Comparison of three sample size calculation methods for non-inferiority vaccine trials with multiple continuous co-primary endpoints

Abstract

Clinical trials that study immunogenicity of combination vaccines often have less power than desirable. To make up for the reduction in statistical power at the study level, researchers have to increase the study sample size. To study immunogenicity variables, we used the geometric mean concentration of immune response after vaccination as immunologic endpoint and compared 3 sample size calculation methods: the "Inflation factors" method, the "Incrementing" method, and the "Bonferroni correction" method when there are multiple continuous co-primary endpoints. The parameters were set according to the actual situation of the use of combination vaccines and the simulation results were used as reference. The present study demonstrates that all 3 methods are applicable when the effect size of each endpoint is similar and the endpoints are at most weakly correlated, but when there is a true difference in effect sizes among endpoints, the "Incrementing" method has the best performance.

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Cost, time savings and effectiveness of wearable devices for remote monitoring of patient rehabilitation after total knee arthroplasty: study protocol for a randomized controlled trial

“…Some researchers recommend that the sample size for each endpoint should be calculated separately, and the largest sample size should be chosen as the overall trial's sample size [ 30 ]. The study is designed with two primary outcome measures: cost and time.…”