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Through the use of statistical regression, potential baseline differences in QALYs can be adjusted for [ 58].
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*Difference in QALYs allows for baseline differences in EQ-5D, sex, and age.
If benefit is maintained over 12 months then at a threshold value of £20,000 the intervention is expected to be cost-effective in 89.6% of simulations.> When we calculate QALYs adjusted for baseline differences in the control and intervention population, the intervention becomes less likely to be cost-effective across all time-horizons and threshold values.
For QALYs, it may be important to adjust for baseline differences in health status between the trial groups.
Scenario analysis was conducted to explore the effects of changing the way QALYs were estimated and adjusting for baseline difference in the population.
After correction for baseline utility, the difference in QALYs between PST and UC was in favour of the PST group (0.03), but statistically non-significant (95% CI −0.02; 0.08).
In addition, adjusted differences in QALYs were calculated since future utility scores are correlated with baseline utility scores and this may be a source of bias.
Adjusting for baseline differences, the incremental QALY difference was −0.014 955% CI −0.034 to 0.007), indicating no significant difference in health benefit (table 3).
17 To obtain adjusted differences in QALYs between the randomisation groups, linear regression analysis was performed to calculate the difference between EQ-5D utility score at 6-month follow-up adjusted for baseline EQ-5D utility score.
Between-group differences in QALYs will be analyzed per period using t-test for unpaired observations.
Differences in QALYs will be estimated from the 'area under the utility curve'.
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