Coefficients for first line treatments relative to gefitinib estimated using a Bayesian multi-state network meta-analyis (NMA).
mstate_nma_coef
A data.table with the following columns:
Line of treatment. Either first or second.
The statistical model.
Name of the treatment.
The health state transition.
Coefficient as outlined mathematically in the PDF model documentation.
2.5th percentile of the posterior distribution.
25th percentile of the posterior distribution.
50th percentile of the posterior distribution.
75th percentile of the posterior distribution.
90th percentile of the posterior distribution.
print(mstate_nma_coef)#> line model transition coef tx_name q0.025 #> 1: 1 Fractional polynomial (0, 0) S to P d_1 afatinib -0.5222232 #> 2: 1 Fractional polynomial (0, 0) S to P d_1 dacomitinib -1.0802027 #> 3: 1 Fractional polynomial (0, 0) S to P d_1 erlotinib -1.4339206 #> 4: 1 Fractional polynomial (0, 0) S to P d_1 gefitinib 0.0000000 #> 5: 1 Fractional polynomial (0, 0) S to P d_1 osimertinib -2.1360725 #> 6: 1 Fractional polynomial (0, 0) S to P d_2 afatinib -0.4928029 #> 7: 1 Fractional polynomial (0, 0) S to P d_2 dacomitinib -0.6898635 #> 8: 1 Fractional polynomial (0, 0) S to P d_2 erlotinib -0.8360265 #> 9: 1 Fractional polynomial (0, 0) S to P d_2 gefitinib 0.0000000 #> 10: 1 Fractional polynomial (0, 0) S to P d_2 osimertinib -0.6225605 #> 11: 1 Fractional polynomial (0, 1) S to P d_1 afatinib -0.5049607 #> 12: 1 Fractional polynomial (0, 1) S to P d_1 dacomitinib -1.1508858 #> 13: 1 Fractional polynomial (0, 1) S to P d_1 erlotinib -1.3594741 #> 14: 1 Fractional polynomial (0, 1) S to P d_1 gefitinib 0.0000000 #> 15: 1 Fractional polynomial (0, 1) S to P d_1 osimertinib -2.2770433 #> 16: 1 Fractional polynomial (0, 1) S to P d_2 afatinib -0.5181481 #> 17: 1 Fractional polynomial (0, 1) S to P d_2 dacomitinib -0.7362069 #> 18: 1 Fractional polynomial (0, 1) S to P d_2 erlotinib -1.0035783 #> 19: 1 Fractional polynomial (0, 1) S to P d_2 gefitinib 0.0000000 #> 20: 1 Fractional polynomial (0, 1) S to P d_2 osimertinib -0.5118987 #> 21: 1 Gompertz S to P d_1 afatinib -0.3720864 #> 22: 1 Gompertz S to P d_1 dacomitinib -0.9358040 #> 23: 1 Gompertz S to P d_1 erlotinib -1.0522126 #> 24: 1 Gompertz S to P d_1 gefitinib 0.0000000 #> 25: 1 Gompertz S to P d_1 osimertinib -2.0709792 #> 26: 1 Gompertz S to P d_2 afatinib -0.2447781 #> 27: 1 Gompertz S to P d_2 dacomitinib -0.2024955 #> 28: 1 Gompertz S to P d_2 erlotinib -0.1859039 #> 29: 1 Gompertz S to P d_2 gefitinib 0.0000000 #> 30: 1 Gompertz S to P d_2 osimertinib -0.2287914 #> 31: 1 Weibull S to P d_1 afatinib -0.4333372 #> 32: 1 Weibull S to P d_1 dacomitinib -1.0579476 #> 33: 1 Weibull S to P d_1 erlotinib -1.2720244 #> 34: 1 Weibull S to P d_1 gefitinib 0.0000000 #> 35: 1 Weibull S to P d_1 osimertinib -2.0923241 #> 36: 1 Weibull S to P d_2 afatinib -0.5659022 #> 37: 1 Weibull S to P d_2 dacomitinib -0.6699134 #> 38: 1 Weibull S to P d_2 erlotinib -0.7930788 #> 39: 1 Weibull S to P d_2 gefitinib 0.0000000 #> 40: 1 Weibull S to P d_2 osimertinib -0.4752217 #> line model transition coef tx_name q0.025 #> q0.25 q0.5 q0.75 q0.975 #> 1: -0.18486168 -0.022254209 0.1626908298 0.505715300 #> 2: -0.55807091 -0.294749160 -0.0620099235 0.397573038 #> 3: -0.74311358 -0.434403053 -0.1088907743 0.475285028 #> 4: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 5: -1.47329837 -1.158583458 -0.8145921893 0.030488245 #> 6: -0.28146915 -0.160351149 -0.0461988984 0.174264866 #> 7: -0.38987500 -0.247000258 -0.1044792964 0.241431360 #> 8: -0.42867093 -0.220098696 -0.0161500247 0.362148229 #> 9: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 10: -0.17660589 0.007776439 0.1843727054 0.521386910 #> 11: -0.16901847 0.001320278 0.1746986267 0.545149000 #> 12: -0.57290413 -0.281550453 -0.0229286006 0.489830944 #> 13: -0.74040364 -0.419474573 -0.0946323638 0.526438016 #> 14: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 15: -1.52329099 -1.166966020 -0.8291139838 -0.231623056 #> 16: -0.28159855 -0.171161951 -0.0577838164 0.151018291 #> 17: -0.37795493 -0.212807157 -0.0461493966 0.329024052 #> 18: -0.48885776 -0.269956000 -0.0654400761 0.331438987 #> 19: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 20: -0.16696108 0.010941294 0.2005025240 0.571847960 #> 21: -0.08635937 0.067123511 0.2363460852 0.565473480 #> 22: -0.49816437 -0.280306299 -0.0821071407 0.255606898 #> 23: -0.52401708 -0.274967203 -0.0409269536 0.393343009 #> 24: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 25: -1.35710056 -1.042097870 -0.7428131691 -0.221050413 #> 26: -0.14913472 -0.087946651 -0.0431639268 0.005289728 #> 27: -0.09459079 -0.066789100 -0.0438709299 0.004382719 #> 28: -0.09188598 -0.049622237 -0.0099898444 0.070651033 #> 29: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 30: -0.07635877 -0.038500005 -0.0060091030 0.063423221 #> 31: -0.11164097 0.066885900 0.2496819815 0.639791061 #> 32: -0.54325476 -0.307991096 -0.0815316826 0.403269129 #> 33: -0.68039129 -0.383338132 -0.0668855107 0.466211388 #> 34: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 35: -1.42637027 -1.133525747 -0.8448649850 -0.318952050 #> 36: -0.34774809 -0.233468253 -0.1236296394 0.094526200 #> 37: -0.37494297 -0.230697638 -0.0912025104 0.159065458 #> 38: -0.39020521 -0.192678770 -0.0006429364 0.370885827 #> 39: 0.00000000 0.000000000 0.0000000000 0.000000000 #> 40: -0.14824135 -0.003885926 0.1527455956 0.489842914 #> q0.25 q0.5 q0.75 q0.975