Speaker
Description
Non-inferiority studies play a crucial role in evaluating new treatments that offer potential benefits, such as reduced side effects, lower costs, or faster treatment times, compared to existing effective treatments. To determine whether a new treatment is not significantly worse than the reference treatment by more than a specified margin, non-inferiority studies are designed. However, a common criticism of such studies comparing experimental treatments with active controls is that the margin is often derived from existing literature rather than estimated based on the sample being studied. By including a placebo group in addition to the groups receiving the new treatment and the active treatment, the gold-standard design for non-inferiority studies circumvents this problem.
This research poses the interest on sample size calculation for a group-sequential design in a framework described above. In particular, based on the sample size estimation algorithm developed by Lanyu Lei (2020), that assumes normal outcomes and homoscedasticity across groups, the paper aims to i) assess the robustness of the sample size calculation algorithm in presence of deviation from normality, relaxing the homoscedasticity assumption, ii) introduce the stopping rule for futility at interim analysis, based on conditional power, and iii) describe the impact of the historical borrowing, through normalized power prior, for the placebo arm on Power and Type I error rate, both in case of “Smaller is better” and “Larger is better” design.
When the outcome variables are derived from three gamma distributions with equal variability, the statistical power consistently reaches an acceptable level using sample size calculated by the original algorithm. When σ(P)> σ(A), using the sample size by the original algorithm, the study results overpowered, otherwise, introducing heteroskedasticity, the algorithm estimates a lower sample size without loss in power and probability to reject at interim. When σ(A)> σ(P), using the sample size by the original algorithm the study results underpowered, otherwise, the proposed algorithm is able to keep stable the power and the probability to reject at interim. When the stop for futility is included, the adjustment of sample size by the Inflation Factor (IF) can contrast the loss of overall power at the end of the enrolment. Historical borrowing introduces a bias, the larger the difference between the averages in the placebo arms the more marked the bias. In general, if the historical mean is lower than current average in placebo arm leads to decrease/ increase in statistical power and type I error, on the other hand, when the historical mean surpasses current average in placebo arm leads to increase/decrease in statistical power and type I error, for the “Larger is better” and “Smaller is better” designs, respectively.
The study's web-applications, RESKOUT and FUST-WING tools, provide a user-friendly interface for sample size and power calculations to help during the three-arm non-inferiority clinical trial designs.
