Additional file 1 of Statistical design and analysis of controlled human malaria infection trials
Creators: name: Tian, Xiaowen
name: Janes, Holly E.
name: Kublin, James G.
Publisher: name: figshare
Year: 2024
Subjects: subject: Statistics
subject: FOS: Mathematics

description: Additional file 1: Table S1. Power analysis for RLD. The results are based on simulations with N = 28, ρ = 0, p0 = 0.25. Table S2. Power analysis for RLD. The results are based on simulations with N = 28, ρ = 0, p0 = 0.5. Table S3. Power analysis for RLD. The results are based on simulations with N = 28, ρ = 0, p0 = 0.75. Table S4. Power analysis for SHD. The results are based on simulations with N = 28, ρ = 0, p0 = 1. Table S5. Power analysis for RLD. The results are based on simulations with N = 16, ρ = 0, p0 = 0.25. Table S6. Power analysis for RLD. The results are based on simulations with N = 16, ρ = 0, p0 = 0.5. Table S7. Power analysis for RLD. The results are based on simulations with N = 16, ρ = 0, p0 = 0.75. Table S8. Power analysis for SHD. The results are based on simulations with N = 16, ρ = 0, p0 = 1. Table S9. Power analysis for RLD. The results are based on simulations with N = 40, ρ = 0, p0 = 0.25. Table S10. Power analysis for RLD. The results are based on simulations with N = 40, ρ = 0, p0 = 0.5. Table S11. Power analysis for RLD. The results are based on simulations with N = 40, ρ = 0, p0 = 0.75. Table S12. Power analysis for SHD. The results are based on simulations with N = 40, ρ = 0, p0 = 1. Figure S1. Quantile-quantile plot of time to infection from 5 previously published CHMI studies. Figure S2. Probability of remaining uninfected against the number of challenges under leaky (ρ = 0) and mixture model (ρ = 0.2). p1 and p0 represent the probability of becoming infected after a single challenge in the intervention and control group, respectively. Figure S3. Power and type I error comparisons: Results are based on simulations with N = 16. The upper panels demonstrate the power of the log-rank test with different sample size allocations. Triangles represent simulations with $$\frac{{n_{1} }}{{n_{0} }}$$ n 1 n 0 = 1 and circles represent simulations with $$\frac{{n_{1} }}{{n_{0} }}$$ n 1 n 0 = 3. The lower panels demonstrate the absolute difference in power when comparing the t-test, Wilcoxon test, and Lachenbruch test to the log-rank test. The log-rank test is less powerful if the absolute power of the log-rank test is negative. Figure S4. Power and type I error comparisons: Results are based on simulations with N = 40. The upper panels demonstrate the power of the log-rank test with different sample size allocations. Triangles represent simulations with $$\frac{{n_{1} }}{{n_{0} }}$$ n 1 n 0 = 1 and circles represent simulations with $$\frac{{n_{1} }}{{n_{0} }}$$ n 1 n 0 = 3. The lower panels demonstrate the absolute difference in power when comparing the t-test, Wilcoxon test, and Lachenbruch test to the log-rank test. The log-rank test is less powerful if the absolute power of the log-rank test is negative. Figure S5. Power of the t-test for different sample size allocations: Results are based on simulations with ρ = 0.2. The t-test is most powerful when $$\frac{{n_{1} }}{{n_{0} }}$$ n 1 n 0 = 3. The square root of the ratio of variance between the intervention and control groups in the simulated data ranges from 4.2 to 4.6 for β ∈ [0.2,1]. Figure S6. Power and type I error comparisons of asymptotic-based and permutation-based tests. Results are shown for different sample size allocations and for ρ = 0 and ρ = 0.2. The total sample size is fixed at 28. Figure S7. Power and type I error comparisons. The results are based on simulations with n0 = n1 = 8, p0 = 1 for SHD, p0 = 0.5 and cmax = 5 for RLD. With relative risk being 1, the points represent the type I error. Figure S8. Power and type I error comparisons. The results are based on simulations with n0 = n1 = 20, p0 = 1 for SHD, p0 = 0.5 and cmax = 5 for RLD. With relative risk being 1, the points represent the type I error.