the number of treatment arms was increased?		12/27 (44%)	12/27 (44%)	3/27 (11%)

Notes: ^a Three trials performed a Bonferroni correction, one a Holm correction, two a Hochberg correction and one used a 1% significance level for all treatment comparisons

^b Two treatment comparisons were split into primary and secondary hypotheses and analysed in a hierarchical manner

^c A post-hoc Bonferroni correction was performed, although this was not the primary analysis for the trial

^d One trial performed a Bonferroni correction and one used Dunnett’s procedure