MaxStudent
Reputation: 93
Get a distinct risk table for univariate and one for multivariate survival analyses
Using the code below, I have managed to create a univariate survival analysis from ggsurvplot using the survminer package.
I have been able to generate a univariate analysis with cox regression and plot using ggsurvplot, but when I run my multivariate analysis (that includes the covariables: age + sex + smoking_status + match_RF_HF + match_RF_AMI + match_RF_T2D + match_RF_HTA + match_RF_MI, I get the same table risk table with the same values, whilst the risk table should be different.
Here is the code to get the univariate analysis:
# your custom theme; unchanged
custom_theme <- function(){
theme_survminer() %+replace%
theme(
legend.background = element_rect(fill = "white", color = "black"),
plot.title=element_text(hjust=0.4)
)
}
# Apply filters to create the filtered dataset
#filtered_data <- df_ukb %>%
# filter(is.na(time_diff_MACE_months) | time_diff_MACE_months >= 0) %>% # remove time diff negative
#filter(time_diff_MACE_months <= N * 12) %>% ## remove time diff more than N follow up
#filter(time_diff_MACE_months >= 1) %>% ## remove AF proc within 1 month after AF
#filter(dateAF_plusNy <= Sys.Date()) ##remove N more than today
filtered_data <- df_ukb %>%
filter(is.na(time_diff_MACE_months) | time_diff_MACE_months >= 0) %>% # remove time diff negative
filter(time_diff_MACE_months <= N * 12) %>% ## remove time diff more than N follow up
filter(time_diff_MACE_months >= 1) %>% ## remove AF proc within 1 month after AF
filter(dateAF_plusNy <= Sys.Date())
## specific order
scoreMVPA_order <- c("no AF proc + inactive", "no AF proc + active", "AF proc + inactive", "AF proc + active")
filtered_data <-
filtered_data %>%
mutate(scoreMVPA = fct_relevel(scoreMVPA,scoreMVPA_order))
# Create the formula
filtered_data$event <- filtered_data$match_MACE
filtered_data$event_followup <- filtered_data$time_diff_MACE_months
formula1 <- as.formula("Surv(event_followup, event) ~ scoreMVPA")
# Fit Cox proportional hazards model with reference group
cox1 <- coxph(Surv(event_followup, event) ~ scoreMVPA, data = filtered_data)
cox1 <- coxph(formula1, data = filtered_data)
cox1
coxP1 <- data.frame(summary(cox1)$coefficients)[,5]
coxP1
# Generate the confidence intervals and p-values with renamed scores
coxConf1 <- data.frame(summary(cox1)$conf.int) %>%
rownames_to_column() %>%
mutate(p = coxP1,
p2 = case_when(
round(p, 3) > p ~ '=',
round(p, 3) < p ~ '=',
round(p, 3) == p ~ '='
),
p3 = ifelse(round(p, 2) == 1, T, F),
tag = paste0(rowname %>% gsub("(\\D)(\\d)", "\\1 \\2", .),
". HR ", exp.coef. %>% sprintf(fmt = "%.2f", .),
" (95% CI ",
lower..95 %>% sprintf(fmt = "%.2f", .),
"-", upper..95 %>% sprintf(fmt = "%.2f", .),
"), ",
ifelse(p < 0.001, "P<0.001",
ifelse(p >= 0.01 & p < 0.05, "P<0.05",
ifelse(p >= 0.05, paste0("P", p2, sprintf(fmt = "%.2f", round(p, 2))),
paste0("P", p2, sprintf(fmt = "%.3f", p))))))) %>%
select(tag) %>%
rename(new_variable_name = tag)
coxConf1$new_variable_name <- gsub("scoreMVPA", "", coxConf1$new_variable_name)
# Remove leading and trailing spaces
coxConf1$new_variable_name <- trimws(coxConf1$new_variable_name)
# Validate the result
coxConf1
# model to plot-
###----
fit1 <- survfit(Surv(event_followup, event) ~ scoreMVPA, data = filtered_data)
# Generate the plot with the reordered legends
uni <- ggsurvplot(fit1,
data = uni_data,
test.for.trend = TRUE,
conf.int = FALSE,
censor = FALSE,
ggtheme = custom_theme(),
legend = c(0.4, 0.25),
font.legend = c(size = 12),
legend.title = element_blank(),
legend.labs = c("no AF proc + inactive. ", unlist(coxConf1)),
xlab = "Follow-up (months)",
font.x = c(size = 12),
risk.table = T,
risk.table.side = "left",
risk.table.y.text = F,
risk.table.title = "Risk Table",
risk.table.col = 1,
ylab = "Free from MACE",
font.y = c(size = 12),
ylim = c(0, 1.05),
xlim = c(0, N * 12),
break.y.by = 0.2,
break.x.by = 6,
axes.offset = TRUE)
And the code for the multivariate analysis:
##MULTIVARIATE ANALYSIS
##covariables
# Define covariates: "age", "sex", "smoking_status", "match_RF_HF", "match_RF_AMI", "match_RF_T2D", "match_RF_HTA", "match_RF_MI"
covariates <- c("age", "sex", "smoking_status", "match_RF_HF", "match_RF_AMI", "match_RF_T2D", "match_RF_HTA", "match_RF_MI")
# Covariate formula
covariate_formula <- as.formula(paste("Surv(event_followup, event) ~ scoreMVPA +", paste(covariates, collapse = "+"))) ###I have tried to combine my covariates but I am not able to
# Apply filters in Cox proportional hazards model
cox2 <- coxph(Surv(event_followup, event) ~ age + sex + smoking_status + match_RF_HF + match_RF_AMI + match_RF_T2D + match_RF_HTA + match_RF_MI + strata(scoreMVPA),
data=filtered_data)
coxP2 <- data.frame(summary(cox2)$coefficients)[,5]
#create theme
coxConf2 <- data.frame(summary(cox2)$conf.int) %>%
rownames_to_column() %>%
mutate(p = coxP2,
p2 = case_when(
round(p, 3) > p ~ '=',
round(p, 3) < p ~ '=',
round(p, 3) == p ~ '='
),
p3 = ifelse(round(p, 2) == 1, T, F),
tag = paste0(rowname %>% gsub("(\\D)(\\d)", "\\1 \\2", .),
". HR ", exp.coef. %>% sprintf(fmt = "%.2f", .),
" (95% CI ",
lower..95 %>% sprintf(fmt = "%.2f", .),
"-", upper..95 %>% sprintf(fmt = "%.2f", .),
"), ",
ifelse(p < 0.001, "P<0.001",
ifelse(p >= 0.01 & p < 0.05, "P<0.05",
ifelse(p >= 0.05, paste0("P", p2, sprintf(fmt = "%.2f", round(p, 2))),
paste0("P", p2, sprintf(fmt = "%.3f", p))))))) %>%
select(tag) %>%
rename(new_variable_name = tag)
coxConf2$new_variable_name <- gsub("scoreMVPA", "", coxConf2$new_variable_name)
# Remove leading and trailing spaces
coxConf2$new_variable_name <- trimws(coxConf2$new_variable_name)
coxConf2 <- coxConf2 %>% slice(1:3)
##
survfit2 <- survfit(cox2)
# Generate the plot with the reordered legends
multi <- ggsurvplot(survfit2,
data = multi_data,
test.for.trend = TRUE,
conf.int = FALSE,
censor = FALSE,
ggtheme = custom_theme(),
legend = c(0.4, 0.25),
font.legend = c(size = 12),
legend.title = element_blank(),
legend.labs = c("no AF proc + inactive. ", unlist(coxConf2)),
xlab = "Follow-up (months)",
font.x = c(size = 12),
risk.table = TRUE,
risk.table.side = "left",
risk.table.y.text = FALSE,
risk.table.title = "Risk Table",
risk.table.col = 1,
ylab = "Free from MACE",
font.y = c(size = 12),
ylim = c(0, 1.05),
xlim = c(0, N * 12),
break.y.by = 0.2,
break.x.by = 6,
axes.offset = TRUE)
Here is a sample of my dataset:
structure(list(sex = c("Male", "Female", "Male", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Female", "Male", "Male",
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Male", "Male", "Male", "Male", "Female", "Female",
"Male", "Female", "Female", "Male", "Male", "Female", "Female",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Female", "Female", "Male", "Male", "Female",
"Female", "Female", "Male", "Female", "Female", "Male", "Male",
"Female", "Male", "Female", "Male", "Male", "Male", "Male", "Male",
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Male",
"Female", "Female", "Female", "Male", "Male", "Male", "Female",
"Male", "Male", "Female", "Male", "Male", "Female", "Male", "Male",
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Female",
"Female", "Male", "Male", "Female", "Male", "Male", "Female",
"Female", "Female", "Male", "Male", "Male", "Male", "Male", "Male",
"Male", "Female", "Male", "Male", "Female", "Male", "Female",
"Male", "Male", "Male", "Female", "Male", "Male", "Male", "Male",
"Female", "Female", "Male", "Female", "Male", "Male", "Male",
"Male", "Male", "Male", "Female", "Female", "Male", "Male", "Male",
"Male", "Female", "Male", "Female", "Male", "Male", "Female",
"Female", "Male", "Male", "Female", "Male", "Male", "Female",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Female",
"Male", "Male", "Female", "Male", "Male", "Female", "Female",
"Male", "Male", "Female", "Female", "Female", "Female"), age = c(41,
58, 63, 65, 62, 66, 68, 48, 68, 59, 63, 67, 64, 66, 52, 52, 69,
64, 61, 67, 65, 67, 61, 62, 58, 54, 53, 66, 67, 67, 64, 65, 64,
69, 63, 51, 67, 65, 68, 64, 65, 58, 62, 65, 67, 63, 60, 52, 69,
69, 54, 53, 55, 68, 61, 46, 67, 67, 57, 55, 61, 65, 61, 66, 48,
47, 69, 55, 58, 69, 64, 53, 65, 64, 62, 68, 64, 65, 60, 58, 54,
43, 51, 69, 60, 69, 70, 64, 59, 65, 61, 69, 67, 63, 69, 58, 55,
61, 63, 64, 65, 69, 62, 69, 61, 65, 60, 53, 41, 56, 61, 65, 65,
68, 65, 51, 69, 52, 61, 54, 44, 63, 55, 62, 62, 68, 54, 63, 41,
69, 67, 50, 65, 60, 59, 67, 66, 61, 67, 47, 63, 66, 65, 64, 57,
52, 65, 65, 60, 44, 48, 67, 43, 63, 46, 59, 63, 66, 67, 62, 62,
59, 70, 64, 65, 63, 62, 62, 42, 60, 62, 61, 64, 61, 60, 68, 64,
56, 66, 61, 69, 62, 58, 59, 60, 57, 69, 47, 69, 51, 43, 63, 63,
64, 68, 64, 60, 70, 66, 62), smoking_status = c(0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0), match_MACE = c(0, 0, 1, 1, 0, 0, 0, 0, 1,
1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,
1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,
0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0,
0, 0), time_diff_MACE_months = c(72, 72, 28.4, 59.4, 72, 72,
72, 72, 68.9, 11.4, 72, 72, 11.7, 7.8, 1.5, 72, 1.7, 16.8, 72,
72, 72, 26.1, 72, 72, 62.3, 72, 72, 72, 38.9, 72, 72, 49.8, 72,
72, 13, 26.4, 72, 72, 72, 72, 72, 18.8, 24, 49.9, 25.1, 72, 16.4,
72, 27, 72, 72, 72, 72, 72, 72, 72, 3.6, 72, 72, 72, 72, 40.8,
72, 1.6, 72, 72, 69, 3.4, 72, 28.1, 72, 72, 52.8, 72, 39.6, 72,
59.9, 72, 72, 72, 72, 72, 72, 6.8, 72, 37.9, 72, 9.3, 18.7, 5.9,
72, 37.1, 72, 72, 5.9, 72, 72, 20.4, 45.6, 68.5, 72, 72, 25.4,
72, 72, 18.5, 72, 72, 72, 10.1, 24, 72, 72, 72, 72, 72, 11.3,
72, 72, 72, 72, 41.6, 72, 72, 72, 66.6, 24.1, 72, 72, 8, 72,
1.2, 72, 72, 72, 2.1, 30.6, 72, 10, 72, 55.5, 20.9, 67, 11.7,
4, 72, 18, 14, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 7.7, 72,
72, 72, 72, 64.3, 56.8, 2.6, 72, 3.9, 72, 72, 72, 72, 72, 72,
72, 37.9, 22.6, 72, 72, 30.4, 72, 72, 72, 72, 34.5, 72, 8, 72,
24.3, 72, 13.5, 9.3, 12, 72, 3, 1.3, 72, 72, 72, 72), match_RF_stroke = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), match_RF_HF = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), match_RF_AMI = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), match_RF_T2D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), match_RF_HTA = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), match_RF_MI = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), scoreMVPA = structure(c(1L, 1L, 2L, 1L, 3L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 4L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 4L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 4L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 4L, 2L, 1L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 4L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 1L,
2L, 2L, 4L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 3L), levels = c("no AF proc + inactive", "no AF proc + active",
"AF proc + inactive", "AF proc + active"), class = "factor")), row.names = c(NA,
-200L), class = c("tbl_df", "tbl", "data.frame"))
I hope it makes sense.
Upvotes: 1
Views: 62
Answers (0)
Related Questions
- Combine and copy columns on condition
- How to get footnotes under the risk table (not in the middle of Kaplan Meier graph and risk table) in R?
- How to get univariate + multivariate survival analysis with R?
- Generating a risk table from survival package
- Stop clipping ggsurvplot risk table text while annotating right-side of survival graph (long labels)
- Stop clipping ggsurvplot risk table text while annotating right-side of survival graph (short labels)
- ggsurvplot risk table misaligned after annotating to the right of survival graph