1 Libraries

2 Data

data <- read_excel("/Users/carolinaferreiraatuesta/Documents/Age and Epilepsy Surgery/data_age_surgery.xlsx")
## New names:
## * Several_OPs...150 -> Several_OPs
drop <- c("Lang-fMRI", "poMRI_Region", 'outcome1', "New_Psychological_Deficit",
'outcome2',
'outcome3',
'outcome4',
'outcome5',
'outcome6',
'outcome7',
'outcome8',
'outcome9',
'outcome10',
'outcome11',
'outcome12',
'outcome13',
'outcome14',
'outcome15',
'outcome16',
'outcome17',
'outcome18',
'outcome19',
'outcome20',
'outcome21',
'outcome22',
'outcome23',
'outcome24',
'outcome25')
data = data[,!(names(data) %in% drop)]
data$age_cat <- findInterval(data$age_at_surgery, c(0, 30, 50, 100), rightmost.closed = TRUE)

#tempdata <- mice(data1, m = 5, maxit = 50, meth = 'pmm', seed = 500, printFlag = FALSE)
#data <- mice::complete(tempdata,1)

3 Table 1

rndr <- function(x, name, ...) {
    if (length(x) == 0) {
        y <- data[[name]]
        s <- rep("", length(render.default(x=y, name=name, ...)))
        if (is.numeric(y)) {
            p <- fisher.test(y ~ data$age_cat)$p.value
        } else {
            p <- chisq.test(table(y, droplevels(data$age_cat)))$p.value
        }
        s[2] <- sub("<", "&lt;", format.pval(p, digits=3, eps=0.001))
        s
    } else {
        render.default(x=x, name=name, ...)
    }
}
rndr.strat <- function(label, n, ...) {
    ifelse(n==0, label, render.strat.default(label, n, ...))
}

table1(~
sex +
childconvul +
neuro_insult +
status_epilepticus +
family_history +
age_onset +
gtcs +
depression_pre +
psychosis_pre +
psychiatric_pre_any +
depression_post_surgery +
psychosis_post +
psychiatric_post_any +
new_depression +
new_psychosis +
new_psychiatric +
duration +
age_at_surgery +
Hand +
OP_Type +
op_side +
pathology +
died +
aeds +
relapse +
relapse_year +
last_follow +

mri_normal +

L8 +
R8 +
BIL8 +
AZM +
BRI +
CBZ +
CLB +
CZP +
DZP +
ESM +
FBM +
GBP +
LCS +
LTG +
LVT +
LZP +
MDL +
OCBZ +
PB +
PGB +
PHT +
PMD +
RUF +
SUL +
TGB +
TPM +
VGB +
VPA +
ZNS +

psych_subjective_improve +
VIQ_preop +
PIQ_preop +
VerbalMem_preop +
VisualMem_preop +
VIQ_3month +
PIQ_3month +
VerbalMem_3month +
VisualMem_3month +
VerbalDec_3month +
VisualDec_3month +
VIQ_1to2year +
PIQ_1to2year +
VerbalMem_1to2year +
VisualMem_1to2year +
VerbalDec_1to2year +
VisualDec_1to2year +
VIQ_3to5year +
PIQ_3to5year +
VerbalMem_3to5year +
VisualMem_3to5year +
VerbalDec_3to5year +
VisualDec_3to5year +
VIQ_10year +
PIQ_10year +
VerbalMem_10year +
VisualMem_10year +
VerbalDec_10year +
VisualDec_10year +
psych_verbal_learning_pre +
psych_verbal_recall_pre +
psych_visual_learning_pre +
psych_visual_recall_pre +
psych_verbal_learning_3m +
psych_verbal_recall_3m +
psych_visual_learning_3m +
psych_visual_recall_3m +
psych_verbal_learning_1y +
psych_verbal_recall_1y +
psych_visual_learning_1y +
psych_visual_recall_1y +
  change_verbal_learning +
improve_VeL +
decline_VeL +
change_verbal_recall +
improve_VeR +
decline_VeR +
change_visual_learning +
improve_ViL +
decline_ViL +
change_visual_recall +
improve_ViR +
decline_ViR | age_cat, data=data, render=rndr, render.strat=rndr.strat)
1
(n=203)		 2
(n=292)		 3
(n=45)		 Overall
(n=540)
sex
Mean (SD) 0.611 (0.489) 0.517 (0.501) 0.556 (0.503) 0.556 (0.497)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
childconvul
Mean (SD) 0.444 (0.498) 0.362 (0.482) 0.136 (0.347) 0.374 (0.484)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 5 (2.5%) 5 (1.7%) 1 (2.2%) 11 (2.0%)
neuro_insult
Mean (SD) 0.438 (0.497) 0.589 (0.493) 0.489 (0.506) 0.524 (0.500)
Median [Min, Max] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00]
status_epilepticus
Mean (SD) 0.0640 (0.245) 0.0514 (0.221) 0.0222 (0.149) 0.0537 (0.226)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
family_history
Mean (SD) 0.271 (0.446) 0.288 (0.453) 0.333 (0.477) 0.285 (0.452)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
age_onset
Mean (SD) 9.06 (6.32) 13.1 (9.54) 19.0 (16.2) 12.0 (9.65)
Median [Min, Max] 9.00 [0.00, 26.0] 12.0 [0.00, 41.0] 14.0 [0.00, 61.0] 10.0 [0.00, 61.0]
Missing 1 (0.5%) 2 (0.7%) 1 (2.2%) 4 (0.7%)
gtcs
Mean (SD) 0.714 (0.453) 0.716 (0.452) 0.778 (0.420) 0.720 (0.449)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
depression_pre
Mean (SD) 0.256 (0.438) 0.284 (0.452) 0.289 (0.458) 0.274 (0.446)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
psychosis_pre
Mean (SD) 0.251 (0.435) 0.291 (0.455) 0.400 (0.495) 0.285 (0.452)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
psychiatric_pre_any
Mean (SD) 0.296 (0.457) 0.339 (0.474) 0.444 (0.503) 0.331 (0.471)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
depression_post_surgery
Mean (SD) 0.345 (0.476) 0.325 (0.469) 0.289 (0.458) 0.330 (0.471)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
psychosis_post
Mean (SD) 0.389 (0.489) 0.414 (0.493) 0.289 (0.458) 0.394 (0.489)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
psychiatric_post_any
Mean (SD) 0.547 (0.499) 0.555 (0.498) 0.400 (0.495) 0.539 (0.499)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00]
new_depression
Mean (SD) 0.236 (0.426) 0.161 (0.368) 0.111 (0.318) 0.185 (0.389)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
new_psychosis
Mean (SD) 0.374 (0.485) 0.336 (0.473) 0.200 (0.405) 0.339 (0.474)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
new_psychiatric
Mean (SD) 0.212 (0.410) 0.253 (0.436) 0.178 (0.387) 0.231 (0.422)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
duration
Mean (SD) 15.9 (6.86) 25.5 (10.4) 37.5 (15.7) 22.9 (11.7)
Median [Min, Max] 16.3 [2.44, 29.9] 25.6 [3.52, 48.8] 40.1 [4.32, 60.5] 21.2 [2.44, 60.5]
age_at_surgery
Mean (SD) 24.9 (3.48) 38.5 (5.54) 56.1 (4.77) 34.9 (10.2)
Median [Min, Max] 25.6 [16.8, 30.0] 38.0 [30.0, 49.5] 54.6 [50.0, 68.1] 33.3 [16.8, 68.1]
Hand
Mean (SD) 0.217 (0.519) 0.223 (0.519) 0.244 (0.484) 0.222 (0.516)
Median [Min, Max] 0.00 [0.00, 2.00] 0.00 [0.00, 2.00] 0.00 [0.00, 2.00] 0.00 [0.00, 2.00]
OP_Type
Mean (SD) 0.892 (0.383) 0.921 (0.282) 0.911 (0.288) 0.909 (0.324)
Median [Min, Max] 1.00 [0.00, 2.00] 1.00 [0.00, 2.00] 1.00 [0.00, 1.00] 1.00 [0.00, 2.00]
op_side
Mean (SD) 0.601 (0.491) 0.507 (0.501) 0.511 (0.506) 0.543 (0.499)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
pathology
CAV 8 (3.9%) 13 (4.5%) 3 (6.7%) 24 (4.4%)
DNT 27 (13.3%) 25 (8.6%) 7 (15.6%) 59 (10.9%)
DUAL 5 (2.5%) 8 (2.7%) 2 (4.4%) 15 (2.8%)
FCD 5 (2.5%) 6 (2.1%) 3 (6.7%) 14 (2.6%)
GL 5 (2.5%) 4 (1.4%) 0 (0%) 9 (1.7%)
HS 134 (66.0%) 212 (72.6%) 27 (60.0%) 373 (69.1%)
OTHER 19 (9.4%) 24 (8.2%) 3 (6.7%) 46 (8.5%)
died
Mean (SD) 0.0493 (0.217) 0.0479 (0.214) 0.111 (0.318) 0.0537 (0.226)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
aeds
Mean (SD) 2.31 (0.870) 2.46 (0.878) 2.64 (0.802) 2.41 (0.873)
Median [Min, Max] 2.00 [0.00, 5.00] 2.00 [0.00, 6.00] 3.00 [1.00, 4.00] 2.00 [0.00, 6.00]
relapse
Mean (SD) 0.660 (0.475) 0.610 (0.489) 0.733 (0.447) 0.639 (0.481)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
relapse_year
Mean (SD) 5.73 (6.52) 5.59 (6.06) 2.71 (3.20) 5.40 (6.11)
Median [Min, Max] 2.00 [1.00, 23.0] 2.00 [1.00, 24.0] 1.00 [1.00, 15.0] 2.00 [1.00, 24.0]
last_follow
Mean (SD) 14.5 (6.60) 11.5 (6.04) 7.33 (5.08) 12.3 (6.51)
Median [Min, Max] 16.0 [1.00, 25.0] 12.0 [1.00, 24.0] 6.00 [1.00, 19.0] 13.0 [1.00, 25.0]
mri_normal
Mean (SD) 0.0591 (0.236) 0.0651 (0.247) 0.0222 (0.149) 0.0593 (0.236)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
L8
Mean (SD) 0.111 (0.315) 0.218 (0.413) 0.409 (0.497) 0.194 (0.395)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 5 (2.5%) 7 (2.4%) 1 (2.2%) 13 (2.4%)
R8
Mean (SD) 0.0101 (0.100) 0.0246 (0.155) 0.0227 (0.151) 0.0190 (0.137)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 5 (2.5%) 7 (2.4%) 1 (2.2%) 13 (2.4%)
BIL8
Mean (SD) 0.0202 (0.141) 0.0386 (0.193) 0.114 (0.321) 0.0380 (0.191)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 5 (2.5%) 7 (2.4%) 1 (2.2%) 13 (2.4%)
AZM
Mean (SD) 0.137 (0.345) 0.161 (0.369) 0.114 (0.321) 0.148 (0.356)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
BRI
Mean (SD) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
CBZ
Mean (SD) 0.975 (0.158) 0.982 (0.132) 0.864 (0.347) 0.970 (0.172)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
CLB
Mean (SD) 0.569 (0.497) 0.625 (0.485) 0.477 (0.505) 0.591 (0.492)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
CZP
Mean (SD) 0.188 (0.392) 0.196 (0.398) 0.0909 (0.291) 0.184 (0.388)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
DZP
Mean (SD) 0.173 (0.379) 0.151 (0.359) 0.136 (0.347) 0.158 (0.365)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
ESM
Mean (SD) 0.0558 (0.230) 0.0702 (0.256) 0.114 (0.321) 0.0684 (0.253)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
FBM
Mean (SD) 0.00508 (0.0712) 0.0140 (0.118) 0.0227 (0.151) 0.0114 (0.106)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
GBP
Mean (SD) 0.259 (0.439) 0.425 (0.495) 0.364 (0.487) 0.357 (0.480)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
LCS
Mean (SD) 0.0152 (0.123) 0.0281 (0.165) 0.114 (0.321) 0.0304 (0.172)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
LTG
Mean (SD) 0.797 (0.403) 0.747 (0.435) 0.795 (0.408) 0.770 (0.421)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
LVT
Mean (SD) 0.249 (0.433) 0.463 (0.500) 0.727 (0.451) 0.405 (0.491)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
LZP
Mean (SD) 0.00508 (0.0712) 0.0211 (0.144) 0.00 (0.00) 0.0133 (0.115)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
MDL
Mean (SD) 0.00508 (0.0712) 0.00 (0.00) 0.0227 (0.151) 0.00380 (0.0616)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
OCBZ
Mean (SD) 0.0508 (0.220) 0.133 (0.341) 0.159 (0.370) 0.105 (0.306)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
PB
Mean (SD) 0.381 (0.487) 0.512 (0.501) 0.614 (0.493) 0.471 (0.500)
Median [Min, Max] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
PGB
Mean (SD) 0.0508 (0.220) 0.116 (0.321) 0.250 (0.438) 0.103 (0.304)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
PHT
Mean (SD) 0.635 (0.483) 0.705 (0.457) 0.795 (0.408) 0.686 (0.464)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
PMD
Mean (SD) 0.0660 (0.249) 0.232 (0.423) 0.341 (0.479) 0.179 (0.383)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
RUF
Mean (SD) 0.00 (0.00) 0.00351 (0.0592) 0.00 (0.00) 0.00190 (0.0436)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
SUL
Mean (SD) 0.0152 (0.123) 0.0386 (0.193) 0.0227 (0.151) 0.0285 (0.167)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
TGB
Mean (SD) 0.0355 (0.186) 0.0702 (0.256) 0.0909 (0.291) 0.0589 (0.236)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
TPM
Mean (SD) 0.320 (0.468) 0.477 (0.500) 0.477 (0.505) 0.418 (0.494)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
VGB
Mean (SD) 0.589 (0.493) 0.495 (0.501) 0.318 (0.471) 0.515 (0.500)
Median [Min, Max] 1.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
VPA
Mean (SD) 0.792 (0.407) 0.804 (0.398) 0.750 (0.438) 0.795 (0.404)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
ZNS
Mean (SD) 0.0355 (0.186) 0.0772 (0.267) 0.136 (0.347) 0.0665 (0.249)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 6 (3.0%) 7 (2.4%) 1 (2.2%) 14 (2.6%)
psych_subjective_improve
Mean (SD) 0.458 (0.500) 0.408 (0.493) 0.174 (0.388) 0.412 (0.493)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 61 (30.0%) 86 (29.5%) 22 (48.9%) 169 (31.3%)
VIQ_preop
Mean (SD) 83.5 (25.7) 81.3 (33.7) 69.3 (46.6) 81.2 (32.3)
Median [Min, Max] 87.5 [0.00, 126] 91.0 [0.00, 142] 86.0 [0.00, 130] 89.0 [0.00, 142]
Missing 15 (7.4%) 28 (9.6%) 7 (15.6%) 50 (9.3%)
PIQ_preop
Mean (SD) 95.4 (16.4) 95.5 (16.0) 103 (16.1) 95.9 (16.2)
Median [Min, Max] 96.0 [57.0, 137] 95.0 [0.00, 137] 107 [78.0, 139] 96.0 [0.00, 139]
Missing 29 (14.3%) 64 (21.9%) 19 (42.2%) 112 (20.7%)
VerbalMem_preop
Mean (SD) 2.44 (0.840) 2.34 (0.800) 2.13 (0.869) 2.37 (0.823)
Median [Min, Max] 3.00 [0.00, 4.00] 3.00 [0.00, 4.00] 2.00 [1.00, 4.00] 3.00 [0.00, 4.00]
Missing 36 (17.7%) 83 (28.4%) 22 (48.9%) 141 (26.1%)
VisualMem_preop
Mean (SD) 2.82 (1.06) 2.74 (0.816) 2.61 (0.783) 2.77 (0.923)
Median [Min, Max] 3.00 [0.00, 5.00] 3.00 [0.00, 5.00] 3.00 [1.00, 4.00] 3.00 [0.00, 5.00]
Missing 38 (18.7%) 84 (28.8%) 22 (48.9%) 144 (26.7%)
VIQ_3month
Mean (SD) 81.6 (31.0) 61.0 (47.6) 23.3 (40.4) 65.9 (41.7)
Median [Min, Max] 87.0 [0.00, 113] 86.5 [0.00, 99.0] 0.00 [0.00, 70.0] 81.0 [0.00, 113]
Missing 193 (95.1%) 286 (97.9%) 42 (93.3%) 521 (96.5%)
PIQ_3month
Mean (SD) 96.9 (38.1) 59.0 (45.8) 36.7 (63.5) 75.4 (48.5)
Median [Min, Max] 103 [0.00, 134] 86.5 [0.00, 92.0] 0.00 [0.00, 110] 91.0 [0.00, 134]
Missing 193 (95.1%) 286 (97.9%) 42 (93.3%) 521 (96.5%)
VerbalMem_3month
Mean (SD) 15.8 (21.5) 20.3 (21.8) 0.00 (0.00) 15.5 (20.2)
Median [Min, Max] 3.00 [0.00, 46.0] 18.5 [0.00, 49.0] 0.00 [0.00, 0.00] 3.00 [0.00, 49.0]
Missing 197 (97.0%) 286 (97.9%) 43 (95.6%) 526 (97.4%)
VisualMem_3month
Mean (SD) 14.3 (19.3) 19.2 (20.3) 0.00 (0.00) 14.4 (18.6)
Median [Min, Max] 3.00 [0.00, 43.0] 17.5 [0.00, 44.0] 0.00 [0.00, 0.00] 3.00 [0.00, 44.0]
Missing 197 (97.0%) 286 (97.9%) 43 (95.6%) 526 (97.4%)
VerbalDec_3month
Mean (SD) 0.00 (0.00) 0.333 (0.577) 0.00 (0.00) 0.143 (0.378)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00]
Missing 201 (99.0%) 289 (99.0%) 43 (95.6%) 533 (98.7%)
VisualDec_3month
Mean (SD) 0.00 (0.00) 0.333 (0.577) 0.00 (0.00) 0.143 (0.378)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00]
Missing 201 (99.0%) 289 (99.0%) 43 (95.6%) 533 (98.7%)
VIQ_1to2year
Mean (SD) 92.0 (12.5) 94.3 (15.3) 106 (14.3) 93.9 (14.4)
Median [Min, Max] 91.0 [61.0, 125] 94.0 [0.00, 137] 104 [81.0, 138] 94.0 [0.00, 138]
Missing 82 (40.4%) 121 (41.4%) 30 (66.7%) 233 (43.1%)
PIQ_1to2year
Mean (SD) 99.7 (16.6) 98.7 (22.1) 104 (16.1) 99.4 (19.8)
Median [Min, Max] 100 [60.0, 149] 100 [0.00, 140] 104 [74.0, 129] 100 [0.00, 149]
Missing 83 (40.9%) 125 (42.8%) 30 (66.7%) 238 (44.1%)
VerbalMem_1to2year
Mean (SD) 42.8 (10.6) 40.9 (10.5) 35.0 (10.3) 41.4 (10.6)
Median [Min, Max] 44.0 [3.00, 64.0] 42.0 [3.00, 68.0] 34.0 [19.0, 51.0] 42.0 [3.00, 68.0]
Missing 77 (37.9%) 115 (39.4%) 30 (66.7%) 222 (41.1%)
VisualMem_1to2year
Mean (SD) 33.2 (10.3) 30.6 (9.89) 24.7 (5.23) 31.3 (10.1)
Median [Min, Max] 34.0 [2.00, 89.0] 31.5 [3.00, 99.0] 25.0 [14.0, 35.0] 32.0 [2.00, 99.0]
Missing 84 (41.4%) 120 (41.1%) 30 (66.7%) 234 (43.3%)
VerbalDec_1to2year
Mean (SD) 0.302 (0.461) 0.281 (0.451) 0.467 (0.516) 0.298 (0.458)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 77 (37.9%) 114 (39.0%) 30 (66.7%) 221 (40.9%)
VisualDec_1to2year
Mean (SD) 0.190 (0.394) 0.213 (0.411) 0.267 (0.458) 0.207 (0.406)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 77 (37.9%) 114 (39.0%) 30 (66.7%) 221 (40.9%)
VIQ_3to5year
Mean (SD) 78.0 (41.6) 73.3 (38.6) NA (NA) 74.9 (38.4)
Median [Min, Max] 88.0 [0.00, 124] 79.0 [0.00, 109] NA [NA, NA] 87.0 [0.00, 124]
Missing 197 (97.0%) 281 (96.2%) 45 (100%) 523 (96.9%)
PIQ_3to5year
Mean (SD) 88.4 (50.4) 77.4 (47.2) NA (NA) 81.4 (46.7)
Median [Min, Max] 104 [0.00, 126] 99.0 [0.00, 132] NA [NA, NA] 101 [0.00, 132]
Missing 198 (97.5%) 283 (96.9%) 45 (100%) 526 (97.4%)
VerbalMem_3to5year
Mean (SD) 2.00 (1.73) 15.0 (18.5) NA (NA) 9.43 (14.9)
Median [Min, Max] 3.00 [0.00, 3.00] 11.0 [0.00, 38.0] NA [NA, NA] 3.00 [0.00, 38.0]
Missing 200 (98.5%) 288 (98.6%) 45 (100%) 533 (98.7%)
VisualMem_3to5year
Mean (SD) 2.33 (2.08) 11.0 (15.1) NA (NA) 7.29 (11.7)
Median [Min, Max] 3.00 [0.00, 4.00] 6.00 [0.00, 32.0] NA [NA, NA] 3.00 [0.00, 32.0]
Missing 200 (98.5%) 288 (98.6%) 45 (100%) 533 (98.7%)
VerbalDec_3to5year
Mean (SD) 0.00 (0.00) 0.250 (0.500) NA (NA) 0.167 (0.408)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00] NA [NA, NA] 0.00 [0.00, 1.00]
Missing 201 (99.0%) 288 (98.6%) 45 (100%) 534 (98.9%)
VisualDec_3to5year
Mean (SD) 0.00 (0.00) 0.250 (0.500) NA (NA) 0.167 (0.408)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 1.00] NA [NA, NA] 0.00 [0.00, 1.00]
Missing 201 (99.0%) 288 (98.6%) 45 (100%) 534 (98.9%)
VIQ_10year
Mean (SD) 75.9 (34.0) 81.1 (48.8) NA (NA) 78.8 (41.7)
Median [Min, Max] 90.0 [0.00, 97.0] 95.0 [0.00, 136] NA [NA, NA] 91.5 [0.00, 136]
Missing 196 (96.6%) 283 (96.9%) 45 (100%) 524 (97.0%)
PIQ_10year
Mean (SD) 80.3 (39.9) 69.1 (48.6) NA (NA) 74.3 (43.3)
Median [Min, Max] 98.0 [0.00, 102] 83.0 [0.00, 111] NA [NA, NA] 97.0 [0.00, 111]
Missing 197 (97.0%) 285 (97.6%) 45 (100%) 527 (97.6%)
VerbalMem_10year
Mean (SD) 22.0 (31.1) 12.3 (23.2) NA (NA) 15.5 (23.3)
Median [Min, Max] 22.0 [0.00, 44.0] 1.00 [0.00, 47.0] NA [NA, NA] 1.00 [0.00, 47.0]
Missing 201 (99.0%) 288 (98.6%) 45 (100%) 534 (98.9%)
VisualMem_10year
Mean (SD) 21.5 (30.4) 12.8 (23.5) NA (NA) 15.7 (23.2)
Median [Min, Max] 21.5 [0.00, 43.0] 1.50 [0.00, 48.0] NA [NA, NA] 1.50 [0.00, 48.0]
Missing 201 (99.0%) 288 (98.6%) 45 (100%) 534 (98.9%)
VerbalDec_10year
Mean (SD) 0.00 (NA) 0.00 (0.00) NA (NA) 0.00 (0.00)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] NA [NA, NA] 0.00 [0.00, 0.00]
Missing 202 (99.5%) 289 (99.0%) 45 (100%) 536 (99.3%)
VisualDec_10year
Mean (SD) 0.00 (NA) 0.00 (0.00) NA (NA) 0.00 (0.00)
Median [Min, Max] 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] NA [NA, NA] 0.00 [0.00, 0.00]
Missing 202 (99.5%) 289 (99.0%) 45 (100%) 536 (99.3%)
psych_verbal_learning_pre
Mean (SD) 45.5 (9.94) 43.5 (9.68) 37.6 (10.5) 43.7 (10.0)
Median [Min, Max] 46.0 [20.0, 69.0] 44.0 [18.0, 69.0] 39.0 [10.0, 62.0] 44.0 [10.0, 69.0]
Missing 6 (3.0%) 3 (1.0%) 0 (0%) 9 (1.7%)
psych_verbal_recall_pre
Mean (SD) 9.01 (3.04) 8.33 (3.19) 7.11 (3.27) 8.48 (3.18)
Median [Min, Max] 9.00 [2.00, 15.0] 8.00 [0.00, 15.0] 7.00 [1.00, 13.0] 9.00 [0.00, 15.0]
Missing 7 (3.4%) 3 (1.0%) 0 (0%) 10 (1.9%)
psych_visual_learning_pre
Mean (SD) 33.0 (9.76) 31.8 (18.6) 27.3 (7.83) 31.9 (15.2)
Median [Min, Max] 35.0 [0.00, 45.0] 32.0 [0.00, 300] 28.5 [12.0, 42.0] 33.0 [0.00, 300]
Missing 14 (6.9%) 9 (3.1%) 1 (2.2%) 24 (4.4%)
psych_visual_recall_pre
Mean (SD) 6.98 (4.16) 6.54 (4.67) 5.07 (2.65) 6.58 (4.37)
Median [Min, Max] 8.00 [0.00, 51.0] 7.00 [0.00, 72.0] 5.00 [0.00, 9.00] 7.00 [0.00, 72.0]
Missing 14 (6.9%) 9 (3.1%) 1 (2.2%) 24 (4.4%)
psych_verbal_learning_3m
Mean (SD) 42.1 (11.1) 39.5 (11.7) 30.1 (13.2) 39.7 (12.0)
Median [Min, Max] 43.0 [0.00, 66.0] 40.0 [0.00, 67.0] 28.5 [0.00, 53.0] 40.5 [0.00, 67.0]
Missing 29 (14.3%) 36 (12.3%) 9 (20.0%) 74 (13.7%)
psych_verbal_recall_3m
Mean (SD) 7.87 (3.29) 7.11 (3.60) 4.33 (3.09) 7.18 (3.56)
Median [Min, Max] 8.00 [0.00, 15.0] 7.00 [0.00, 15.0] 4.00 [0.00, 12.0] 7.00 [0.00, 15.0]
Missing 29 (14.3%) 36 (12.3%) 9 (20.0%) 74 (13.7%)
psych_visual_learning_3m
Mean (SD) 33.6 (10.1) 30.1 (9.79) 23.0 (8.40) 30.8 (10.2)
Median [Min, Max] 35.0 [0.00, 97.0] 31.0 [0.00, 45.0] 22.0 [0.00, 38.0] 31.0 [0.00, 97.0]
Missing 38 (18.7%) 44 (15.1%) 9 (20.0%) 91 (16.9%)
psych_visual_recall_3m
Mean (SD) 7.37 (7.77) 6.12 (2.72) 4.42 (2.73) 6.44 (5.24)
Median [Min, Max] 8.00 [0.00, 97.0] 7.00 [0.00, 9.00] 4.00 [0.00, 9.00] 7.00 [0.00, 97.0]
Missing 38 (18.7%) 43 (14.7%) 9 (20.0%) 90 (16.7%)
psych_verbal_learning_1y
Mean (SD) 44.1 (9.67) 41.0 (11.2) 33.8 (11.4) 41.6 (11.0)
Median [Min, Max] 45.0 [21.0, 65.0] 42.0 [3.00, 71.0] 34.0 [11.0, 52.0] 43.0 [3.00, 71.0]
Missing 17 (8.4%) 21 (7.2%) 3 (6.7%) 41 (7.6%)
psych_verbal_recall_1y
Mean (SD) 8.52 (3.26) 7.81 (3.50) 6.23 (3.45) 7.94 (3.46)
Median [Min, Max] 8.00 [0.00, 15.0] 8.00 [0.00, 15.0] 6.00 [0.00, 12.0] 8.00 [0.00, 15.0]
Missing 17 (8.4%) 21 (7.2%) 2 (4.4%) 40 (7.4%)
psych_visual_learning_1y
Mean (SD) 33.5 (8.84) 30.2 (8.00) 25.6 (7.37) 31.0 (8.56)
Median [Min, Max] 34.0 [4.00, 89.0] 31.0 [9.00, 45.0] 25.5 [10.0, 41.0] 31.0 [4.00, 89.0]
Missing 25 (12.3%) 28 (9.6%) 3 (6.7%) 56 (10.4%)
psych_visual_recall_1y
Mean (SD) 6.75 (2.58) 6.52 (3.69) 4.71 (2.63) 6.45 (3.28)
Median [Min, Max] 8.00 [0.00, 13.0] 7.00 [0.00, 45.0] 4.50 [0.00, 9.00] 7.00 [0.00, 45.0]
Missing 26 (12.8%) 28 (9.6%) 3 (6.7%) 57 (10.6%)
change_verbal_learning
Mean (SD) -1.75 (8.60) -2.32 (10.6) -4.14 (12.4) -2.27 (10.1)
Median [Min, Max] -3.00 [-25.0, 21.0] -1.00 [-43.0, 26.0] -6.00 [-27.0, 36.0] -2.00 [-43.0, 36.0]
Missing 21 (10.3%) 24 (8.2%) 3 (6.7%) 48 (8.9%)
improve_VeL
Mean (SD) 0.0989 (0.299) 0.116 (0.320) 0.119 (0.328) 0.110 (0.313)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 21 (10.3%) 24 (8.2%) 3 (6.7%) 48 (8.9%)
decline_VeL
Mean (SD) 0.577 (0.495) 0.541 (0.499) 0.619 (0.492) 0.561 (0.497)
Median [Min, Max] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 1.00 [0.00, 1.00]
Missing 21 (10.3%) 24 (8.2%) 3 (6.7%) 48 (8.9%)
change_verbal_recall
Mean (SD) -0.530 (3.42) -0.496 (3.23) -0.907 (3.48) -0.545 (3.32)
Median [Min, Max] 0.00 [-12.0, 8.00] 0.00 [-9.00, 10.0] -1.00 [-11.0, 9.00] 0.00 [-12.0, 10.0]
Missing 22 (10.8%) 24 (8.2%) 2 (4.4%) 48 (8.9%)
improve_VeR
Mean (SD) 0.188 (0.392) 0.160 (0.368) 0.140 (0.351) 0.169 (0.375)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 22 (10.8%) 24 (8.2%) 2 (4.4%) 48 (8.9%)
decline_VeR
Mean (SD) 0.492 (0.501) 0.470 (0.500) 0.512 (0.506) 0.482 (0.500)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 1.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 22 (10.8%) 24 (8.2%) 2 (4.4%) 48 (8.9%)
change_visual_learning
Mean (SD) 0.366 (9.40) 0.378 (8.41) 2.31 (7.97) 0.539 (8.75)
Median [Min, Max] 0.00 [-23.0, 62.0] 1.00 [-25.0, 42.0] 2.00 [-15.0, 29.0] 1.00 [-25.0, 62.0]
Missing 50 (24.6%) 62 (21.2%) 9 (20.0%) 121 (22.4%)
improve_ViL
Mean (SD) 0.0719 (0.259) 0.0870 (0.282) 0.0833 (0.280) 0.0811 (0.273)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 50 (24.6%) 62 (21.2%) 9 (20.0%) 121 (22.4%)
decline_ViL
Mean (SD) 0.451 (0.499) 0.413 (0.493) 0.306 (0.467) 0.418 (0.494)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 50 (24.6%) 62 (21.2%) 9 (20.0%) 121 (22.4%)
change_visual_recall
Mean (SD) -0.118 (3.17) 0.554 (4.00) 0.194 (2.01) 0.279 (3.59)
Median [Min, Max] 0.00 [-27.0, 7.00] 0.00 [-8.00, 43.0] 1.00 [-5.00, 4.00] 0.00 [-27.0, 43.0]
Missing 51 (25.1%) 61 (20.9%) 9 (20.0%) 121 (22.4%)
improve_ViR
Mean (SD) 0.0724 (0.260) 0.117 (0.322) 0.0556 (0.232) 0.0955 (0.294)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 51 (25.1%) 61 (20.9%) 9 (20.0%) 121 (22.4%)
decline_ViR
Mean (SD) 0.309 (0.464) 0.303 (0.461) 0.306 (0.467) 0.305 (0.461)
Median [Min, Max] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00] 0.00 [0.00, 1.00]
Missing 51 (25.1%) 61 (20.9%) 9 (20.0%) 121 (22.4%)

4 MANOVA - Kruskal wallis sum rank

drop <- c("AZM","BRI",  "CBZ",  "CLB",  "CZP",  "DZP",  "ESM",  "FBM",  "GBP",  "LCS",  "LTG",  "LVT",  "LZP",  "MDL",  "OCBZ", "PB",   "PGB",  "PHT",  "PMD",  "RET",  "RUF",  "SUL",  "TGB",  "TPM",  "VGB",  "VPA",  "ZNS")
data = data[,!(names(data) %in% drop)]
x <- which(names(data) == "age_cat") # name of grouping variable
y <- which(names(data)  == 'depression_post_surgery'
| names(data) == 'psychosis_post'
| names(data) == 'psychiatric_post_any'
| names(data) == 'new_depression'
| names(data) == 'new_psychosis'
| names(data) == 'new_psychiatric'

| names(data) == 'died'
| names(data) == 'aeds'
| names(data) == 'relapse'
| names(data) == 'relapse_year'

| names(data) == 'mri_normal'


| names(data) == 'psych_subjective_improve'
| names(data) == 'VIQ_preop'
| names(data) == 'PIQ_preop'
| names(data) == 'VerbalMem_preop'
| names(data) == 'VisualMem_preop'

| names(data) == 'VerbalMem_10year'
| names(data) == 'VisualMem_10year'
| names(data) == 'VerbalDec_10year'
| names(data) == 'VisualDec_10year'
| names(data) == 'psych_verbal_learning_pre'
| names(data) == 'psych_verbal_recall_pre'
| names(data) == 'psych_visual_learning_pre'
| names(data) == 'psych_visual_recall_pre'
| names(data) == 'psych_verbal_learning_3m'
| names(data) == 'psych_verbal_recall_3m'
| names(data) == 'psych_visual_learning_3m'
| names(data) == 'psych_visual_recall_3m'
| names(data) == 'psych_verbal_learning_1y'
| names(data) == 'psych_verbal_recall_1y'
| names(data) == 'psych_visual_learning_1y'
| names(data) == 'psych_visual_recall_1y'
| names(data) == 'improve_VeL' 
| names(data) == 'decline_VeL' 
| names(data) == 'improve_VeR' 
| names(data) == 'decline_VeR' 

| names(data) == 'improve_ViL' 
| names(data) == 'decline_ViL' 

| names(data) == 'improve_ViR' 
| names(data) == 'decline_ViR'
)
method1 <- "kruskal.test" # one of "anova" or "kruskal.test"
method2 <- "t.test" # one of "wilcox.test" or "t.test"
my_comparisons <- list(c("1", "2"), c("1", "3"), c("2", "3")) # comparisons for post-hoc tests

for (i in y) {
  for (j in x) {
    p <- ggboxplot(data,
      x = colnames(data[j]), y = colnames(data[i]),
      color = colnames(data[j]),
      legend = "none",
      palette = "npg",
      add = "jitter"
    )
    print(
      p + stat_compare_means(aes(label = paste0(..method.., ", p-value = ", ..p.format..)),
        method = method1, label.y = max(data[, i], na.rm = TRUE)
      )
      + stat_compare_means(comparisons = my_comparisons, method = method2, label = "p.format") # remove if p-value of ANOVA or Kruskal-Wallis test >= alpha
    )
  }
}

4.1 Table MANOVA

library(broom)
df2 <- data %>% gather(key, value, -age_cat) %>% 
       group_by(key) %>% 
       do(tidy(kruskal.test(x= .$value, g= .$age_cat)))


kableExtra::kbl(df2, digits = round(3)) %>%
  kableExtra::kable_paper(bootstrap_options = "striped", full_width = F, )
key statistic p.value parameter method
aeds 9.082 0.011 2 Kruskal-Wallis rank sum test
age_at_surgery 424.832 0.000 2 Kruskal-Wallis rank sum test
age_onset 3.404 0.182 2 Kruskal-Wallis rank sum test
BIL8 8.599 0.014 2 Kruskal-Wallis rank sum test
change_verbal_learning 0.384 0.825 2 Kruskal-Wallis rank sum test
change_verbal_recall 0.474 0.789 2 Kruskal-Wallis rank sum test
change_visual_learning 5.193 0.075 2 Kruskal-Wallis rank sum test
change_visual_recall 1.282 0.527 2 Kruskal-Wallis rank sum test
childconvul 14.942 0.001 2 Kruskal-Wallis rank sum test
decline_VeL 1.193 0.551 2 Kruskal-Wallis rank sum test
decline_VeR 0.369 0.831 2 Kruskal-Wallis rank sum test
decline_ViL 2.573 0.276 2 Kruskal-Wallis rank sum test
decline_ViR 0.016 0.992 2 Kruskal-Wallis rank sum test
depression_post_surgery 0.573 0.751 2 Kruskal-Wallis rank sum test
depression_pre 0.528 0.768 2 Kruskal-Wallis rank sum test
died 3.182 0.204 2 Kruskal-Wallis rank sum test
duration 51.659 0.000 2 Kruskal-Wallis rank sum test
family_history 0.721 0.697 2 Kruskal-Wallis rank sum test
gtcs 0.803 0.669 2 Kruskal-Wallis rank sum test
Hand 0.570 0.752 2 Kruskal-Wallis rank sum test
id 20.593 0.000 2 Kruskal-Wallis rank sum test
improve_VeL 0.352 0.839 2 Kruskal-Wallis rank sum test
improve_VeR 0.862 0.650 2 Kruskal-Wallis rank sum test
improve_ViL 0.281 0.869 2 Kruskal-Wallis rank sum test
improve_ViR 2.823 0.244 2 Kruskal-Wallis rank sum test
L8 22.725 0.000 2 Kruskal-Wallis rank sum test
last_follow 4.588 0.101 2 Kruskal-Wallis rank sum test
mri_normal 1.282 0.527 2 Kruskal-Wallis rank sum test
neuro_insult 11.115 0.004 2 Kruskal-Wallis rank sum test
new_depression 6.297 0.043 2 Kruskal-Wallis rank sum test
new_psychiatric 1.957 0.376 2 Kruskal-Wallis rank sum test
new_psychosis 5.021 0.081 2 Kruskal-Wallis rank sum test
op_side 4.463 0.107 2 Kruskal-Wallis rank sum test
OP_Type 1.164 0.559 2 Kruskal-Wallis rank sum test
pathology 3.682 0.159 2 Kruskal-Wallis rank sum test
PIQ_10year 0.743 0.389 1 Kruskal-Wallis rank sum test
PIQ_1to2year 0.932 0.627 2 Kruskal-Wallis rank sum test
PIQ_3month 4.156 0.125 2 Kruskal-Wallis rank sum test
PIQ_3to5year 0.758 0.384 1 Kruskal-Wallis rank sum test
PIQ_preop 3.295 0.193 2 Kruskal-Wallis rank sum test
psych_subjective_improve 6.604 0.037 2 Kruskal-Wallis rank sum test
psych_verbal_learning_1y 26.941 0.000 2 Kruskal-Wallis rank sum test
psych_verbal_learning_3m 13.927 0.001 2 Kruskal-Wallis rank sum test
psych_verbal_learning_pre 20.099 0.000 2 Kruskal-Wallis rank sum test
psych_verbal_recall_1y 0.650 0.723 2 Kruskal-Wallis rank sum test
psych_verbal_recall_3m 4.300 0.116 2 Kruskal-Wallis rank sum test
psych_verbal_recall_pre 1.633 0.442 2 Kruskal-Wallis rank sum test
psych_visual_learning_1y 36.684 0.000 2 Kruskal-Wallis rank sum test
psych_visual_learning_3m 37.536 0.000 2 Kruskal-Wallis rank sum test
psych_visual_learning_pre 25.630 0.000 2 Kruskal-Wallis rank sum test
psych_visual_recall_1y 21.199 0.000 2 Kruskal-Wallis rank sum test
psych_visual_recall_3m 19.555 0.000 2 Kruskal-Wallis rank sum test
psych_visual_recall_pre 17.939 0.000 2 Kruskal-Wallis rank sum test
psychiatric_post_any 3.835 0.147 2 Kruskal-Wallis rank sum test
psychiatric_pre_any 3.841 0.147 2 Kruskal-Wallis rank sum test
psychosis_post 2.604 0.272 2 Kruskal-Wallis rank sum test
psychosis_pre 4.100 0.129 2 Kruskal-Wallis rank sum test
R8 1.346 0.510 2 Kruskal-Wallis rank sum test
relapse 3.216 0.200 2 Kruskal-Wallis rank sum test
relapse_year 0.823 0.663 2 Kruskal-Wallis rank sum test
Several_OPs 2.197 0.333 2 Kruskal-Wallis rank sum test
sex 4.251 0.119 2 Kruskal-Wallis rank sum test
status_epilepticus 1.333 0.513 2 Kruskal-Wallis rank sum test
VerbalDec_10year NaN NaN 1 Kruskal-Wallis rank sum test
VerbalDec_1to2year 2.290 0.318 2 Kruskal-Wallis rank sum test
VerbalDec_3month 1.333 0.513 2 Kruskal-Wallis rank sum test
VerbalDec_3to5year 0.500 0.480 1 Kruskal-Wallis rank sum test
VerbalMem_10year 0.000 1.000 1 Kruskal-Wallis rank sum test
VerbalMem_1to2year 9.794 0.007 2 Kruskal-Wallis rank sum test
VerbalMem_3month 2.855 0.240 2 Kruskal-Wallis rank sum test
VerbalMem_3to5year 0.137 0.711 1 Kruskal-Wallis rank sum test
VerbalMem_preop 4.992 0.082 2 Kruskal-Wallis rank sum test
VIQ_10year 1.910 0.167 1 Kruskal-Wallis rank sum test
VIQ_1to2year 8.070 0.018 2 Kruskal-Wallis rank sum test
VIQ_3month 3.410 0.182 2 Kruskal-Wallis rank sum test
VIQ_3to5year 0.825 0.364 1 Kruskal-Wallis rank sum test
VIQ_preop 12.537 0.002 2 Kruskal-Wallis rank sum test
VisualDec_10year NaN NaN 1 Kruskal-Wallis rank sum test
VisualDec_1to2year 0.579 0.749 2 Kruskal-Wallis rank sum test
VisualDec_3month 1.333 0.513 2 Kruskal-Wallis rank sum test
VisualDec_3to5year 0.500 0.480 1 Kruskal-Wallis rank sum test
VisualMem_10year 0.000 1.000 1 Kruskal-Wallis rank sum test
VisualMem_1to2year 20.678 0.000 2 Kruskal-Wallis rank sum test
VisualMem_3month 2.855 0.240 2 Kruskal-Wallis rank sum test
VisualMem_3to5year 0.538 0.463 1 Kruskal-Wallis rank sum test
VisualMem_preop 5.423 0.066 2 Kruskal-Wallis rank sum test

5 Pre-matching table

#tabUnmatched <- CreateTableOne(vars = listvars, strata = "data_psych_match.age_cat", data = match, test = TRUEtestApprox = aov )
#tabUnmatched2 <- print(tabUnmatched, smd = TRUE)
#addmargins(table(ExtractSmd(tabUnmatched) > 0.1))

6 Regression pre-match on cognitive outcomes

reg1 <- glm(data_psych_match$data_psych_match.improve_VeL ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg1)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_VeL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.14279  -0.11816  -0.11816  -0.09353   0.90647  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.06890    0.04062   1.696
## data_psych_match$data_psych_match.age_cat  0.02463    0.02232   1.103
##                                           Pr(>|t|)  
## (Intercept)                                 0.0904 .
## data_psych_match$data_psych_match.age_cat   0.2704  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09891121)
## 
##     Null deviance: 52.444  on 530  degrees of freedom
## Residual deviance: 52.324  on 529  degrees of freedom
## AIC: 282.42
## 
## Number of Fisher Scoring iterations: 2
reg2 <- glm(data_psych_match$data_psych_match.improve_VeR ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg2)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_VeR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.1883  -0.1883  -0.1619  -0.1619   0.8645  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.21471    0.04850   4.427
## data_psych_match$data_psych_match.age_cat -0.02639    0.02666  -0.990
##                                           Pr(>|t|)    
## (Intercept)                               1.16e-05 ***
## data_psych_match$data_psych_match.age_cat    0.323    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1410351)
## 
##     Null deviance: 74.746  on 530  degrees of freedom
## Residual deviance: 74.608  on 529  degrees of freedom
## AIC: 470.81
## 
## Number of Fisher Scoring iterations: 2
reg3 <-  glm(data_psych_match$data_psych_match.improve_ViL ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg3)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_ViL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.16024  -0.11619  -0.11619  -0.07214   0.92786  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.02808    0.03927   0.715
## data_psych_match$data_psych_match.age_cat  0.04405    0.02158   2.041
##                                           Pr(>|t|)  
## (Intercept)                                 0.4749  
## data_psych_match$data_psych_match.age_cat   0.0418 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09247262)
## 
##     Null deviance: 49.303  on 530  degrees of freedom
## Residual deviance: 48.918  on 529  degrees of freedom
## AIC: 246.68
## 
## Number of Fisher Scoring iterations: 2
reg4<-  glm(data_psych_match$data_psych_match.improve_ViR~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg4)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_ViR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.1724  -0.1262  -0.1262  -0.0800   0.9200  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.03378    0.04080   0.828
## data_psych_match$data_psych_match.age_cat  0.04622    0.02242   2.061
##                                           Pr(>|t|)  
## (Intercept)                                 0.4081  
## data_psych_match$data_psych_match.age_cat   0.0397 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09980382)
## 
##     Null deviance: 53.220  on 530  degrees of freedom
## Residual deviance: 52.796  on 529  degrees of freedom
## AIC: 287.19
## 
## Number of Fisher Scoring iterations: 2
reg5<-  glm(data_psych_match$data_psych_match.decline_VeL ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg5)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_VeL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5629  -0.5579   0.4371   0.4421   0.4471  
## 
## Coefficients:
##                                            Estimate Std. Error t value
## (Intercept)                                0.567810   0.064239   8.839
## data_psych_match$data_psych_match.age_cat -0.004953   0.035305  -0.140
##                                           Pr(>|t|)    
## (Intercept)                                 <2e-16 ***
## data_psych_match$data_psych_match.age_cat    0.888    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.2474036)
## 
##     Null deviance: 130.88  on 530  degrees of freedom
## Residual deviance: 130.88  on 529  degrees of freedom
## AIC: 769.24
## 
## Number of Fisher Scoring iterations: 2
reg6<-  glm(data_psych_match$data_psych_match.decline_VeR ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg6)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_VeR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4867  -0.4817  -0.4767   0.5183   0.5233  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.47164    0.06464   7.296
## data_psych_match$data_psych_match.age_cat  0.00501    0.03553   0.141
##                                           Pr(>|t|)    
## (Intercept)                               1.09e-12 ***
## data_psych_match$data_psych_match.age_cat    0.888    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.2505433)
## 
##     Null deviance: 132.54  on 530  degrees of freedom
## Residual deviance: 132.54  on 529  degrees of freedom
## AIC: 775.94
## 
## Number of Fisher Scoring iterations: 2
reg7 <- glm(data_psych_match$data_psych_match.decline_ViL ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg7)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_ViL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.4659  -0.4306  -0.4306   0.5694   0.6048  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.50126    0.06418   7.810
## data_psych_match$data_psych_match.age_cat -0.03535    0.03527  -1.002
##                                           Pr(>|t|)    
## (Intercept)                               3.08e-14 ***
## data_psych_match$data_psych_match.age_cat    0.317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.2469438)
## 
##     Null deviance: 130.88  on 530  degrees of freedom
## Residual deviance: 130.63  on 529  degrees of freedom
## AIC: 768.26
## 
## Number of Fisher Scoring iterations: 2
reg8 <- glm(data_psych_match$data_psych_match.decline_ViR~ data_psych_match$data_psych_match.age_cat, data = data_psych_match)
summary(reg8)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_ViR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.3499  -0.3443  -0.3388   0.6557   0.6612  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                               0.333270   0.061412   5.427
## data_psych_match$data_psych_match.age_cat 0.005531   0.033752   0.164
##                                           Pr(>|t|)    
## (Intercept)                               8.75e-08 ***
## data_psych_match$data_psych_match.age_cat     0.87    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.2261125)
## 
##     Null deviance: 119.62  on 530  degrees of freedom
## Residual deviance: 119.61  on 529  degrees of freedom
## AIC: 721.46
## 
## Number of Fisher Scoring iterations: 2

7 Matching

w.out <- weightit(as.factor(data_psych_match$data_psych_match.age_cat) ~
data_psych_match.sex+
data_psych_match.childconvul+
data_psych_match.neuro_insult+
data_psych_match.status_epilepticus+
data_psych_match.family_history+
data_psych_match.pathology+
data_psych_match.mri_normal+
data_psych_match.psychiatric_pre_any+
data_psych_match.Hand+
data_psych_match.OP_Type+
data_psych_match.op_side+
data_psych_match.gtcs+
data_psych_match.duration+
data_psych_match.aeds+
data_psych_match.psych_verbal_learning_pre+
data_psych_match.psych_verbal_recall_pre+
data_psych_match.psych_visual_learning_pre+
data_psych_match.psych_visual_recall_pre+
data_psych_match.depression_pre+
data_psych_match.psychosis_pre+
data_psych_match.TPM+
data_psych_match.LVT+
data_psych_match.LTG,
data = data_psych_match, 
method = "cbps", focal = "1", estimand = "ATT")
summary(w.out)
##                  Summary of weights
## 
## - Weight ranges:
## 
##     Min                                   Max
## 1 1.000  ||                            1.0000
## 2 0.013 |-------|                     14.2950
## 3 0.000 |---------------------------| 48.2937
## 
## - Units with 5 greatest weights by group:
##                                          
##         1       3       4       5       6
##  1      1       1       1       1       1
##       273     251     112      79      20
##  2 9.6716 10.9426 12.2633 12.7028  14.295
##       508     448     440     209     185
##  3 2.9664   3.148 11.8085 17.1247 48.2937
## 
## - Weight statistics:
## 
##         Coef of Var   MAD Entropy
## 1             0.000 0.000   0.000
## 2             1.437 0.892 301.212
## 3             3.665 1.557 128.108
## overall       2.023 0.776 527.392
## 
## - Effective Sample Sizes:
## 
##              1       2      3
## Unweighted 197 289.000 45.000
## Weighted   197  94.487  3.184

8 Regression post-match on cognitive outcomes

#Check balance
library(cobalt)
bal.tab(w.out, which.treat = NA)
## Call
##  weightit(formula = as.factor(data_psych_match$data_psych_match.age_cat) ~ 
##     data_psych_match.sex + data_psych_match.childconvul + data_psych_match.neuro_insult + 
##         data_psych_match.status_epilepticus + data_psych_match.family_history + 
##         data_psych_match.pathology + data_psych_match.mri_normal + 
##         data_psych_match.psychiatric_pre_any + data_psych_match.Hand + 
##         data_psych_match.OP_Type + data_psych_match.op_side + 
##         data_psych_match.gtcs + data_psych_match.duration + data_psych_match.aeds + 
##         data_psych_match.psych_verbal_learning_pre + data_psych_match.psych_verbal_recall_pre + 
##         data_psych_match.psych_visual_learning_pre + data_psych_match.psych_visual_recall_pre + 
##         data_psych_match.depression_pre + data_psych_match.psychosis_pre + 
##         data_psych_match.TPM + data_psych_match.LVT + data_psych_match.LTG, 
##     data = data_psych_match, method = "cbps", estimand = "ATT", 
##     focal = "1")
## 
## Balance summary across all treatment pairs
##                                               Type Max.Diff.Adj
## data_psych_match.sex                        Binary       0.5274
## data_psych_match.childconvul                Binary       0.4161
## data_psych_match.neuro_insult               Binary       0.1777
## data_psych_match.status_epilepticus         Binary       0.0601
## data_psych_match.family_history             Binary       0.2307
## data_psych_match.pathology_CAV              Binary       0.0104
## data_psych_match.pathology_DNT              Binary       0.1017
## data_psych_match.pathology_DUAL             Binary       0.0203
## data_psych_match.pathology_FCD              Binary       0.0247
## data_psych_match.pathology_GL               Binary       0.0254
## data_psych_match.pathology_HS               Binary       0.0550
## data_psych_match.pathology_OTHER            Binary       0.0781
## data_psych_match.mri_normal                 Binary       0.0567
## data_psych_match.psychiatric_pre_any        Binary       0.2164
## data_psych_match.Hand                      Contin.       0.0261
## data_psych_match.OP_Type                   Contin.       0.1878
## data_psych_match.op_side                    Binary       0.2808
## data_psych_match.gtcs                       Binary       0.1307
## data_psych_match.duration                  Contin.       0.3990
## data_psych_match.aeds                      Contin.       0.9084
## data_psych_match.psych_verbal_learning_pre Contin.       0.6733
## data_psych_match.psych_verbal_recall_pre   Contin.       0.3077
## data_psych_match.psych_visual_learning_pre Contin.       0.3835
## data_psych_match.psych_visual_recall_pre   Contin.       0.1410
## data_psych_match.depression_pre             Binary       0.1930
## data_psych_match.psychosis_pre              Binary       0.3199
## data_psych_match.TPM                        Binary       0.0310
## data_psych_match.LVT                        Binary       0.1384
## data_psych_match.LTG                        Binary       0.1535
## 
## Effective sample sizes
##                  2      3   1
## Unadjusted 289.000 45.000 197
## Adjusted    94.487  3.184 197
#Estimate treatment effects (using jtools to get robust SEs)
#(Can also use survey package)
library(jtools)
reg10 <- glm(data_psych_match$data_psych_match.improve_VeL ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg10)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_VeL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.40622  -0.11979  -0.10389  -0.03837   2.75720  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.15046    0.03975   3.785
## data_psych_match$data_psych_match.age_cat -0.03067    0.02031  -1.510
##                                           Pr(>|t|)    
## (Intercept)                               0.000172 ***
## data_psych_match$data_psych_match.age_cat 0.131560    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1140487)
## 
##     Null deviance: 60.592  on 530  degrees of freedom
## Residual deviance: 60.332  on 529  degrees of freedom
## AIC: 596.65
## 
## Number of Fisher Scoring iterations: 2
reg11 <- glm(data_psych_match$data_psych_match.improve_VeR ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg11)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_VeR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.48751  -0.21084  -0.12737  -0.03542   2.63667  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.29274    0.04706   6.220
## data_psych_match$data_psych_match.age_cat -0.08190    0.02404  -3.407
##                                           Pr(>|t|)    
## (Intercept)                               1.01e-09 ***
## data_psych_match$data_psych_match.age_cat 0.000707 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1598415)
## 
##     Null deviance: 86.411  on 530  degrees of freedom
## Residual deviance: 84.556  on 529  degrees of freedom
## AIC: 775.89
## 
## Number of Fisher Scoring iterations: 2
reg12 <- glm(data_psych_match$data_psych_match.improve_ViL ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg12)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_ViL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.44033  -0.10343  -0.10145  -0.03888   3.03208  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.12347    0.03838   3.217
## data_psych_match$data_psych_match.age_cat -0.02004    0.01960  -1.022
##                                           Pr(>|t|)   
## (Intercept)                                0.00137 **
## data_psych_match$data_psych_match.age_cat  0.30722   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.106295)
## 
##     Null deviance: 56.341  on 530  degrees of freedom
## Residual deviance: 56.230  on 529  degrees of freedom
## AIC: 559.26
## 
## Number of Fisher Scoring iterations: 2
reg13 <- glm(data_psych_match$data_psych_match.improve_ViR ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg13)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.improve_ViR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78189  -0.12964  -0.12964  -0.05551   3.07790  
## 
## Coefficients:
##                                            Estimate Std. Error t value
## (Intercept)                                0.138208   0.044825   3.083
## data_psych_match$data_psych_match.age_cat -0.008565   0.022895  -0.374
##                                           Pr(>|t|)   
## (Intercept)                                0.00215 **
## data_psych_match$data_psych_match.age_cat  0.70848   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1449943)
## 
##     Null deviance: 76.722  on 530  degrees of freedom
## Residual deviance: 76.702  on 529  degrees of freedom
## AIC: 724.13
## 
## Number of Fisher Scoring iterations: 2
reg14 <- glm(data_psych_match$data_psych_match.decline_VeL~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg14)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_VeL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.4207  -0.5999   0.1721   0.4001   2.1012  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.65376    0.06786   9.634
## data_psych_match$data_psych_match.age_cat -0.05384    0.03466  -1.553
##                                           Pr(>|t|)    
## (Intercept)                                 <2e-16 ***
## data_psych_match$data_psych_match.age_cat    0.121    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.3323326)
## 
##     Null deviance: 176.61  on 530  degrees of freedom
## Residual deviance: 175.80  on 529  degrees of freedom
## AIC: 1164.6
## 
## Number of Fisher Scoring iterations: 2
reg15 <- glm(data_psych_match$data_psych_match.decline_VeR ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg15)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_VeR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.6485  -0.5224  -0.0769   0.4776   1.9656  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                               0.521035   0.068339   7.624
## data_psych_match$data_psych_match.age_cat 0.001325   0.034905   0.038
##                                           Pr(>|t|)    
## (Intercept)                               1.15e-13 ***
## data_psych_match$data_psych_match.age_cat     0.97    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.3370134)
## 
##     Null deviance: 178.28  on 530  degrees of freedom
## Residual deviance: 178.28  on 529  degrees of freedom
## AIC: 1172
## 
## Number of Fisher Scoring iterations: 2
reg16 <- glm(data_psych_match$data_psych_match.decline_ViR ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg16)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_ViR ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0149  -0.2434  -0.2355   0.4149   3.5657  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.12000    0.06430   1.866
## data_psych_match$data_psych_match.age_cat  0.12230    0.03284   3.724
##                                           Pr(>|t|)    
## (Intercept)                               0.062539 .  
## data_psych_match$data_psych_match.age_cat 0.000217 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.298329)
## 
##     Null deviance: 161.95  on 530  degrees of freedom
## Residual deviance: 157.82  on 529  degrees of freedom
## AIC: 1107.2
## 
## Number of Fisher Scoring iterations: 2
reg17 <- glm(data_psych_match$data_psych_match.decline_ViL ~ data_psych_match$data_psych_match.age_cat, data = data_psych_match, weights = w.out$weights, contrast=list(age_cat=contr.sum))
summary(reg17)
## 
## Call:
## glm(formula = data_psych_match$data_psych_match.decline_ViL ~ 
##     data_psych_match$data_psych_match.age_cat, data = data_psych_match, 
##     weights = w.out$weights, contrasts = list(age_cat = contr.sum))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7864  -0.3822  -0.1445   0.6168   3.9494  
## 
## Coefficients:
##                                           Estimate Std. Error t value
## (Intercept)                                0.35744    0.06709   5.328
## data_psych_match$data_psych_match.age_cat  0.02475    0.03427   0.722
##                                           Pr(>|t|)    
## (Intercept)                               1.47e-07 ***
## data_psych_match$data_psych_match.age_cat     0.47    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.3248295)
## 
##     Null deviance: 172.00  on 530  degrees of freedom
## Residual deviance: 171.83  on 529  degrees of freedom
## AIC: 1152.4
## 
## Number of Fisher Scoring iterations: 2
#bal.table(mnps.AOD, collapse.to = 'covariate', digits = 4)

```