PostP.design: The stopping boundaries based on the posterior probability...
In ph2bye: Phase II Clinical Trial Design Using Bayesian Methods
Description
Usage
Arguments
Value
References
Examples
Description
The design function to sequentially monitor sample size and boundary based on Thall and Simon's criterion.
Usage
1
PostP.design(type, nmax, a, b, p0, theta, optimize)
Arguments
type
type of boundaries: "efficacy" or "futility".
nmax
the maximum number of patients treated by the experimental drug.
a
the hyperparameter (shape1) of the Beta prior for the experimental drug.
b
the hyperparameter (shape2) of the Beta prior for the experimental drug.
p0
the pre-specified reseponse rate.
theta
the cutoff probability: typically, θ = [0.9, 0.99] for efficacy, θ = [0.01, 0.1] for futility.
optimize
logical value, if optimize=TRUE, then only output the minimal sample size for the same number of futility and efficacy boundaries.
Value
boundset
the boundaries set: U_n or L_n
References
Thall, P. F., Simon, R. (1994).
Practical Bayesian guidelines for phase IIB clinical trials.
Biometrics 50: 337-349.
Yin, G. (2012).
Clinical Trial Design: Bayesian and Frequentist Adaptive Methods.
New York: Wiley.
Examples
1
2
3
4
5
6
## Using vague prior Unif(0,1)
PostP.design(type = "futility", nmax=100, a=1, b=1, p0=0.3, theta=0.05)
PostP.design(type = "efficacy", nmax=100, a=1, b=1, p0=0.3, theta=0.9)
## Or using Jeffery prior with Beta(0.5,0.5)
PostP.design(type = "futility", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.05)
PostP.design(type = "efficacy", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.9)
Example output
Loading required package: animation
Loading required package: nleqslv
n bound
1 1 NA
2 2 NA
3 3 NA
4 4 NA
5 5 NA
6 6 NA
7 7 NA
8 8 0
9 9 0
10 10 0
11 11 0
12 12 0
13 13 1
14 14 1
15 15 1
16 16 1
17 17 1
18 18 2
19 19 2
20 20 2
21 21 2
22 22 2
23 23 3
24 24 3
25 25 3
26 26 3
27 27 4
28 28 4
29 29 4
30 30 4
31 31 4
32 32 5
33 33 5
34 34 5
35 35 5
36 36 6
37 37 6
38 38 6
39 39 6
40 40 7
41 41 7
42 42 7
43 43 7
44 44 8
45 45 8
46 46 8
47 47 8
48 48 9
49 49 9
50 50 9
51 51 9
52 52 10
53 53 10
54 54 10
55 55 10
56 56 11
57 57 11
58 58 11
59 59 11
60 60 12
61 61 12
62 62 12
63 63 12
64 64 13
65 65 13
66 66 13
67 67 13
68 68 14
69 69 14
70 70 14
71 71 14
72 72 15
73 73 15
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76 76 16
77 77 16
78 78 16
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89 89 19
90 90 19
91 91 19
92 92 20
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95 95 21
96 96 21
97 97 21
98 98 21
99 99 22
100 100 22
n bound
1 1 1
2 2 2
3 3 2
4 4 3
5 5 3
6 6 4
7 7 4
8 8 4
9 9 5
10 10 5
11 11 6
12 12 6
13 13 6
14 14 7
15 15 7
16 16 8
17 17 8
18 18 8
19 19 9
20 20 9
21 21 9
22 22 10
23 23 10
24 24 10
25 25 11
26 26 11
27 27 12
28 28 12
29 29 12
30 30 13
31 31 13
32 32 13
33 33 14
34 34 14
35 35 14
36 36 15
37 37 15
38 38 15
39 39 16
40 40 16
41 41 16
42 42 17
43 43 17
44 44 17
45 45 18
46 46 18
47 47 19
48 48 19
49 49 19
50 50 20
51 51 20
52 52 20
53 53 21
54 54 21
55 55 21
56 56 22
57 57 22
58 58 22
59 59 23
60 60 23
61 61 23
62 62 24
63 63 24
64 64 24
65 65 25
66 66 25
67 67 25
68 68 26
69 69 26
70 70 26
71 71 27
72 72 27
73 73 27
74 74 28
75 75 28
76 76 28
77 77 29
78 78 29
79 79 29
80 80 30
81 81 30
82 82 30
83 83 31
84 84 31
85 85 31
86 86 32
87 87 32
88 88 32
89 89 33
90 90 33
91 91 33
92 92 34
93 93 34
94 94 34
95 95 35
96 96 35
97 97 35
98 98 36
99 99 36
100 100 36
n bound
1 1 NA
2 2 NA
3 3 NA
4 4 NA
5 5 NA
6 6 0
7 7 0
8 8 0
9 9 0
10 10 0
11 11 0
12 12 1
13 13 1
14 14 1
15 15 1
16 16 1
17 17 2
18 18 2
19 19 2
20 20 2
21 21 2
22 22 3
23 23 3
24 24 3
25 25 3
26 26 4
27 27 4
28 28 4
29 29 4
30 30 5
31 31 5
32 32 5
33 33 5
34 34 5
35 35 6
36 36 6
37 37 6
38 38 6
39 39 7
40 40 7
41 41 7
42 42 7
43 43 8
44 44 8
45 45 8
46 46 8
47 47 9
48 48 9
49 49 9
50 50 9
51 51 10
52 52 10
53 53 10
54 54 10
55 55 11
56 56 11
57 57 11
58 58 11
59 59 12
60 60 12
61 61 12
62 62 12
63 63 13
64 64 13
65 65 13
66 66 13
67 67 14
68 68 14
69 69 14
70 70 14
71 71 15
72 72 15
73 73 15
74 74 15
75 75 16
76 76 16
77 77 16
78 78 16
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80 80 17
81 81 17
82 82 17
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84 84 18
85 85 18
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87 87 19
88 88 19
89 89 19
90 90 19
91 91 20
92 92 20
93 93 20
94 94 21
95 95 21
96 96 21
97 97 21
98 98 22
99 99 22
100 100 22
n bound
1 1 1
2 2 2
3 3 2
4 4 3
5 5 3
6 6 4
7 7 4
8 8 5
9 9 5
10 10 5
11 11 6
12 12 6
13 13 7
14 14 7
15 15 7
16 16 8
17 17 8
18 18 8
19 19 9
20 20 9
21 21 10
22 22 10
23 23 10
24 24 11
25 25 11
26 26 11
27 27 12
28 28 12
29 29 12
30 30 13
31 31 13
32 32 13
33 33 14
34 34 14
35 35 15
36 36 15
37 37 15
38 38 16
39 39 16
40 40 16
41 41 17
42 42 17
43 43 17
44 44 18
45 45 18
46 46 18
47 47 19
48 48 19
49 49 19
50 50 20
51 51 20
52 52 20
53 53 21
54 54 21
55 55 21
56 56 22
57 57 22
58 58 22
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61 61 23
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83 83 31
84 84 31
85 85 31
86 86 32
87 87 32
88 88 32
89 89 33
90 90 33
91 91 33
92 92 34
93 93 34
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95 95 35
96 96 35
97 97 35
98 98 36
99 99 36
100 100 36
ph2bye documentation built on May 1, 2019, 6:33 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value References Examples
Description
The design function to sequentially monitor sample size and boundary based on Thall and Simon's criterion.
Usage
1 | PostP.design(type, nmax, a, b, p0, theta, optimize)
|
Arguments
type |
type of boundaries: "efficacy" or "futility". |
nmax |
the maximum number of patients treated by the experimental drug. |
a |
the hyperparameter (shape1) of the Beta prior for the experimental drug. |
b |
the hyperparameter (shape2) of the Beta prior for the experimental drug. |
p0 |
the pre-specified reseponse rate. |
theta |
the cutoff probability: typically, θ = [0.9, 0.99] for efficacy, θ = [0.01, 0.1] for futility. |
optimize |
logical value, if optimize=TRUE, then only output the minimal sample size for the same number of futility and efficacy boundaries. |
Value
boundset |
the boundaries set: U_n or L_n |
References
Thall, P. F., Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics 50: 337-349.
Yin, G. (2012). Clinical Trial Design: Bayesian and Frequentist Adaptive Methods. New York: Wiley.
Examples
1 2 3 4 5 6 | ## Using vague prior Unif(0,1)
PostP.design(type = "futility", nmax=100, a=1, b=1, p0=0.3, theta=0.05)
PostP.design(type = "efficacy", nmax=100, a=1, b=1, p0=0.3, theta=0.9)
## Or using Jeffery prior with Beta(0.5,0.5)
PostP.design(type = "futility", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.05)
PostP.design(type = "efficacy", nmax=100, a=0.5, b=0.5, p0=0.3, theta=0.9)
|
Example output
Loading required package: animation
Loading required package: nleqslv
n bound
1 1 NA
2 2 NA
3 3 NA
4 4 NA
5 5 NA
6 6 NA
7 7 NA
8 8 0
9 9 0
10 10 0
11 11 0
12 12 0
13 13 1
14 14 1
15 15 1
16 16 1
17 17 1
18 18 2
19 19 2
20 20 2
21 21 2
22 22 2
23 23 3
24 24 3
25 25 3
26 26 3
27 27 4
28 28 4
29 29 4
30 30 4
31 31 4
32 32 5
33 33 5
34 34 5
35 35 5
36 36 6
37 37 6
38 38 6
39 39 6
40 40 7
41 41 7
42 42 7
43 43 7
44 44 8
45 45 8
46 46 8
47 47 8
48 48 9
49 49 9
50 50 9
51 51 9
52 52 10
53 53 10
54 54 10
55 55 10
56 56 11
57 57 11
58 58 11
59 59 11
60 60 12
61 61 12
62 62 12
63 63 12
64 64 13
65 65 13
66 66 13
67 67 13
68 68 14
69 69 14
70 70 14
71 71 14
72 72 15
73 73 15
74 74 15
75 75 15
76 76 16
77 77 16
78 78 16
79 79 16
80 80 17
81 81 17
82 82 17
83 83 17
84 84 18
85 85 18
86 86 18
87 87 18
88 88 19
89 89 19
90 90 19
91 91 19
92 92 20
93 93 20
94 94 20
95 95 21
96 96 21
97 97 21
98 98 21
99 99 22
100 100 22
n bound
1 1 1
2 2 2
3 3 2
4 4 3
5 5 3
6 6 4
7 7 4
8 8 4
9 9 5
10 10 5
11 11 6
12 12 6
13 13 6
14 14 7
15 15 7
16 16 8
17 17 8
18 18 8
19 19 9
20 20 9
21 21 9
22 22 10
23 23 10
24 24 10
25 25 11
26 26 11
27 27 12
28 28 12
29 29 12
30 30 13
31 31 13
32 32 13
33 33 14
34 34 14
35 35 14
36 36 15
37 37 15
38 38 15
39 39 16
40 40 16
41 41 16
42 42 17
43 43 17
44 44 17
45 45 18
46 46 18
47 47 19
48 48 19
49 49 19
50 50 20
51 51 20
52 52 20
53 53 21
54 54 21
55 55 21
56 56 22
57 57 22
58 58 22
59 59 23
60 60 23
61 61 23
62 62 24
63 63 24
64 64 24
65 65 25
66 66 25
67 67 25
68 68 26
69 69 26
70 70 26
71 71 27
72 72 27
73 73 27
74 74 28
75 75 28
76 76 28
77 77 29
78 78 29
79 79 29
80 80 30
81 81 30
82 82 30
83 83 31
84 84 31
85 85 31
86 86 32
87 87 32
88 88 32
89 89 33
90 90 33
91 91 33
92 92 34
93 93 34
94 94 34
95 95 35
96 96 35
97 97 35
98 98 36
99 99 36
100 100 36
n bound
1 1 NA
2 2 NA
3 3 NA
4 4 NA
5 5 NA
6 6 0
7 7 0
8 8 0
9 9 0
10 10 0
11 11 0
12 12 1
13 13 1
14 14 1
15 15 1
16 16 1
17 17 2
18 18 2
19 19 2
20 20 2
21 21 2
22 22 3
23 23 3
24 24 3
25 25 3
26 26 4
27 27 4
28 28 4
29 29 4
30 30 5
31 31 5
32 32 5
33 33 5
34 34 5
35 35 6
36 36 6
37 37 6
38 38 6
39 39 7
40 40 7
41 41 7
42 42 7
43 43 8
44 44 8
45 45 8
46 46 8
47 47 9
48 48 9
49 49 9
50 50 9
51 51 10
52 52 10
53 53 10
54 54 10
55 55 11
56 56 11
57 57 11
58 58 11
59 59 12
60 60 12
61 61 12
62 62 12
63 63 13
64 64 13
65 65 13
66 66 13
67 67 14
68 68 14
69 69 14
70 70 14
71 71 15
72 72 15
73 73 15
74 74 15
75 75 16
76 76 16
77 77 16
78 78 16
79 79 17
80 80 17
81 81 17
82 82 17
83 83 18
84 84 18
85 85 18
86 86 18
87 87 19
88 88 19
89 89 19
90 90 19
91 91 20
92 92 20
93 93 20
94 94 21
95 95 21
96 96 21
97 97 21
98 98 22
99 99 22
100 100 22
n bound
1 1 1
2 2 2
3 3 2
4 4 3
5 5 3
6 6 4
7 7 4
8 8 5
9 9 5
10 10 5
11 11 6
12 12 6
13 13 7
14 14 7
15 15 7
16 16 8
17 17 8
18 18 8
19 19 9
20 20 9
21 21 10
22 22 10
23 23 10
24 24 11
25 25 11
26 26 11
27 27 12
28 28 12
29 29 12
30 30 13
31 31 13
32 32 13
33 33 14
34 34 14
35 35 15
36 36 15
37 37 15
38 38 16
39 39 16
40 40 16
41 41 17
42 42 17
43 43 17
44 44 18
45 45 18
46 46 18
47 47 19
48 48 19
49 49 19
50 50 20
51 51 20
52 52 20
53 53 21
54 54 21
55 55 21
56 56 22
57 57 22
58 58 22
59 59 23
60 60 23
61 61 23
62 62 24
63 63 24
64 64 24
65 65 25
66 66 25
67 67 25
68 68 26
69 69 26
70 70 26
71 71 27
72 72 27
73 73 27
74 74 28
75 75 28
76 76 28
77 77 29
78 78 29
79 79 29
80 80 30
81 81 30
82 82 30
83 83 31
84 84 31
85 85 31
86 86 32
87 87 32
88 88 32
89 89 33
90 90 33
91 91 33
92 92 34
93 93 34
94 94 34
95 95 35
96 96 35
97 97 35
98 98 36
99 99 36
100 100 36
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