-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path.Rhistory
171 lines (171 loc) · 11.9 KB
/
.Rhistory
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(psych)
library(moonBook)
library(magrittr)
library(ztable)
library(sjlabelled)
library(ggpubr)
library(gt)
library(ztable)
library(sjlabelled)
library(knitr)
library(kableExtra)
library(papeR)
library(EMT)
library(rcompanion)
library(psych)
library(vcd)
library(DescTools)
library(ggpubr)
library(lme4)
library(lmerTest)
library(broom.mixed)
library(pander)
library(multcomp)
library(lubridate)
library(rstatix)
library(EMAtools)
root="/media/egarza/Elements2/projects/ADDIMEX_TMS/FC_CLIN/4-Dissemination/2-Publications/OpenScience/addimex_tms/data/clinical/" # modify the root path.
# Import datasets
data2w <- read_csv(paste(root,"data2w.csv", sep=""))
root="/media/egarza/Elements2/projects/ADDIMEX_TMS/FC_CLIN/4-Dissemination/2-Publications/OpenScience/addimex_tms/data/" # modify the root path.
# Import datasets
data2w <- read_csv(paste(root,"data2w.csv", sep=""))
root="/media/egarza/Elements2/projects/ADDIMEX_TMS/FC_CLIN/4-Dissemination/2-Publications/OpenScience/sudmex_tms_main/data/clinical/" # modify the root path.
# Import datasets
data2w <- read_csv(paste(root,"data2w.csv", sep=""))
data6m <- read_csv(paste(root,"data6m.csv", sep=""))
data6m_conn <- read_csv(paste(root,"data6m_conn.csv", sep=""))
dataT0_gen <- read_csv(paste(root, "dataT0_gen.csv", sep =""))
groupfactor <- function(data) {
factor(data$group,levels = c("SHAM","ACTIVE"), labels = c("SHAM", "ACTIVE"))
}
stagefactor <- function(data) {
factor(data$stage, levels = c("T0","T1","T1-4","T2","T3","T4","T5"))
}
dataT0_gen$group <- groupfactor(dataT0_gen)
dataT0_gen$q1_sex <- factor(dataT0_gen$q1_sex, levels = c("M","F"), labels = c("M","F"))
dataT0_gen$q5_civ <- factor(dataT0_gen$q5_civ, levels = c("single", "married", "divorced", "separated", "widowed"), labels = c("single", "married", "divorced", "separated", "widowed"))
dataT0_gen$q6_employeedays <- factor(dataT0_gen$q6_employeedays, levels = c("full time", "half time", "free lance", "scholarized","not scholarized", "reteired", "housewife", "unemployee"), labels = c("full time", "half time", "free lance", "scholarized","not scholarized", "reteired", "housewife", "unemployee"))
dataT0_gen$q6_employeeyr <- factor(dataT0_gen$q6_employeeyr, levels = c("full time", "half time", "free lance", "scholarized","not scholarized", "reteired", "housewife", "unemployee"), labels = c("full time", "half time", "free lance", "scholarized","not scholarized", "reteired", "housewife", "unemployee"))
dataT0_gen$q6_sustance <- factor(dataT0_gen$q6_sustance, levels = c("crack-cocaine","cocaine"), labels = c("crack-cocaine","cocaine"))
dataT0_gen$score_clasif <- factor(dataT0_gen$score_clasif, levels = c("AB","C+","C", "C-", "D+", "D", "E"))
data2w$group <- groupfactor(data2w)
data2w$stage <- stagefactor(data2w)
data2w$sex <- factor(data2w$sex, levels = c("M","F"), labels = c("M","F"))
data2w$ut_coc <- factor(data2w$ut_coc, levels = c("positive","negative"), labels = c("positive","negative"))
data2w$hars_categories <- factor(data2w$hars_categories, levels = c("mild severity","mild to moderate severity", "moderate-severe"), labels = c("mild severity","mild to moderate severity", "moderate-severe"))
data2w$hdrs_categories <- factor(data2w$hdrs_categories, levels = c("normal","minor depression","less than major depressive","major depression","more than major depression"), labels = c("normal","minor depression","less than major depressive","major depression","more than major depression"))
data2w$pitt_gqs <- factor(data2w$pitt_gqs, levels = c(1,2), labels = c("Good", "Bad"))
data6m$group <- groupfactor(data6m)
data6m$stage <- stagefactor(data6m)
data6m$sex <- factor(data6m$sex, levels = c("M","F"), labels = c("M","F"))
data6m$ut_coc <- factor(data6m$ut_coc, levels = c("positive","negative"), labels = c("positive","negative"))
data6m$hars_categories <- factor(data6m$hars_categories, levels = c("mild severity","mild to moderate severity", "moderate-severe"), labels = c("mild severity","mild to moderate severity", "moderate-severe"))
data6m$hdrs_categories <- factor(data6m$hdrs_categories, levels = c("normal","minor depression","less than major depressive","major depression","more than major depression"), labels = c("normal","minor depression","less than major depressive","major depression","more than major depression"))
data6m$pitt_gqs <- factor(data6m$pitt_gqs, levels = c("Good", "Bad"), labels = c("Good", "Bad"))
data6m$stage_openlabel <- factor(data6m$stage_openlabel, levels = c("BASELINE", "2W", "3M", "6M"))
data6m_conn$group <- groupfactor(data6m_conn)
data6m_conn$stage <- stagefactor(data6m_conn)
data6m_conn$sex <- factor(data6m_conn$sex, levels = c("M","F"), labels = c("M","F"))
data6m_conn$ut_coc <- factor(data6m_conn$ut_coc, levels = c("positive","negative"), labels = c("positive","negative"))
data6m_conn$hars_categories <- factor(data6m_conn$hars_categories, levels = c("mild severity","mild to moderate severity", "moderate-severe"), labels = c("mild severity","mild to moderate severity", "moderate-severe"))
data6m_conn$hdrs_categories <- factor(data6m_conn$hdrs_categories, levels = c("normal","minor depression","less than major depressive","major depression","more than major depression"), labels = c("normal","minor depression","less than major depressive","major depression","more than major depression"))
data6m_conn$pitt_gqs <- factor(data6m_conn$pitt_gqs, levels = c("Good", "Bad"), labels = c("Good", "Bad"))
data6m_conn$stage_openlabel <- factor(data6m_conn$stage_openlabel, levels = c("BASELINE", "2W", "3M", "6M"))
dataT0_gen$rid <- set_label(dataT0_gen$rid, label = "ID")
dataT0_gen$group <- set_label(dataT0_gen$group, label = "Group")
dataT0_gen$stage <- set_label(dataT0_gen$stage, label = "Timepoint")
dataT0_gen$q1_sex <- set_label(dataT0_gen$q1_sex, label = "Sex")
dataT0_gen$q1_age <- set_label(dataT0_gen$q1_age, label = "Age")
dataT0_gen$q2_edyears <- set_label(dataT0_gen$q2_edyears, label = "Years of education")
dataT0_gen$q5_civ <- set_label(dataT0_gen$q5_civ, label = "Marital status")
dataT0_gen$q6_month <- set_label(dataT0_gen$q6_month, label = "Montly income (MXN)")
dataT0_gen$q6_employeeyr <- set_label(dataT0_gen$q6_employeeyr, label = "Employment (last 3 years)")
dataT0_gen$q7_yrstart <- set_label(dataT0_gen$q7_yrstart, label = "Onset age of cocaine use")
dataT0_gen$q7_tconsume <- set_label(dataT0_gen$q7_tconsume, label = "Years of cocaine use")
dataT0_gen$score_clasif <- set_label(dataT0_gen$score_clasif, label = "Socioeconomic status")
data2w$rid <- set_label(data2w$rid, label = "ID")
data2w$group <- set_label(data2w$group, label = "Group")
data2w$stage <- set_label(data2w$stage, label = "Timepoint")
data2w$sex <- set_label(data2w$sex, label = "Sex")
data2w$age <- set_label(data2w$age, label = "Age")
data2w$educ <- set_label(data2w$educ, label = "Education")
data2w$ccq_g <- set_label(data2w$ccq_g, label = "CCQ-General")
data2w$ccq_n <- set_label(data2w$ccq_n, label = "CCQ-Now")
data2w$vas <- set_label(data2w$vas, label = "VAS")
data2w$ut_coc <- set_label(data2w$ut_coc, label = "Urine Test: Cocaine")
data2w$bis11t <- set_label(data2w$bis11t, label = "BIS-11 Total")
data2w$bis11cog <- set_label(data2w$bis11cog, label = "BIS-11 Cognitive")
data2w$bis11mot <- set_label(data2w$bis11mot, label = "BIS-11 Motor")
data2w$bis11nonp <- set_label(data2w$bis11nonp, label = "BIS-11 Non-planned")
data2w$hars_tot <- set_label(data2w$hars_tot, label = "HDRS score")
data2w$hars_categories <- set_label(data2w$hars_categories, label = "HDRS categories")
data2w$hdrs_tot <- set_label(data2w$hdrs_tot, label = "HDRS score")
data2w$hdrs_categories <- set_label(data2w$hdrs_categories, label = "HDRS categories")
data2w$pitt_score <- set_label(data2w$pitt_score, label = "PSQI total score")
data2w$pitt_gqs <- set_label(data2w$pitt_gqs, label = "PSQI Global Quality of Sleep")
data2w$conn_dlvm <- set_label(data2w$conn_dlvm, label = "lDLPFC - vmPFC Conn Z-score")
data2w$vmpfc_cluster <- set_label(data2w$vmpfc_cluster, label = "vmPFC - Right AnG Conn Z-score")
data2w$vasZ <- set_label(data2w$vasZ, label = "VAS Z-score")
data2w$bis11tZ <- set_label(data2w$bis11tZ, label = "BIS-11 Total Z-score")
data2w$ccqnZ <- set_label(data2w$ccqnZ, label = "CCQ-Now Z-score")
data6m$rid <- set_label(data6m$rid, label = "ID")
data6m$group <- set_label(data6m$group, label = "Group")
data6m$stage <- set_label(data6m$stage, label = "Timepoint")
data6m$stage_openlabel <- factor(data6m$stage_openlabel, levels = c("BASELINE", "2W", "3M", "6M"))
data6m$sex <- set_label(data6m$sex, label = "Sex")
data6m$age <- set_label(data6m$age, label = "Age")
data6m$educ <- set_label(data6m$educ, label = "Education")
data6m$ccq_g <- set_label(data6m$ccq_g, label = "CCQ-General")
data6m$ccq_n <- set_label(data6m$ccq_n, label = "CCQ-Now")
data6m$vas <- set_label(data6m$vas, label = "VAS")
data6m$ut_coc <- set_label(data6m$ut_coc, label = "Urine Test: Cocaine")
data6m$bis11t <- set_label(data6m$bis11t, label = "BIS-11 Total")
data6m$bis11cog <- set_label(data6m$bis11cog, label = "BIS-11 Cognitive")
data6m$bis11mot <- set_label(data6m$bis11mot, label = "BIS-11 Motor")
data6m$bis11nonp <- set_label(data6m$bis11nonp, label = "BIS-11 Non-planned")
data6m$hars_tot <- set_label(data6m$hars_tot, label = "HDRS score")
data6m$hars_categories <- set_label(data6m$hars_categories, label = "HDRS categories")
data6m$hdrs_tot <- set_label(data6m$hdrs_tot, label = "HDRS score")
data6m$hdrs_categories <- set_label(data6m$hdrs_categories, label = "HDRS categories")
data6m$pitt_score <- set_label(data6m$pitt_score, label = "PSQI total score")
data6m$pitt_gqs <- set_label(data6m$pitt_gqs, label = "PSQI Global Quality of Sleep")
data6m_conn$rid <- set_label(data6m_conn$rid, label = "ID")
data6m_conn$group <- set_label(data6m_conn$group, label = "Group")
data6m_conn$stage <- set_label(data6m_conn$stage, label = "Timepoint")
data6m_conn$stage_openlabel <- factor(data6m_conn$stage_openlabel, levels = c("BASELINE", "2W", "3M", "6M"))
data6m_conn$sex <- set_label(data6m_conn$sex, label = "Sex")
data6m_conn$age <- set_label(data6m_conn$age, label = "Age")
data6m_conn$educ <- set_label(data6m_conn$educ, label = "Education")
data6m_conn$ccq_g <- set_label(data6m_conn$ccq_g, label = "CCQ-General")
data6m_conn$ccq_n <- set_label(data6m_conn$ccq_n, label = "CCQ-Now")
data6m_conn$vas <- set_label(data6m_conn$vas, label = "VAS")
data6m_conn$ut_coc <- set_label(data6m_conn$ut_coc, label = "Urine Test: Cocaine")
data6m_conn$bis11t <- set_label(data6m_conn$bis11t, label = "BIS-11 Total")
data6m_conn$bis11cog <- set_label(data6m_conn$bis11cog, label = "BIS-11 Cognitive")
data6m_conn$bis11mot <- set_label(data6m_conn$bis11mot, label = "BIS-11 Motor")
data6m_conn$bis11nonp <- set_label(data6m_conn$bis11nonp, label = "BIS-11 Non-planned")
data6m_conn$hars_tot <- set_label(data6m_conn$hars_tot, label = "HDRS score")
data6m_conn$hars_categories <- set_label(data6m_conn$hars_categories, label = "HDRS categories")
data6m_conn$hdrs_tot <- set_label(data6m_conn$hdrs_tot, label = "HDRS score")
data6m_conn$hdrs_categories <- set_label(data6m_conn$hdrs_categories, label = "HDRS categories")
data6m_conn$pitt_score <- set_label(data6m_conn$pitt_score, label = "PSQI total score")
data6m_conn$pitt_gqs <- set_label(data6m_conn$pitt_gqs, label = "PSQI Global Quality of Sleep")
data6m_conn$conn_dlvm <- set_label(data6m_conn$conn_dlvm, label = "lDLPFC - vmPFC Conn Z-score")
data6m_conn$vmpfc_cluster <- set_label(data6m_conn$vmpfc_cluster, label = "vmPFC - Right AnG Conn Z-score")
table1 <- mytable(group~., data=dataT0_gen, use.column.label = TRUE, use.labels = TRUE)
table1
lm_vas <- lmer(vas ~ stage * group + age + sex + (1|rid), data2w)
summary(lm_vas)
lm_ccqn <- lmer(ccq_n ~ stage * group + age + sex + (1|rid), data2w)
summary(lm_ccqn)
lm_bis11 <- lmer(bis11t ~ stage * group + age + sex + (1|rid), data2w)
summary(lm_bis11)
lm_hars <- lmer(hars_tot ~ stage * group + age + sex + (1|rid), data2w)
summary(lm_hars)
lm_hdrs <- lmer(hdrs_tot ~ stage * group + age + sex + (1|rid), data2w)
summary(lm_hdrs)
lm_sleep <- lmer(pitt_score ~ stage * group + age + sex + (1|rid), data2w)
summary(lm_sleep)