diff --git a/DESCRIPTION b/DESCRIPTION
index 2c9207c..19b1cd2 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: jstable
 Title: Create Tables from Different Types of Regression
-Version: 1.3.1
-Date: 2024-07-17
+Version: 1.3.2
+Date: 2024-07-23
 Authors@R: c(person("Jinseob", "Kim", email = "jinseob2kim@gmail.com", role = c("aut", "cre"),  comment = c(ORCID = "0000-0002-9403-605X")),
              person("Zarathu", role = c("cph", "fnd")),
              person("Yoonkyoung","Jeon", role = c("aut")),
diff --git a/NEWS.md b/NEWS.md
index 8eb7336..4675e62 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,9 +1,12 @@
+# jstable 1.3.2
+
+* Fix: error in subgroup option due to non-existent item in `forestcox`
+
 # jstable 1.3.1
 
 * Fix: `addOverall` options to  `svyCreateTableOneJS` 
 * Fix: `TableSubgroupCox`
 
-
 # jstable 1.3.0
 
 * Update: Add `weights` option to `TableSubgroupCox` and `TableSubgroupMultiCox` for marginal cox model. ex: `weights = "weights"`
diff --git a/R/forestcox.R b/R/forestcox.R
index 17cee2e..00d9b5d 100644
--- a/R/forestcox.R
+++ b/R/forestcox.R
@@ -64,7 +64,7 @@ TableSubgroupCox <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
     if (is.numeric(data[[var_subgroup]])) stop("var_subgroup must categorical.")
     # if (length(levels(data[[as.character(formula[[3]])]])) != 2) stop("Independent variable must have 2 levels.")
   }
-
+  
   ## functions with error
   possible_table <- purrr::possibly(table, NA)
   possible_prop.table <- purrr::possibly(function(x) {
@@ -192,7 +192,6 @@ TableSubgroupCox <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
     stop("Please input correct subgroup variable.")
   } else {
     ### subgroup 지정 한 경우 ###
-
     # 공변량 있는 경우 formula 변경
     if (!is.null(var_cov)) {
       formula <- as.formula(paste0(deparse(formula), " + ", paste(var_cov, collapse = "+")))
@@ -205,7 +204,7 @@ TableSubgroupCox <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
 
     if (any(class(data) == "survey.design")) {
       ### survey data인 경우 ###
-
+      data$variables[[var_subgroup]] <- factor(data$variables[[var_subgroup]])
       data$variables[[var_subgroup]] %>%
         table() %>%
         names() -> label_val
@@ -261,7 +260,7 @@ TableSubgroupCox <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
     } else {
       ### survey data가 아닌 경우 ###
       weights_option <- if (!is.null(weights)) TRUE else FALSE
-
+      data[[var_subgroup]] <- factor(data[[var_subgroup]])
       # Coxph 함수를 각 subgroup에 대해 적용시키기 위한 함수
       run_coxph <- function(subgroup_var, subgroup_value, data, formula, weights_option) {
         subset_data <- data[data[[subgroup_var]] == subgroup_value, ]
@@ -414,7 +413,7 @@ TableSubgroupCox <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
           )
         }) %>%
         Reduce(rbind, .) -> CI
-
+      
       model %>%
         purrr::map(possible_pv) %>%
         purrr::map_dbl(~ round(., decimal.pvalue)) -> pv
@@ -453,11 +452,11 @@ TableSubgroupCox <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
 
     # output 만들기
     if (ncoef < 2) {
-      out <- data.frame(Variable = paste("  ", label_val), Count = Count, Percent = round(Count / sum(Count) * 100, decimal.percent), `Point Estimate` = Point.Estimate, Lower = CI[, 1], Upper = CI[, 2], check.names = F) %>%
+      out <- data.frame(Variable = paste("  ", label_val), Count = Count, Percent = round(Count / sum(Count) * 100, decimal.percent), `Point Estimate` = Point.Estimate, Lower = CI[, 1], Upper = CI[, 2], check.names = F, row.names = NULL) %>%
         mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
 
       if (!is.null(prop)) {
-        out <- data.frame(Variable = paste("  ", label_val), Count = Count, Percent = round(Count / sum(Count) * 100, decimal.percent), `Point Estimate` = Point.Estimate, Lower = CI[, 1], Upper = CI[, 2], check.names = F) %>%
+        out <- data.frame(Variable = paste("  ", label_val), Count = Count, Percent = round(Count / sum(Count) * 100, decimal.percent), `Point Estimate` = Point.Estimate, Lower = CI[, 1], Upper = CI[, 2], check.names = F, row.names = NULL) %>%
           cbind(prop) %>%
           mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
       }