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Type 'q()' to quit R. > > library(testthat) > library(cbamm) > test_check("cbamm") ======================================== CBAMM v0.9.2 Analysis ======================================== Dataset: 18 studies | by type: RCT=5, OBS=5, MR=8 transport (sum=1): min=0.05556, median=0.05556, max=0.05556 Applying GRADE-based down-weighting... final transportxGRADE (sum=1): min=0.03279, median=0.05738, max=0.08197 === STRATIFIED META-ANALYSIS (HKSJ + PI) === RCT : 0.900 (95% CI 0.759-1.067) | k=5, tau^2=0.0000, I^2=0.0% OBS : 0.713 (95% CI 0.531-0.956) | k=5, tau^2=0.0387, I^2=72.8% MR : 0.789 (95% CI 0.634-0.983) | k=8, tau^2=0.0199, I^2=39.0% === ALL-STUDIES POOLED (Transport & GRADE) === Transport only : 0.796 (95% CI 0.708-0.894) | k=18, tau^2=0.0248, I^2=58.3% Transport + GRADE : 0.795 (95% CI 0.709-0.892) | k=18, tau^2=0.0248, I^2=58.3% [RVE-CR2] Skipped (clubSandwich not available) === ADAPTIVE ADVISOR (concise) === k=18, I^2=58.3%, Egger p=0.279 === MULTIVERSE (tau^2 estimator x subset x weighting) === | | | 0% | |== | 3% | |==== | 6% | |====== | 8% | |======== | 11% | |========== | 14% | |============ | 17% | |============== | 19% | |================ | 22% | |================== | 25% | |=================== | 28% | |===================== | 31% | |======================= | 33% | |========================= | 36% | |=========================== | 39% | |============================= | 42% | |=============================== | 44% | |================================= | 47% | |=================================== | 50% | |===================================== | 53% | |======================================= | 56% | |========================================= | 58% | |=========================================== | 61% | |============================================= | 64% | |=============================================== | 67% | |================================================= | 69% | |=================================================== | 72% | |==================================================== | 75% | |====================================================== | 78% | |======================================================== | 81% | |========================================================== | 83% | |============================================================ | 86% | |============================================================== | 89% | |================================================================ | 92% | |================================================================== | 94% | |==================================================================== | 97% | |======================================================================| 100%Successful specs: 36/36 | Range: [0.795, 0.900] | eff 1 1 3 -0.604 0.547 2017. 0 0.667 0.333 2 2 5 -0.332 0.717 2018. 0.2 0.2 0.6 3 3 9 -0.0907 0.913 2019. 0.444 0.222 0.333 4 4 1 0.255 1.29 2025 0 0 1 === MISSING-STUDY SENSITIVITY (augmentation grid) === | | | 0% | |=== | 4% | |====== | 8% | |======== | 12% | |=========== | 16% | |============== | 20% | |================= | 24% | |==================== | 28% | |====================== | 32% | |========================= | 36% | |============================ | 40% | |=============================== | 44% | |================================== | 48% | |==================================== | 52% | |======================================= | 56% | |========================================== | 60% | |============================================= | 64% | |================================================ | 68% | |================================================== | 72% | |===================================================== | 76% | |======================================================== | 80% | |=========================================================== | 84% | |============================================================== | 88% | |================================================================ | 92% | |=================================================================== | 96% | |======================================================================| 100%# A tibble: 25 x 4 n_missing hr pooled_hr prob_benefit 1 0 0.8 0.795 1.000 2 0 0.9 0.795 1.000 3 0 1 0.795 1.000 4 0 1.1 0.795 1.000 5 0 1.2 0.795 1.000 6 1 0.8 0.795 1.000 7 1 0.9 0.800 1.000 8 1 1 0.805 1.000 9 1 1.1 0.809 0.999 10 1 1.2 0.813 0.999 # i 15 more rows === SAFEGUARD META (one-sided trimming of extremes) === | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100%# A tibble: 7 x 4 trim hr ci_lb ci_ub 1 0 0.795 0.709 0.892 2 1 0.814 0.730 0.908 3 2 0.842 0.767 0.924 4 3 0.865 0.795 0.942 5 4 0.874 0.802 0.952 6 5 0.887 0.814 0.967 7 6 0.914 0.842 0.993 Safeguard result: minimal trims to lose benefit (HR<1) = > max (out of k=18) === BAYESIAN ANALYSIS === Skipped (disabled) Generating combined plot layout... === TOST (Equivalence & NI) on pooled fit === $equivalence $equivalence$p_value [1] 0.537896 $equivalence$t1 [1] -0.09655624 $equivalence$t2 [1] 8.191918 $equivalence$df [1] 17 $equivalence$margins [1] 0.80 1.25 $non_inferiority $non_inferiority$p_value [1] 0.0003393069 $non_inferiority$t [1] 4.144237 $non_inferiority$df [1] 17 $non_inferiority$margin [1] 1 ======================================== CBAMM Analysis Complete ======================================== [ FAIL 0 | WARN 3 | SKIP 0 | PASS 7 ] [ FAIL 0 | WARN 3 | SKIP 0 | PASS 7 ] > > > proc.time() user system elapsed 8.53 0.54 9.07