I appreciate if anyone can help with how to make a graph showing reliable and clinically significant change in particular variable, something like panel A or B of figure 2 below from a paper. I searched every Stata forum questions and Stata materials related to this plot, including a book on A visual Guide to Stata Graphics (Third Edition) by Michael N. Mitchell, yet nothing matched my question.
Array
I also have quite similar data to the above study, with the following variables as pasted below:
- ID is participant ID, with 99 participants (1-99).
- GAD_0 is depression score at baseline (0-21)
- GAD_8 is depression score at week 8 (post-intervention time) after clinical intervention (0-20)
- Dif_GAD_0_8 is difference score between pre- and post-intervention (-7-20)
- RCCriterion_0_8=4.45312 is the cutoff for assessing the reliable change or reliable improvement. This parameter was estimated based on the formula: 1.96*SEdiff = 1.96*SD* sqrt(2-r1-r2)where SEdiff = SE of the difference; SD = SD of test 1; r1 and r2 are reliability of tests 1 and 2. So, if Dif_GAD_0_8 > 1.96*SEdiff or >4.45312, then we can identify him/her as reliable improvers.
- Actual_Dif_GAD_0_8 is a dichotomous variable classifying a participant as reliable changer or not based on the above criteria.
- A clinical cutoff for GAD defined from literature is a score of 13.
So, with the above data, how can I make a plot of figure 2 (like Panel A) as above, if possible, can you suggest with Stata codes based on Stata data below.
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float ID double(GAD_0 GAD_8) float(Dif_GAD_0_8 RCCriterion_GAD_0_8 Actual_Dif_GAD_0_8)
1 20 4 16 4.45312 1
2 4 7 -3 4.45312 0
3 9 4 5 4.45312 1
4 7 6 1 4.45312 0
5 17 6 11 4.45312 1
6 18 7 11 4.45312 1
7 7 4 3 4.45312 0
8 13 5 8 4.45312 1
9 12 4 8 4.45312 1
10 18 6 12 4.45312 1
11 7 1 6 4.45312 1
12 14 0 14 4.45312 1
13 12 4 8 4.45312 1
14 17 5 12 4.45312 1
15 12 7 5 4.45312 1
16 21 10 11 4.45312 1
17 3 2 1 4.45312 0
18 4 5 -1 4.45312 0
19 7 5 2 4.45312 0
20 17 4 13 4.45312 1
21 5 4 1 4.45312 0
22 14 5 9 4.45312 1
23 0 3 -3 4.45312 0
24 8 10 -2 4.45312 0
25 12 3 9 4.45312 1
26 11 9 2 4.45312 0
27 15 3 12 4.45312 1
28 21 14 7 4.45312 1
29 16 4 12 4.45312 1
30 4 2 2 4.45312 0
31 8 4 4 4.45312 0
32 7 5 2 4.45312 0
33 10 6 4 4.45312 0
34 16 10 6 4.45312 1
35 12 3 9 4.45312 1
36 14 6 8 4.45312 1
37 4 4 0 4.45312 0
38 6 5 1 4.45312 0
39 20 8 12 4.45312 1
40 15 4 11 4.45312 1
41 10 5 5 4.45312 1
42 16 4 12 4.45312 1
43 4 1 3 4.45312 0
44 19 5 14 4.45312 1
45 9 2 7 4.45312 1
46 6 4 2 4.45312 0
47 12 7 5 4.45312 1
48 14 5 9 4.45312 1
49 7 7 0 4.45312 0
50 16 7 9 4.45312 1
51 9 3 6 4.45312 1
52 1 3 -2 4.45312 0
53 7 4 3 4.45312 0
54 20 8 12 4.45312 1
55 15 4 11 4.45312 1
56 8 6 2 4.45312 0
57 13 6 7 4.45312 1
58 18 7 11 4.45312 1
59 12 4 8 4.45312 1
60 16 6 10 4.45312 1
61 5 4 1 4.45312 0
62 20 0 20 4.45312 1
63 7 5 2 4.45312 0
64 18 6 12 4.45312 1
65 16 12 4 4.45312 0
66 19 0 19 4.45312 1
67 15 8 7 4.45312 1
68 7 4 3 4.45312 0
69 20 7 13 4.45312 1
70 10 3 7 4.45312 1
71 17 20 -3 4.45312 0
72 7 9 -2 4.45312 0
73 14 6 8 4.45312 1
74 11 7 4 4.45312 0
75 9 2 7 4.45312 1
76 7 11 -4 4.45312 0
77 21 9 12 4.45312 1
78 3 3 0 4.45312 0
79 14 9 5 4.45312 1
80 19 0 19 4.45312 1
81 16 13 3 4.45312 0
82 9 16 -7 4.45312 0
83 8 3 5 4.45312 1
84 6 4 2 4.45312 0
85 12 8 4 4.45312 0
86 6 5 1 4.45312 0
87 18 4 14 4.45312 1
88 21 9 12 4.45312 1
89 7 5 2 4.45312 0
90 5 4 1 4.45312 0
91 7 5 2 4.45312 0
92 4 5 -1 4.45312 0
93 20 19 1 4.45312 0
94 1 8 -7 4.45312 0
95 21 6 15 4.45312 1
96 13 8 5 4.45312 1
97 3 4 -1 4.45312 0
98 11 5 6 4.45312 1
99 12 5 7 4.45312 1
end
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