![]() Here, -Δ is an integral part not only of the design, as with γ in superiority trials, but of the analysis as well, a role that γ does not play in superiority trials. In the inverted world of non-inferiority, the alternative hypothesis seems 'null', whereas the null hypothesis includes a specified treatment difference of -Δ. The alternative hypothesis states that the difference in the effect between the new and old interventions is less than -Δ (Figure 1). Instead, it states that the new treatment is worse than the old by more than -Δ, where -Δ is the 'non-inferiority margin'. Thus the null hypothesis seems backwards, in a sense, as this hypothesis is not 'null' at all. Gpass jt money dl how to#The preselected γ plays no formal statistical role in the analysis of a superiority trial, although the difference in magnitude between the hypothesized γ and the estimated effect is likely to influence how to interpret the results.Ī non-inferiority experiment, by contrast, tries to show that the new intervention is not 'inferior' to the previous one, or, more precisely, that the new intervention is 'not unacceptably worse' than the intervention used as the control. At the end of the trial, the estimated effect may be bigger or smaller than γ, but as long as the lower bound of your 95% CI is above zero, you may reject your null hypothesis. The goal of your experiment is to reject that null hypothesis, thus γ is in some sense a tool to help you select your sample size. You use your chosen Type I error rate or α, your desired power, and γ to select your sample size. ![]() For situations in which a smaller outcome is better than a larger outcome (for example, tumor size in cancer applications) the signs in this paper would change from negative to positive. Throughout this paper, we assume that larger positive outcomes are better than smaller positive outcomes, and a positive treatment difference, γ, provides evidence of benefit. You then choose an alternative hypothesis stating that the difference between the means, or some other statistic, is γ. Gpass jt money dl trial#When designing a trial to show superiority of a new intervention, you specify a null hypothesis consistent with the word 'null', your hypothesis asserts that the two interventions are the same. (No wonder people flee to Bayesian inference!) But as difficult and counterintuitive as classical statistics may be, they are simple compared with the problems of inference in non-inferiority trials. Then, when you try to estimate the magnitude of the effect, all you can say is that if you repeated your experiment an infinite number of times and calculated your confidence interval (CI) as you were taught, 95% of those intervals would cover the true effect. ![]() You cannot prove what you want to prove all you can say is that the data you observe provide sufficient evidence to reject the hypothesis that the two interventions have the same effect. ![]() If you want to get somewhere else, you must run at least twice as fast as that!'Ĭlassical statistics is non-intuitive enough when you are trying to show that a new intervention is better than a previous one. 'Now, here, you see, it takes all the running you can do, to keep in the same place. ![]() 'A slow sort of country!' said the Queen. 'Well, in our country,' said Alice, still panting a little, 'you'd generally get to somewhere else - if you ran very fast for a long time as we've been doing.' ![]()
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