Table 4.

Results of primary and sensitivity analyses (based on 3000 bootstrap simulations).

Analysis		Incremental costs, Aus $ (95% CI)	Incremental effects, quality-adjusted life year (95% CI)	ICERa, mean (95% CI)	Distribution over the ICER plane (%)
					NEb	NWb	SEb	SWb
Primary analysis
	Intention-to-treat analysis	−79 (−342 to 134)	0.01 (0.01 to 0.02)	Dominant (dominant to Aus $11,928)	27	—c	73	—
	Complete case analysis	−130 (−590 to 226)	0.01 (0.00 to 0.02)	Dominant (dominant to Aus $24,529)	29	—	71	—
Sensitivity analysis
	Dropout rate 10% (cover 17% population); cost development per case: Aus $3.82	−85 (−363 to 134)	0.01 (0.00 to 0.02)	Dominant (dominant to Aus $13,035)	25	—	75	—
	Dropout rate 90% (cover 2% population); cost development per case: Aus $34.40	−50 (−319 to 159)	0.01 (0.00 to 0.02)	Dominant (dominant to Aus $14,564)	37	—	63	—

^aICER: incremental cost-effectiveness ratio, based on 3000 bootstrap simulation.

^bIn the northeast (NE) quadrant, the intervention is cost-effective if the ICER falls under the specified value-for-money criterion because the intervention is more effective and costlier than the comparator. In the southeast (SE) quadrant, the intervention is less costly and more effective than the comparator (ie, dominant); therefore, the intervention is likely to be excellent for value-for-money. In the southwest (SW) quadrant, the intervention is less costly and less effective; therefore, the decision to adopt the intervention may be based on decision-makers willingness to accept some health loss relative to cost-saving. Finally, in the northwest (NW) quadrant, the results show that the intervention is associated with greater costs but less health gain, therefore, not a good option to adopt.

^cNot applicable.