data <- read.csv("/Users/carolinaferreiraatuesta/Documents/UCL/Reserach Project/Thesis Databases/Database_thesis_final_R.csv")
dataset <- saveRDS(data, "data.rds")
ddist<-datadist(data)
options(datadist='ddist')tab1 <-
CreateTableOne(data = data,
testApprox = chisq.test,
testExact = fisher.test)
cat <-
c("sz",
"auras",
"drugs",
"gtcs",
"temp",
"nosz",
"MRI",
"complete",
"pathology_HS")
cont <- c("time_sz", "time_begin", "duration")
tab2 <- print(
tab1,
nonnormal = cont,
smd = TRUE,
missing = TRUE,
testNormal = TRUE
)##
## Overall Missing
## n 350
## auras (mean (SD)) 0.06 (0.24) 0.0
## time_begin (median [IQR]) 1.31 [0.88, 3.00] 0.0
## sz (mean (SD)) 0.30 (0.46) 0.0
## time_sz (median [IQR]) 5.13 [2.05, 9.79] 0.0
## all (mean (SD)) 0.40 (0.49) 0.0
## all2 (mean (SD)) 0.60 (0.49) 0.0
## surgery_to_withdrawalall (mean (SD)) 5.04 (3.91) 0.0
## begin_all_follow (mean (SD)) 4.12 (3.90) 0.0
## duration (median [IQR]) 21.74 [13.21, 29.74] 0.0
## duration_strat (mean (SD)) 2.75 (1.25) 0.0
## drugs (mean (SD)) 2.38 (0.83) 0.0
## gtcs (mean (SD)) 0.67 (0.47) 0.0
## nosz (mean (SD)) 24.01 (53.45) 0.0
## temp (mean (SD)) 0.11 (0.31) 0.0
## MRI (mean (SD)) 0.96 (0.19) 0.0
## complete (mean (SD)) 0.00 (0.05) 0.0
## pathology_HS (mean (SD)) 0.68 (0.47) 0.0
## withdrawalall (mean (SD)) 0.40 (0.49) 0.0
## psychiatric_pre_any (mean (SD)) 0.43 (0.50) 0.0
## age_onset (mean (SD)) 12.37 (10.16) 0.0
## age_at_surgery (mean (SD)) 34.89 (10.60) 0.0
## cure_last (mean (SD)) 0.33 (0.47) 0.0
## years_follow_up (mean (SD)) 11.28 (6.20) 0.0Cohort: Seizure free before reduction Outcome: Seizure relapse
model1 <-
coxph(
Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin + data$duration + data$drugs + data$gtcs + data$nosz +
data$temp + data$MRI + data$pathology_HS,
x = TRUE,
y = TRUE,
data = data
)
print(model1)## Call:
## coxph(formula = Surv(time = data$time_sz, event = data$sz) ~
## data$auras + data$time_begin + data$duration + data$drugs +
## data$gtcs + data$nosz + data$temp + data$MRI + data$pathology_HS,
## data = data, x = TRUE, y = TRUE)
##
## coef exp(coef) se(coef) z p
## data$auras 1.344e+00 3.834e+00 3.159e-01 4.255 2.09e-05
## data$time_begin -3.520e-02 9.654e-01 2.684e-02 -1.311 0.190
## data$duration 8.171e-03 1.008e+00 8.664e-03 0.943 0.346
## data$drugs 1.854e-01 1.204e+00 1.238e-01 1.498 0.134
## data$gtcs 3.492e-01 1.418e+00 2.265e-01 1.541 0.123
## data$nosz 5.138e-05 1.000e+00 1.971e-03 0.026 0.979
## data$temp -3.173e-01 7.281e-01 4.145e-01 -0.766 0.444
## data$MRI -5.347e-02 9.479e-01 6.170e-01 -0.087 0.931
## data$pathology_HS -7.492e-02 9.278e-01 2.747e-01 -0.273 0.785
##
## Likelihood ratio test=21.11 on 9 df, p=0.01218
## n= 350, number of events= 106step(model1, direction = "both")## Start: AIC=1136.43
## Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin +
## data$duration + data$drugs + data$gtcs + data$nosz + data$temp +
## data$MRI + data$pathology_HS
##
## Df AIC
## - data$nosz 1 1134.4
## - data$MRI 1 1134.4
## - data$pathology_HS 1 1134.5
## - data$temp 1 1135.0
## - data$duration 1 1135.3
## - data$time_begin 1 1136.3
## <none> 1136.4
## - data$drugs 1 1136.6
## - data$gtcs 1 1136.9
## - data$auras 1 1148.7
##
## Step: AIC=1134.43
## Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin +
## data$duration + data$drugs + data$gtcs + data$temp + data$MRI +
## data$pathology_HS
##
## Df AIC
## - data$MRI 1 1132.4
## - data$pathology_HS 1 1132.5
## - data$temp 1 1133.0
## - data$duration 1 1133.3
## - data$time_begin 1 1134.3
## <none> 1134.4
## - data$drugs 1 1134.6
## - data$gtcs 1 1134.9
## + data$nosz 1 1136.4
## - data$auras 1 1146.7
##
## Step: AIC=1132.44
## Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin +
## data$duration + data$drugs + data$gtcs + data$temp + data$pathology_HS
##
## Df AIC
## - data$pathology_HS 1 1130.5
## - data$temp 1 1131.1
## - data$duration 1 1131.3
## - data$time_begin 1 1132.3
## <none> 1132.4
## - data$drugs 1 1132.6
## - data$gtcs 1 1133.0
## + data$MRI 1 1134.4
## + data$nosz 1 1134.4
## - data$auras 1 1144.7
##
## Step: AIC=1130.53
## Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin +
## data$duration + data$drugs + data$gtcs + data$temp
##
## Df AIC
## - data$temp 1 1129.1
## - data$duration 1 1129.3
## - data$time_begin 1 1130.5
## <none> 1130.5
## - data$drugs 1 1130.7
## - data$gtcs 1 1131.1
## + data$pathology_HS 1 1132.4
## + data$MRI 1 1132.5
## + data$nosz 1 1132.5
## - data$auras 1 1142.7
##
## Step: AIC=1129.09
## Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin +
## data$duration + data$drugs + data$gtcs
##
## Df AIC
## - data$duration 1 1128.0
## - data$time_begin 1 1128.9
## - data$drugs 1 1128.9
## <none> 1129.1
## - data$gtcs 1 1129.7
## + data$temp 1 1130.5
## + data$MRI 1 1131.1
## + data$pathology_HS 1 1131.1
## + data$nosz 1 1131.1
## - data$auras 1 1141.2
##
## Step: AIC=1128.05
## Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin +
## data$drugs + data$gtcs
##
## Df AIC
## <none> 1128.0
## - data$drugs 1 1128.1
## - data$time_begin 1 1128.3
## - data$gtcs 1 1129.0
## + data$duration 1 1129.1
## + data$temp 1 1129.3
## + data$pathology_HS 1 1129.9
## + data$nosz 1 1130.0
## + data$MRI 1 1130.0
## - data$auras 1 1141.2## Call:
## coxph(formula = Surv(time = data$time_sz, event = data$sz) ~
## data$auras + data$time_begin + data$drugs + data$gtcs, data = data,
## x = TRUE, y = TRUE)
##
## coef exp(coef) se(coef) z p
## data$auras 1.37550 3.95704 0.30905 4.451 8.56e-06
## data$time_begin -0.03769 0.96301 0.02636 -1.430 0.1528
## data$drugs 0.17264 1.18844 0.11772 1.467 0.1425
## data$gtcs 0.37628 1.45685 0.22418 1.678 0.0933
##
## Likelihood ratio test=19.49 on 4 df, p=0.0006307
## n= 350, number of events= 106model1f <-
coxph(
Surv(time = data$time_sz, event = data$sz) ~ data$auras + data$time_begin + data$drugs + data$gtcs
)
summary(model1f)## Call:
## coxph(formula = Surv(time = data$time_sz, event = data$sz) ~
## data$auras + data$time_begin + data$drugs + data$gtcs)
##
## n= 350, number of events= 106
##
## coef exp(coef) se(coef) z Pr(>|z|)
## data$auras 1.37550 3.95704 0.30905 4.451 8.56e-06 ***
## data$time_begin -0.03769 0.96301 0.02636 -1.430 0.1528
## data$drugs 0.17264 1.18844 0.11772 1.467 0.1425
## data$gtcs 0.37628 1.45685 0.22418 1.678 0.0933 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## data$auras 3.957 0.2527 2.1593 7.252
## data$time_begin 0.963 1.0384 0.9145 1.014
## data$drugs 1.188 0.8414 0.9436 1.497
## data$gtcs 1.457 0.6864 0.9388 2.261
##
## Concordance= 0.627 (se = 0.03 )
## Likelihood ratio test= 19.49 on 4 df, p=6e-04
## Wald test = 23.84 on 4 df, p=9e-05
## Score (logrank) test = 25.38 on 4 df, p=4e-05``{r} library(Hmisc) x <- rcorrcens(formula = Surv(time_sz,sz)~ data\(auras + data\)time_begin + data\(drugs + data\)gtcs ,data=data) x
CstatisticCI <- function(x) { se <- x[“S.D.”]/sqrt(x[“n”]) Low95 <- x[“C Index”] - 1.96se Upper95 <- x[“C Index”] + 1.96se cbind(x[“C Index”], Low95, Upper95) }
CstatisticCI(model1fr)
set.seed(1)
x <- round(rnorm(200))
y <- rnorm(200)
rcorr.cens(x, y, outx=TRUE) # can correlate non-censored variables
library(survival)
age <- rnorm(400, 50, 10)
bp <- rnorm(400,120, 15)
bp[1] <- NA
d.time <- rexp(400)
cens <- runif(400,.5,2)
death <- d.time <= cens
d.time <- pmin(d.time, cens)
out.rcorr <- rcorr.cens(age, Surv(d.time, death))
CstatisticCI(out.rcorr)
fitmodel1f <-
cph(
Surv(time = time_sz, event = sz) ~ auras + time_begin + drugs + gtcs,
data = data,
x = TRUE,
y = TRUE,
surv = TRUE,
time.inc = 2
)
fitmodel1f## Cox Proportional Hazards Model
##
## cph(formula = Surv(time = time_sz, event = sz) ~ auras + time_begin +
## drugs + gtcs, data = data, x = TRUE, y = TRUE, surv = TRUE,
## time.inc = 2)
##
## Model Tests Discrimination
## Indexes
## Obs 350 LR chi2 19.49 R2 0.056
## Events 106 d.f. 4 Dxy 0.253
## Center 0.6434 Pr(> chi2) 0.0006 g 0.399
## Score chi2 25.38 gr 1.490
## Pr(> chi2) 0.0000
##
## Coef S.E. Wald Z Pr(>|Z|)
## auras 1.3755 0.3090 4.45 <0.0001
## time_begin -0.0377 0.0264 -1.43 0.1528
## drugs 0.1726 0.1177 1.47 0.1425
## gtcs 0.3763 0.2242 1.68 0.0933
## rms::validate(fitmodel1f, dxy = TRUE, B = 1000)## index.orig training test optimism index.corrected n
## Dxy 0.2530 0.2555 0.2264 0.0291 0.2239 1000
## R2 0.0563 0.0671 0.0484 0.0187 0.0376 1000
## Slope 1.0000 1.0000 0.8882 0.1118 0.8882 1000
## D 0.0162 0.0198 0.0138 0.0060 0.0102 1000
## U -0.0018 -0.0018 0.0018 -0.0036 0.0019 1000
## Q 0.0180 0.0216 0.0119 0.0097 0.0083 1000
## g 0.3987 0.4422 0.3752 0.0670 0.3317 1000fitmodel1f$Design$label <-
c(
"SPSs before withdrawal (Yes=1, No=0)",
"Time to begin withdrawal (Years from surgery)",
"Number of AEDs at time of surgery",
"GTCS before surgery (Yes=1, No=0)"
)
surv.fitmodel1f <- Survival(fitmodel1f)
nom.cox1 <-
nomogram(
fitmodel1f,
fun = list(function(x)
surv.fitmodel1f(2, x), function(x)
surv.fitmodel1f(5, x)),
funlabel = c("2-year seizure freedom", "5-year seizure freedom"),
lp = F,
fun.at = c(0.99, 0.95, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2)
)
tiff(
"nomo1.tiff",
width = cm(17),
height = cm(10),
units = 'cm',
res = 300
)
nomo1<- plot(
nom.cox1,
cex.axis = 1,
cex.var = 1,
col.grid = gray(c(0.8, 0.95)),
theme(text = element_text(
family = "Times New Roman",
face = "bold",
size = 20
))
)
title(main = "Nomogram predicting risk of seizure relapse after beginnign AED withdrawal after epilepsy surgery", theme(text =
element_text(
family = "Times New Roman",
face = "bold",
size = 20
)))
dev.off()## quartz_off_screen
## 2nomo1## NULLddist <- datadist(data)
options(datadist = 'ddist')
web <-
cph(Surv(time = time_sz, event = sz) ~ auras + time_begin + drugs + gtcs ,
data = data)
DNbuilder(
web,
data = data,
clevel = 0.95,
covariate = c("numeric"),
ptype = c("st")
)## creating new directory: /Users/carolinaferreiraatuesta/R/DynNomapp## Export dataset: /Users/carolinaferreiraatuesta/R/DynNomapp/dataset.RData## Export functions: /Users/carolinaferreiraatuesta/R/DynNomapp/functions.R## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/README.txt## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/ui.R## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/server.R## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/global.RCohort: Seizure free before reduction Outcome: withdrawal all
model2 <-
coxph(
Surv(time = begin_all_follow, event = all) ~ auras + age_at_surgery + drugs + time_begin + pathology_HS + gtcs + MRI + psychiatric_pre_any + duration,
data = data,
x = TRUE,
y = TRUE
)
print(model2)## Call:
## coxph(formula = Surv(time = begin_all_follow, event = all) ~
## auras + age_at_surgery + drugs + time_begin + pathology_HS +
## gtcs + MRI + psychiatric_pre_any + duration, data = data,
## x = TRUE, y = TRUE)
##
## coef exp(coef) se(coef) z p
## auras -0.497195 0.608234 0.439740 -1.131 0.25820
## age_at_surgery -0.028425 0.971975 0.011276 -2.521 0.01171
## drugs -0.360725 0.697171 0.117311 -3.075 0.00211
## time_begin -0.066724 0.935453 0.032535 -2.051 0.04029
## pathology_HS -0.085182 0.918345 0.194728 -0.437 0.66179
## gtcs -0.046131 0.954917 0.180559 -0.255 0.79834
## MRI 0.695083 2.003875 0.599016 1.160 0.24590
## psychiatric_pre_any -0.320041 0.726119 0.177625 -1.802 0.07158
## duration 0.009447 1.009492 0.010032 0.942 0.34631
##
## Likelihood ratio test=31.41 on 9 df, p=0.0002516
## n= 350, number of events= 141summary(model2)## Call:
## coxph(formula = Surv(time = begin_all_follow, event = all) ~
## auras + age_at_surgery + drugs + time_begin + pathology_HS +
## gtcs + MRI + psychiatric_pre_any + duration, data = data,
## x = TRUE, y = TRUE)
##
## n= 350, number of events= 141
##
## coef exp(coef) se(coef) z Pr(>|z|)
## auras -0.497195 0.608234 0.439740 -1.131 0.25820
## age_at_surgery -0.028425 0.971975 0.011276 -2.521 0.01171 *
## drugs -0.360725 0.697171 0.117311 -3.075 0.00211 **
## time_begin -0.066724 0.935453 0.032535 -2.051 0.04029 *
## pathology_HS -0.085182 0.918345 0.194728 -0.437 0.66179
## gtcs -0.046131 0.954917 0.180559 -0.255 0.79834
## MRI 0.695083 2.003875 0.599016 1.160 0.24590
## psychiatric_pre_any -0.320041 0.726119 0.177625 -1.802 0.07158 .
## duration 0.009447 1.009492 0.010032 0.942 0.34631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## auras 0.6082 1.6441 0.2569 1.4401
## age_at_surgery 0.9720 1.0288 0.9507 0.9937
## drugs 0.6972 1.4344 0.5540 0.8774
## time_begin 0.9355 1.0690 0.8777 0.9970
## pathology_HS 0.9183 1.0889 0.6270 1.3451
## gtcs 0.9549 1.0472 0.6703 1.3604
## MRI 2.0039 0.4990 0.6194 6.4827
## psychiatric_pre_any 0.7261 1.3772 0.5126 1.0285
## duration 1.0095 0.9906 0.9898 1.0295
##
## Concordance= 0.649 (se = 0.024 )
## Likelihood ratio test= 31.41 on 9 df, p=3e-04
## Wald test = 28.55 on 9 df, p=8e-04
## Score (logrank) test = 29.22 on 9 df, p=6e-04step(model2, direction = "both")## Start: AIC=1505.92
## Surv(time = begin_all_follow, event = all) ~ auras + age_at_surgery +
## drugs + time_begin + pathology_HS + gtcs + MRI + psychiatric_pre_any +
## duration
##
## Df AIC
## - gtcs 1 1504.0
## - pathology_HS 1 1504.1
## - duration 1 1504.8
## - auras 1 1505.4
## - MRI 1 1505.6
## <none> 1505.9
## - psychiatric_pre_any 1 1507.2
## - time_begin 1 1508.7
## - age_at_surgery 1 1510.8
## - drugs 1 1513.9
##
## Step: AIC=1503.98
## Surv(time = begin_all_follow, event = all) ~ auras + age_at_surgery +
## drugs + time_begin + pathology_HS + MRI + psychiatric_pre_any +
## duration
##
## Df AIC
## - pathology_HS 1 1502.2
## - duration 1 1502.9
## - auras 1 1503.4
## - MRI 1 1503.7
## <none> 1504.0
## - psychiatric_pre_any 1 1505.3
## + gtcs 1 1505.9
## - time_begin 1 1506.8
## - age_at_surgery 1 1508.8
## - drugs 1 1512.0
##
## Step: AIC=1502.19
## Surv(time = begin_all_follow, event = all) ~ auras + age_at_surgery +
## drugs + time_begin + MRI + psychiatric_pre_any + duration
##
## Df AIC
## - duration 1 1500.9
## - auras 1 1501.6
## - MRI 1 1501.7
## <none> 1502.2
## - psychiatric_pre_any 1 1503.7
## + pathology_HS 1 1504.0
## + gtcs 1 1504.1
## - time_begin 1 1505.1
## - age_at_surgery 1 1506.8
## - drugs 1 1510.1
##
## Step: AIC=1500.89
## Surv(time = begin_all_follow, event = all) ~ auras + age_at_surgery +
## drugs + time_begin + MRI + psychiatric_pre_any
##
## Df AIC
## - auras 1 1500.2
## - MRI 1 1500.6
## <none> 1500.9
## + duration 1 1502.2
## - psychiatric_pre_any 1 1502.2
## + gtcs 1 1502.8
## + pathology_HS 1 1502.9
## - time_begin 1 1503.5
## - age_at_surgery 1 1505.4
## - drugs 1 1508.2
##
## Step: AIC=1500.22
## Surv(time = begin_all_follow, event = all) ~ age_at_surgery +
## drugs + time_begin + MRI + psychiatric_pre_any
##
## Df AIC
## - MRI 1 1500.0
## <none> 1500.2
## + auras 1 1500.9
## + duration 1 1501.6
## - psychiatric_pre_any 1 1502.0
## + pathology_HS 1 1502.2
## + gtcs 1 1502.2
## - time_begin 1 1505.5
## - age_at_surgery 1 1505.7
## - drugs 1 1506.9
##
## Step: AIC=1500
## Surv(time = begin_all_follow, event = all) ~ age_at_surgery +
## drugs + time_begin + psychiatric_pre_any
##
## Df AIC
## <none> 1500.0
## + MRI 1 1500.2
## + auras 1 1500.6
## + duration 1 1501.1
## - psychiatric_pre_any 1 1501.5
## + pathology_HS 1 1502.0
## + gtcs 1 1502.0
## - time_begin 1 1505.3
## - age_at_surgery 1 1505.6
## - drugs 1 1507.0## Call:
## coxph(formula = Surv(time = begin_all_follow, event = all) ~
## age_at_surgery + drugs + time_begin + psychiatric_pre_any,
## data = data, x = TRUE, y = TRUE)
##
## coef exp(coef) se(coef) z p
## age_at_surgery -0.023985 0.976301 0.008915 -2.691 0.00713
## drugs -0.333390 0.716491 0.113360 -2.941 0.00327
## time_begin -0.074550 0.928161 0.030598 -2.436 0.01483
## psychiatric_pre_any -0.322484 0.724347 0.175853 -1.834 0.06668
##
## Likelihood ratio test=27.33 on 4 df, p=1.707e-05
## n= 350, number of events= 141model2f <-
coxph(
Surv(time = begin_all_follow, event = all) ~ age_at_surgery + drugs + time_begin + psychiatric_pre_any,
data = data
)
summary(model2f)## Call:
## coxph(formula = Surv(time = begin_all_follow, event = all) ~
## age_at_surgery + drugs + time_begin + psychiatric_pre_any,
## data = data)
##
## n= 350, number of events= 141
##
## coef exp(coef) se(coef) z Pr(>|z|)
## age_at_surgery -0.023985 0.976301 0.008915 -2.691 0.00713 **
## drugs -0.333390 0.716491 0.113360 -2.941 0.00327 **
## time_begin -0.074550 0.928161 0.030598 -2.436 0.01483 *
## psychiatric_pre_any -0.322484 0.724347 0.175853 -1.834 0.06668 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## age_at_surgery 0.9763 1.024 0.9594 0.9935
## drugs 0.7165 1.396 0.5737 0.8948
## time_begin 0.9282 1.077 0.8741 0.9855
## psychiatric_pre_any 0.7243 1.381 0.5132 1.0224
##
## Concordance= 0.64 (se = 0.025 )
## Likelihood ratio test= 27.33 on 4 df, p=2e-05
## Wald test = 25.38 on 4 df, p=4e-05
## Score (logrank) test = 25.83 on 4 df, p=3e-05fitmodel2f <-
cph(
Surv(time = begin_all_follow, event = all) ~ age_at_surgery + drugs + time_begin + psychiatric_pre_any,
data = data,
surv = TRUE,
x = TRUE,
y = TRUE
)
rms::validate(fitmodel2f,
dxy = TRUE,
B = 1000,
u = 5)## index.orig training test optimism index.corrected n
## Dxy 0.2794 0.2900 0.2636 0.0264 0.2530 1000
## R2 0.0761 0.0873 0.0685 0.0189 0.0572 1000
## Slope 1.0000 1.0000 0.9058 0.0942 0.9058 1000
## D 0.0173 0.0202 0.0155 0.0048 0.0126 1000
## U -0.0013 -0.0013 0.0010 -0.0023 0.0010 1000
## Q 0.0186 0.0216 0.0145 0.0070 0.0116 1000
## g 0.5577 0.5989 0.5260 0.0729 0.4848 1000ddist <- datadist(data)
options(datadist = 'ddist')
fitmodel2f$Design$label <-
c(
"Age at surgery (Years)",
"Number of AEDs at time of surgery",
"Time to begin withdrawal (Years from surgery)",
"Presurgical psychiatric comorbidity (Yes=1, No=0)"
)
surv.fitmodel2f <- Survival(fitmodel2f)
nomo2 <-
nomogram(
fitmodel2f,
fun = list(function(x)
surv.fitmodel2f(2, x), function(x)
surv.fitmodel2f(5, x)),
funlabel = c(
"2-years AED freedom (= 1-value)",
"5-years AED freedom (= 1-value)"
),
lp = F,
fun.at = c(0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2)
)
tiff(
"nomo2.2.tiff",
width = cm(17),
height = cm(10),
units = 'cm',
res = 300
)
plot(
nomo2,
cex.axis = 1,
cex.var = 1,
col.grid = gray(c(0.8, 0.95)),
theme(text = element_text(
family = "Times New Roman",
face = "bold",
size = 20
))
)
title(main = "Nomogram predicting risk of achieving complete AED withdrawal after epielpsy surgery", theme(text =
element_text(
family = "Times New Roman",
face = "bold",
size = 20
)))
dev.off## function (which = dev.cur())
## {
## if (which == 1)
## stop("cannot shut down device 1 (the null device)")
## .External(C_devoff, as.integer(which))
## dev.cur()
## }
## <bytecode: 0x7fd90585d4f8>
## <environment: namespace:grDevices>web2 <-
cph(
Surv(time = begin_all_follow, event = all) ~ age_at_surgery + drugs + time_begin + psychiatric_pre_any,
data = data
)
DNbuilder(
web2,
data = data,
clevel = 0.95,
covariate = c("numeric"),
ptype = c("1-st")
)## creating new directory: /Users/carolinaferreiraatuesta/R/DynNomapp## Export dataset: /Users/carolinaferreiraatuesta/R/DynNomapp/dataset.RData## Export functions: /Users/carolinaferreiraatuesta/R/DynNomapp/functions.R## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/README.txt## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/ui.R## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/server.R## writing file: /Users/carolinaferreiraatuesta/R/DynNomapp/global.R