Abstract
BackgroundThe size of the margin strongly influences the required sample size in non-inferiority and equivalence trials. What is sometimes ignored, however, is that for trials with binary outcomes, the scale of the margin – risk difference, risk ratio or odds ratio – also has a large impact on power and thus on sample size requirement. When considering several scales at the design stage of a trial, these sample size consequences should be taken into account. Sometimes, changing the scale may be needed at a later stage of a trial, for example, when the event proportion in the control arm turns out different from expected. Also after completion of a trial, a switch to another scale is sometimes made, for example, when using a regression model in a secondary analysis or when combining study results in a meta-analysis that requires unifying scales. The exact consequences of such switches are currently unknown.Methods and ResultsThis article first outlines sample size consequences for different choices of analysis scale at the design stage of a trial. We add a new result on sample size requirement comparing the risk difference scale with the risk ratio scale. Then, we study two different approaches to changing the analysis scale after the trial has commenced: (1) mapping the original non-inferiority margin using the event proportion in the control arm that was anticipated at the design stage or (2) mapping the original non-inferiority margin using the observed event proportion in the control arm. We use simulations to illustrate consequences on type I and type II error rates. Methods are illustrated on the INES trial, a non-inferiority trial that compared single birth rates in subfertile couples after different fertility treatments. Our results demonstrate large differences in required sample size when choosing between risk difference, risk ratio and odds ratio scales at the design stage of non-inferiority trials. In some cases, the sample size requirement is twice as large on one scale compared with another. Changing the scale after commencing the trial using anticipated proportions mainly impacts type II error rate, whereas switching using observed proportions is not advised due to not maintaining type I error rate. Differences were more pronounced with larger margins.ConclusionsTrialists should be aware that the analysis scale can have large impact on type I and type II error rates in non-inferiority trials.
Highlights
For ethical reasons, in several disease areas it is becoming increasingly difficult to justify testing the efficacy of new treatments against placebo
If the same percentages were formulated as failure rates instead of success rates, that is, the percentage of patients not achieving a singleton pregnancy within 1 year, excluding an risk ratio (RR) of 1.21 (72.5% divided by 60%) would require many more patients per arm (235)
The power on the risk difference (RD) scale and the odds ratio (OR) scale lies between them, crossing at some point. This shows that power increases when switching from the RD scale to the RR scale, both when using the anticipated and when using the observed control success proportion during mapping of the non-inferiority margin
Summary
In several disease areas it is becoming increasingly difficult to justify testing the efficacy of new treatments against placebo. Different scales for the analysis and corresponding non-inferiority margin may lead to different sample size requirements This phenomenon has been pointed out in some statistical papers,[4,5,6] it is not known to many trialists. What is sometimes ignored is that for trials with binary outcomes, the scale of the margin – risk difference, risk ratio or odds ratio – has a large impact on power and on sample size requirement. Methods and Results: This article first outlines sample size consequences for different choices of analysis scale at the design stage of a trial. Our results demonstrate large differences in required sample size when choosing between risk difference, risk ratio and odds ratio scales at the design stage of non-inferiority trials. Conclusions: Trialists should be aware that the analysis scale can have large impact on type I and type II error rates in non-inferiority trials
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