Average operating characteristics with respect to historic target
avg_oc_wr_ph.RdFunction for calculating the average operating characteristics of a single arm Bayesian designs for early gating with respect to the historic target.
avg_oc_wr_ph( N_e, delta, delta_power, confidence, e_a = 0.5, e_b = 0.5, alpha_c, beta_c, trues = seq(0, 1, 0.01), adapt = 1, plot = T, legend = T, legend.pos = "topleft" )
Arguments
| N_e | Sample Size in the experimental group. Can be either a single value or a vector. |
|---|---|
| delta | Required superiority to make a "GO" decision. Corresponds to \(\delta\). |
| delta_power | Superiority, at which decision power will be evaluated. Corresponds to \(\bar{\delta}\). |
| confidence | Required confidence to make "GO" decision. Corresponds to \(\gamma\). |
| e_a | Alpha parameter of Beta Prior Distribution for the experimental response rate. Corresponds to \(\alpha_e\). Default is \(\frac{1}{2}\). |
| e_b | Beta parameter of Beta Prior Distribution for the experimental response rate. Corresponds to \(\beta_e\). Default is \(\frac{1}{2}\). |
| alpha_c | Alpha parameter of Beta Distribution for the control response rate used to calculate average operating characteristics. Corresponds to \(\alpha_c\). |
| beta_c | Beta parameter of Beta Distribution for the control response rate used to calculate average operating characteristics. Corresponds to \(\beta_c\). |
| trues | Sequence of true control response rates and experimental response rates, at which the Probability to Go will be computed. Default is seq(0,1,0.01) to ensure continuous plots and accurate results. |
| adapt | Level of adapting of experimental control rate to account for patient selection bias from phase II to phase III. Corresponds to \(\xi\). Default is 1, so no adapting. |
| plot | Plots yes or no. Default is TRUE. |
| legend | Logical; whether or not to include legend in plot. Default is TRUE. |
| legend.pos | Position of legend. Default is "topleft". |
Value
A matrix containing information about the decision power and the decision alpha with respect to p_h.
Examples
avg_oc_wr_ph( N_e = 50, delta = 0.08, delta_power = 0.13, confidence = 0.6, alpha_c = 15, beta_c = 13 )#> Dec.Alpha Dec.Power p_h #> [1,] 9.900907e-01 0.9900927135 0.00 #> [2,] 9.900907e-01 0.9900927135 0.01 #> [3,] 9.900721e-01 0.9900926731 0.02 #> [4,] 9.900721e-01 0.9900926731 0.03 #> [5,] 9.900232e-01 0.9900925132 0.04 #> [6,] 9.900232e-01 0.9900925132 0.05 #> [7,] 9.899080e-01 0.9900919618 0.06 #> [8,] 9.896603e-01 0.9900902683 0.07 #> [9,] 9.896603e-01 0.9900902683 0.08 #> [10,] 9.891679e-01 0.9900855626 0.09 #> [11,] 9.891679e-01 0.9900855626 0.10 #> [12,] 9.882538e-01 0.9900735851 0.11 #> [13,] 9.882538e-01 0.9900735851 0.12 #> [14,] 9.866556e-01 0.9900453772 0.13 #> [15,] 9.866556e-01 0.9900453772 0.14 #> [16,] 9.840076e-01 0.9899834073 0.15 #> [17,] 9.840076e-01 0.9899834073 0.16 #> [18,] 9.798279e-01 0.9898555434 0.17 #> [19,] 9.798279e-01 0.9898555434 0.18 #> [20,] 9.735157e-01 0.9896063396 0.19 #> [21,] 9.735157e-01 0.9896063396 0.20 #> [22,] 9.643630e-01 0.9891453347 0.21 #> [23,] 9.643630e-01 0.9891453347 0.22 #> [24,] 9.515833e-01 0.9883325069 0.23 #> [25,] 9.515833e-01 0.9883325069 0.24 #> [26,] 9.343585e-01 0.9869616906 0.25 #> [27,] 9.343585e-01 0.9869616906 0.26 #> [28,] 9.119025e-01 0.9847435944 0.27 #> [29,] 9.119025e-01 0.9847435944 0.28 #> [30,] 8.835369e-01 0.9812909261 0.29 #> [31,] 8.835369e-01 0.9812909261 0.30 #> [32,] 8.487727e-01 0.9761088846 0.31 #> [33,] 8.487727e-01 0.9761088846 0.32 #> [34,] 8.073866e-01 0.9685946917 0.33 #> [35,] 8.073866e-01 0.9685946917 0.34 #> [36,] 7.594853e-01 0.9580497281 0.35 #> [37,] 7.594853e-01 0.9580497281 0.36 #> [38,] 7.055442e-01 0.9437070558 0.37 #> [39,] 7.055442e-01 0.9437070558 0.38 #> [40,] 6.464165e-01 0.9247756072 0.39 #> [41,] 6.464165e-01 0.9247756072 0.40 #> [42,] 5.833054e-01 0.9005001809 0.41 #> [43,] 5.833054e-01 0.9005001809 0.42 #> [44,] 5.177014e-01 0.8702338351 0.43 #> [45,] 5.177014e-01 0.8702338351 0.44 #> [46,] 4.512874e-01 0.8335166545 0.45 #> [47,] 4.512874e-01 0.8335166545 0.46 #> [48,] 3.858223e-01 0.7901526316 0.47 #> [49,] 3.858223e-01 0.7901526316 0.48 #> [50,] 3.230129e-01 0.7402749903 0.49 #> [51,] 3.230129e-01 0.7402749903 0.50 #> [52,] 2.643908e-01 0.6843900749 0.51 #> [53,] 2.643908e-01 0.6843900749 0.52 #> [54,] 2.112054e-01 0.6233911804 0.53 #> [55,] 2.112054e-01 0.6233911804 0.54 #> [56,] 1.643465e-01 0.5585364150 0.55 #> [57,] 1.643465e-01 0.5585364150 0.56 #> [58,] 1.243035e-01 0.4913886667 0.57 #> [59,] 1.243035e-01 0.4913886667 0.58 #> [60,] 9.116442e-02 0.4237205058 0.59 #> [61,] 9.116442e-02 0.4237205058 0.60 #> [62,] 6.465316e-02 0.3573917534 0.61 #> [63,] 6.465316e-02 0.3573917534 0.62 #> [64,] 4.419695e-02 0.2942117244 0.63 #> [65,] 4.419695e-02 0.2942117244 0.64 #> [66,] 2.901427e-02 0.2358010726 0.65 #> [67,] 2.901427e-02 0.2358010726 0.66 #> [68,] 1.821059e-02 0.1834691464 0.67 #> [69,] 1.821059e-02 0.1834691464 0.68 #> [70,] 1.086963e-02 0.1381214937 0.69 #> [71,] 1.086963e-02 0.1381214937 0.70 #> [72,] 6.129924e-03 0.1002086608 0.71 #> [73,] 6.129924e-03 0.1002086608 0.72 #> [74,] 3.239859e-03 0.0697221349 0.73 #> [75,] 3.239859e-03 0.0697221349 0.74 #> [76,] 1.588393e-03 0.0462369303 0.75 #> [77,] 1.588393e-03 0.0462369303 0.76 #> [78,] 7.127325e-04 0.0289938942 0.77 #> [79,] 7.127325e-04 0.0289938942 0.78 #> [80,] 2.874714e-04 0.0170093781 0.79 #> [81,] 2.874714e-04 0.0170093781 0.80 #> [82,] 1.016165e-04 0.0091964146 0.81 #> [83,] 1.016165e-04 0.0091964146 0.82 #> [84,] 3.031672e-05 0.0044806104 0.83 #> [85,] 3.031672e-05 0.0044806104 0.84 #> [86,] 7.182208e-06 0.0018958133 0.85 #> [87,] 7.182208e-06 0.0018958133 0.86 #> [88,] 1.205791e-06 0.0006489251 0.87 #> [89,] 1.205791e-06 0.0006489251 0.88 #> [90,] 1.205791e-06 0.0006489251 0.89 #> [91,] 1.080821e-07 0.0001495436 0.90 #> [92,] 1.080821e-07 0.0001495436 0.91 #> [93,] NA NA 0.92 #> [94,] NA NA 0.93 #> [95,] NA NA 0.94 #> [96,] NA NA 0.95 #> [97,] NA NA 0.96 #> [98,] NA NA 0.97 #> [99,] NA NA 0.98 #> [100,] NA NA 0.99 #> [101,] NA NA 1.00
