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MDC_v2.Rmd
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---
title: "Multidisciplinary breast program in New York City - Update"
author: "Anni Liu"
date: "`r format(Sys.Date(), '%B %d, %Y')`"
knit: knitautomator::knit_filename
output:
word_document:
fig_height: 4.5
fig_width: 4.5
html_document:
df_print: paged
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
options(scipen = 999, digits = 4),
cache = TRUE,
error = FALSE,
message = FALSE,
warning = FALSE,
tidy.opts = list(width.cutoff = 60),
tidy = TRUE,
fig.width = 12,
fig.height = 8
)
```
# Data preparations
Please notice that the value of column E (`Race Code`) for the patient with ID WCM1260 is changed from `NHW` into `NHB`. The value of column F (`Race 2`) for the patient with ID NYPQ641 is changed from `Non-Asian` into `Southeast Asian`.
```{r include=FALSE}
## Load libraries
easypackages::libraries("tidyverse", "readxl", "gtsummary", "flextable", "stringi", "fst")
```
```{r eval=FALSE, include=FALSE}
## Load WCM 2019-2022 data
wcm_data <- read_xlsx("../data/raw/MDC Asian Patients_4_11_Stats_deidentified.xlsx",
sheet = "WCM 2019-2022",
range = c("A1:T2307"),
col_names = TRUE) %>%
select(-c("Date of Diagnosis", "Year")) %>%
rename("StudyID" = "Patient ID",
"RaceEthnicity.1" = "Race/Ethnicity (if mixed, circle more than one)",
"RaceEthnicity.2" = "Race Code",
"Race.Asian" = "Race Asian",
"Insure.type" = "Type of insurance",
"Age.Dx" = "Age at Diagnosis",
"MMGSD" = "Mammo Screen-Detected",
"MMGO" = "Mammo-Occult",
"MRISD" = "MRI Screen-Detected",
"MMGD" = "Mammographic Density",
"T.stage" = "Clinical T Stage",
"N.stage" = "Clinical N Stage",
"Clinic.stage" = "Clinical Stage",
"ER" = "ER Status",
"PR" = "PR Status",
"HER2" = "HER2 Status") %>%
mutate("Hospital" = "WCM")
## Load Queens 19-22 data
nypq_data <- read_xlsx("../data/raw/MDC Asian Patients_4_11_Stats_deidentified.xlsx",
sheet = "Queens 2019-2022",
range = c("A1:T1163"),
col_names = TRUE) %>%
select(-c("Month", "Year")) %>%
rename("StudyID" = "ID",
"RaceEthnicity.1" = "Race/Ethnicity",
"RaceEthnicity.2" = "Race Code",
"Race.Asian" = "Race 2",
"Insure.type" = "Type of Insurance",
"Age.Dx" = "Age at Diagnosis",
"MMGSD" = "Mammo Screen-Detected",
"MMGO" = "Mammmo-Occult",
"MRISD" = "MRI Screen-Detected",
"MMGD" = "Mammographic Density",
"T.stage" = "Clinical T Stage",
"N.stage" = "Clinical N Stage",
"Clinic.stage" = "Clinical Stage",
"ER" = "ER Status",
"PR" = "PR Status",
"HER2" = "HER2 Status") %>%
mutate("Hospital" = "NYPQ")
## Merge two data
data_full <- rbind(wcm_data, nypq_data)
## Recode variables
##------RaceEthnicity.1------
# table(data_full$RaceEthnicity.1, useNA = "always")
data_full$RaceEthnicity.1 <- gsub("Nhw|NHw", "NHW", # 3rd gsub run
gsub("Asian/Indian/ Pakistani", "Asian/Indian/Pakistani", # 2nd gsub run
gsub("Afghan$", "Afghani", # 1st gsub run
data_full$RaceEthnicity.1)))
##------RaceEthnicity.2------
# Fix the value in `RaceEthnicity.2` of one subject (row 279; WCM1260)
idx <- which(data_full$RaceEthnicity.1 == "NHB" & data_full$RaceEthnicity.2 %in% c("NHW"))
data_full[idx, c("RaceEthnicity.2")] <- "NHB"
data_full$RaceEthnicity.2 <- gsub("As", "AS",
gsub("HiS|His|^HB", "HIS", data_full$RaceEthnicity.2)) |> factor(levels = c("AS", "HIS", "NHB", "NHW", "OTH"),
labels = c("Asian", "Hispanic", "Non-hispanic black", "Non-hispanic white", "Other"))
##------Race.Asian------
# Fix the value in `Race.Asian` of one subject (row 2941; NYPQ641)
table(data_full$Race.Asian, data_full$RaceEthnicity.2)
idx <- with(data_full, which(Race.Asian == "Non-Asian" & RaceEthnicity.2 == "Asian"))
data_full[idx, c("StudyID", "RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian")]
data_full[idx, c("StudyID", "Race.Asian")] <- "Southeast Asian"
data_full$Race.Asian <- factor(data_full$Race.Asian)
##------Language------
data_full$Language <- gsub("Unknown", NA, data_full$Language)
##------BMI------
data_full$BMI <- ifelse(data_full$BMI %in% c("?", "*", "Unknown"), NA, as.numeric(data_full$BMI))
##------Insure.type------
data_full$Insure.type <- with(data_full, case_when(
Insure.type %in% c("Medcaid", "Medicad", "medicaid") ~ "Medicaid",
Insure.type == "medicare" ~ "Medicare",
Insure.type %in% c("private", "PrIvate", "privAte", "PrIvate", "PRIVATE") ~ "Private",
Insure.type == "Unknown" ~ NA,
TRUE ~ Insure.type))
data_full$Insure.type <- factor(
data_full$Insure.type,
levels = c("Medicaid", "Medicare", "Private", "Self Pay", "None"),
ordered = TRUE)
##------MMGSD------
data_full$MMGSD <- gsub("Unknown", NA,
gsub("y.*|Y.*", "Yes",
gsub("^n", "N", data_full$MMGSD))) %>%
factor(levels = c("Yes", "No"))
##------MMGO------
data_full <- data_full %>%
mutate(MMGO = case_when(MMGO %in% c("no", "NO") ~ "No",
MMGO %in% c("Unknown", "N/A") ~ as.character(NA),
TRUE ~ MMGO))
data_full$MMGO <- with(data_full,
factor(MMGO,
levels = c("Yes", "No", NA)))
##------MRISD------
data_full <- data_full %>%
mutate(MRISD = case_when(MRISD %in% c("no", "NO") ~ "No",
MRISD == "Unknown" ~ as.character(NA),
TRUE ~ MRISD))
data_full$MRISD <- factor(data_full$MRISD,
levels = c("Yes", "No"))
##------MMGD------
data_full$MMGD <- gsub("^Almost.*", "Almost entirely fatty",
gsub("^Hetero.*", "Heterogenously dense",
gsub("^Extreme.*", "Extremely dense",
gsub("Unknown", NA, data_full$MMGD))))
data_full$MMGD <- factor(data_full$MMGD,
levels = c("Almost entirely fatty", "Scattered areas of fibrogladular density", "Heterogenously dense", "Extremely dense"))
##------Clinic.stage------
data_full$Clinic.stage <- gsub("Unknown", NA, data_full$Clinic.stage)
data_full$Clinic.stage <- factor(data_full$Clinic.stage, levels = c("0", "I", "II", "III"))
##------T.stage------
data_full$T.stage <- with(data_full, case_when(
T.stage %in% c("NA", "Unknown") ~ as.character(NA),
T.stage %in% c("rcT4c", "T4", "T4a", "T4b", "T4c", "T4d") ~ "T4",
T.stage %in% c("rcTx", "Tx") ~ "TX",
T.stage %in% c("T1c", "T1C", "T1mi", "T1mic", "T1", "T1a", "Tia", "T1b") ~ "T1",
T.stage %in% c("Recurrence pathologic stage T2N3a", "T2") ~ "T2",
T.stage %in% c("T1A", "T1B") ~ "T1",
T.stage %in% c("Tis", "TIs", "Pagets") ~ "Tis",
TRUE ~ T.stage))
data_full$T.stage <- factor(data_full$T.stage, levels = c("TX", "T0", "Tis", "T1", "T2", "T3", "T4"))
##------T.stage.2------
data_full$T.stage.2 <- stri_replace_all_regex(
data_full$T.stage,
pattern = c("TX", "T0|Tis|T1|T2", "T3|T4"),
replacement = c("TX", "T0/Tis/T1/T2", "T3/T4"),
vectorize = FALSE) %>%
factor(levels = c("TX", "T0/Tis/T1/T2", "T3/T4"))
##------T.stage.3------
data_full$T.stage.3 <- stri_replace_all_regex(
data_full$T.stage,
pattern = c("TX|T0", "Tis", "T1|T2", "T3|T4"),
replacement = c("TX/T0", "Tis", "T1/T2", "T3/T4"),
vectorize = FALSE) %>%
factor(levels = c("TX/T0", "Tis", "T1/T2", "T3/T4"))
##------N.stage------
data_full$N.stage <- with(data_full, case_when(
N.stage %in% c("n0") ~ "N0",
N.stage %in% c("1a", "N1", "N1a", "N1mic") ~ "N1",
N.stage %in% c("N2", "N2a", "N2b") ~ "N2",
N.stage %in% c("N3", "N3a", "N3b", "N3c") ~ "N3",
N.stage %in% c("NA", "n/a", "Unknown") ~ as.character(NA),
N.stage %in% c("Nx") ~ "NX",
TRUE ~ N.stage))
data_full$N.stage <- factor(data_full$N.stage,
levels = c("NX", "N0", "N1", "N2", "N3"))
##------N.stage.2------
data_full$N.stage.2 <- stri_replace_all_regex(
data_full$N.stage,
pattern = c("NX", "N0", "N1|N2|N3"),
replacement = c("NX", "N0", "N1/N2/N3"),
vectorize = FALSE) %>%
factor(levels = c("NX", "N0", "N1/N2/N3"))
##------ER------
data_full$ER <- with(data_full, case_when(
ER %in% c("NA", "n/a", "N/A", "Unknown", "unknown") ~ as.character(NA),
ER %in% c("POSITIVE", "positive") ~ "Positive",
ER == "negative" ~ "Negative",
TRUE ~ ER))
data_full$ER <- factor(data_full$ER,
levels = c("Positive", "Negative"))
##------PR------
data_full$PR <- with(data_full, case_when(
PR %in% c("NA", "n/a", "N/A", "Unknown", "unknown") ~ as.character(NA),
PR %in% c("POSITIVE", "positive") ~ "Positive",
PR %in% c("negative", "NEGATIVE") ~ "Negative",
TRUE ~ PR))
data_full$PR <- factor(data_full$PR,
levels = c("Positive", "Negative"))
##------HER2------
data_full$HER2 <- with(data_full, case_when(
HER2 %in% c("NA", "n/a", "N/A", "Unknown", "unknown") ~ as.character(NA),
HER2 %in% c("POSITIVE", "positive") ~ "Positive",
HER2 %in% c("negative", "NEGATIVE") ~ "Negative",
TRUE ~ HER2))
data_full$HER2 <- factor(data_full$HER2,
levels = c("Positive", "Negative"))
##------Hospital------
data_full$Hospital <- factor(data_full$Hospital,
levels = c("NYPQ", "WCM"),
ordered = TRUE)
##------Subtype------
data_full$Subtype <- with(data_full, case_when(
(ER == "Positive" | PR == "Positive") & HER2 == "Negative" ~ "HR+, HER2-",
HER2 == "Positive" ~ "HER2+",
ER == "Negative" & PR == "Negative" & HER2 == "Negative" ~ "Triple negative",
TRUE ~ as.character(NA)))
data_full$Subtype <- with(data_full,
factor(Subtype,
levels = c("HR+, HER2-", "HER2+", "Triple negative")))
```
```{r save_cleaned_data, eval=FALSE, include=FALSE}
date.analysis <- format(Sys.Date(), "%Y%b%d")
write_fst(data_full,
path = paste0("../data/derived/", date.analysis, "_dat_MDCAsian.RData"),
compress = 50, uniform_encoding = T)
write.csv(data_full,
file = paste0("../data/derived/", date.analysis, "_dat_MDCAsian.csv"),
row.names = F)
```
```{r load_cleaned_data, include=FALSE}
data_full <- read_fst(path = "../data/derived/2023Apr24_dat_MDCAsian.RData")
# class(dat.work) # data.frame
```
## Check if Race/Ethnicity is coded consistently between the columns D and E
```{r echo=FALSE}
table(ColumnD = data_full$RaceEthnicity.1, ColumnE = data_full$RaceEthnicity.2, useNA = "ifany") |> knitr::kable()
```
## Check if Race/Ethnicity is coded consistently between the columns E and F
```{r echo=FALSE}
table(ColumnD = data_full$RaceEthnicity.1, ColumnF = data_full$Race.Asian, useNA = "ifany") |> knitr::kable()
table(ColumnE = data_full$RaceEthnicity.2, ColumnF = data_full$Race.Asian, useNA = "ifany") |> knitr::kable()
```
Question:
* Should the patient of Arabic (ID: `r data_full$StudyID[which(data_full$RaceEthnicity.1 == "Arabic" & data_full$Race.Asian %in% c("South Asian"))]`) coded as `Western Asian` or `Asian Other` in the column F?
# Summary tables
## Compare patient characteristics between two hospitals
```{r echo=FALSE, warning=FALSE}
## Table one of all patients
table_one <- tbl_summary(data = data_full %>%
select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Hospital")),
by = "Hospital",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})",
"{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>% # Remove unnecessary footnotes
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 1. Patient characteristics between two hospitals (NYPQ vs WCM).")
table_one
```
## Compare Asian patient characteristics between two hospitals
```{r echo=FALSE}
## Table two of Asian patients
table_two_asian <- tbl_summary(data = data_full %>%
filter(RaceEthnicity.2 == "Asian") %>%
select(c("Age.Dx",
"RaceEthnicity.1", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Hospital")),
by = "Hospital",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 2. Asian patient characteristics between two hospitals (NYPQ vs WCM).")
table_two_asian
```
## Compare Chinese patient characteristics between two hospitals
```{r echo=FALSE}
## Table three of Chinese patients
table_three_chinese <- tbl_summary(data = data_full %>%
filter(RaceEthnicity.1 == "Chinese") %>%
select(c("Age.Dx",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Hospital")),
by = "Hospital",
label = list(Age.Dx ~ "Age at Diagnosis",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})",
"{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 3. Chinese patient characteristics between two hospitals (NYPQ vs WCM).")
table_three_chinese
```
## Compare patient characteristics across race and hospital site (NYPQ Asian vs NYPQ Non-Asian vs WCM Asian vs WCM Non-Asian)
```{r echo=FALSE}
## Create a new variable to record the combined category of Race.2 and Hospital
# data_full <- data_full %>%
# mutate(RaceSite.1 = case_when(
# Race.2 == "Asian" & Hospital == "Queens" ~ "Asian Queens",
# Race.2 == "Asian" & Hospital == "Cornell" ~ "Asian Cornell",
# Race.2 == "Non-Asian" & Hospital == "Queens" ~ "Non-Asian Queens",
# Race.2 == "Non-Asian" & Hospital == "Cornell" ~ "Non-Asian Cornell"))
## Another fast way
data_full$Race.2 <- with(data_full, ifelse(RaceEthnicity.2 == "Asian", "Asian", "Non-Aisan"))
data_full$RaceSite.1 <- with(data_full, paste0(Hospital, ":", Race.2))
data_full$RaceSite.1 <- factor(data_full$RaceSite.1,
levels = c("NYPQ:Asian", "NYPQ:Non-Aisan", "WCM:Asian", "WCM:Non-Aisan"),
ordered = TRUE)
## Table four
table_four <- tbl_summary(data = data_full %>%
select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"RaceSite.1")),
by = "RaceSite.1",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 4. Patient characteristics across race and hospital site (NYPQ Asian vs NYPQ Non-Asian vs WCM Asian vs WCM Non-Asian).")
table_four
```
## Compare patient characteristics across race and hospital site (NYPQ East-Asian vs NYPQ Non-East-Asian vs WCM East-Asian vs WCM Non-East-Asian)
```{r echo=FALSE}
data_full$Race.3 <- with(data_full, ifelse(Race.Asian == "East Asian", "East-Asian", "Non-East-Asian"))
data_full$RaceSite.2 <- with(data_full, paste0(Hospital, ":", Race.3))
# table(data_full$RaceSite.2, useNA = "always")
data_full$RaceSite.2 <- factor(data_full$RaceSite.2,
levels = c("NYPQ:East-Asian", "NYPQ:Non-East-Asian", "WCM:East-Asian", "WCM:Non-East-Asian"),
ordered = TRUE)
## Table five
table_five <- tbl_summary(data = data_full %>%
select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"RaceSite.2")),
by = "RaceSite.2",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 5. Patient characteristics across race and hospital site (NYPQ East-Asian vs NYPQ Non-East-Asian vs WCM East-Asian vs WCM Non-East-Asian).")
table_five
```
## Compare the node-negative breast cancers between Non-Chinese patients at WCM and Chinese patients at WCM
```{r include=FALSE}
data_full$Race.4 <- with(data_full, ifelse(RaceEthnicity.1 != "Chinese", "Non-Chinese", RaceEthnicity.1))
data_full$RaceSite.3 <- with(data_full, paste0(Hospital, ":", Race.4))
data_full$RaceSite.3 <- factor(data_full$RaceSite.3,
levels = c("NYPQ:Chinese", "NYPQ:Non-Chinese", "WCM:Chinese", "WCM:Non-Chinese"),
ordered = TRUE)
## Create two categorical variables
data_full$CornelC <- with(data_full,
case_when(RaceSite.3 == "WCM:Chinese" ~ "Chinese patients at WCM",
RaceSite.3 == "WCM:Non-Chinese" ~ "Non-Chinese patients at WCM",
TRUE ~ as.character(NA))) %>%
factor()
data_full$N.stage.3 <- with(data_full, ifelse(N.stage.2 == "N0", "N0 = Yes", "N0 = No")) %>%
factor(levels = c("N0 = Yes", "N0 = No"))
out.CornelC <- BTKR::fsmry2.by.grp(y = data_full %>% filter(!is.na(CornelC)) %>% dplyr::select("CornelC"),
grp = data_full %>% filter(!is.na(CornelC)) %>% dplyr::select("N.stage.3"),
cmp.method = "chisq")
```
```{r echo=FALSE, results="asis"}
knitr::kable(out.CornelC)
```
## Compare the node-negative breast cancers between Non-Chinese patients at NYPQ and Chinese patients at NYPQ
```{r include=FALSE}
data_full$QueenC <- with(data_full,
case_when(RaceSite.3 == "NYPQ:Chinese" ~ "Chinese patients at NYPQ",
RaceSite.3 == "NYPQ:Non-Chinese" ~ "Non-Chinese patients at NYPQ",
TRUE ~ as.character(NA))) %>%
factor()
out.QueenC <- BTKR::fsmry2.by.grp(y = data_full %>% filter(!is.na(QueenC)) %>% dplyr::select("QueenC"),
grp = data_full %>% filter(!is.na(QueenC)) %>% dplyr::select("N.stage.3"),
cmp.method = "chisq")
```
```{r echo=FALSE, results="asis"}
knitr::kable(out.QueenC)
```
## Compare patient characteristics across race and hospital site (NYPQ Chinese vs NYPQ Non-Chinese vs WCM Chinese vs WCM Non-Chinese)
```{r echo=FALSE}
## Create a new variable to record the combined category of RaceEthnicity.2 and Hospital
# data_full$Race.4 <- with(data_full, ifelse(RaceEthnicity.1 != "Chinese", "Non-Chinese", RaceEthnicity.1))
# data_full$RaceSite.3 <- with(data_full, paste0(Hospital, ":", Race.4))
# data_full$RaceSite.3 <- gsub("Cornell:NA", NA, data_full$RaceSite.3) %>%
# factor(levels = c("Queens:Chinese", "Queens:Non-Chinese", "Cornell:Chinese", "Cornell:Non-Chinese"),
# ordered = TRUE)
# table(data_full$RaceSite.3, useNA = "always")
## Table six
table_six <- tbl_summary(data = data_full %>%
dplyr::select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"RaceSite.3")),
by = "RaceSite.3",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 6. Patient characteristics across race and hospital site (NYPQ Chinese vs NYPQ Non-Chinese vs WCM Chinese vs WCM Non-Chinese).")
table_six
```
## Compare Non-Chinese Asian patient characteristics between two hospitals
```{r echo=FALSE}
## Table seven
table_seven <- tbl_summary(data = data_full %>%
filter(RaceEthnicity.1 != "Chinese" & Race.2 == "Asian") %>%
dplyr::select(c("Age.Dx",
"RaceEthnicity.1", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Hospital")),
by = "Hospital",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 7. Non-Chinese Asian patient characteristics between two hospitals (NYPQ vs WCM).")
table_seven
```
## Compare patient characteristics between Chinese and Non-Chinese
```{r echo=FALSE}
## Table eight
table_eight <- tbl_summary(data = data_full %>%
dplyr::select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"Hospital",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Race.4")),
by = "Race.4",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
Hospital ~ "Hospital Site, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 8. Patient characteristics between Chinese and Non-Chinese.")
table_eight
```
## Compare East-Asian patient characteristics between two hospitals
```{r echo=FALSE}
table_nine <- tbl_summary(data = data_full %>%
filter(Race.Asian == "East Asian") %>%
dplyr::select(c("Age.Dx",
"RaceEthnicity.1",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Hospital")),
by = "Hospital",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 9. East-Asian patient characteristics between NYPQ and WCM.")
table_nine
```
## Compare Chinese and Non-Chinese patient characteristics at WCM
```{r echo=FALSE}
table_ten <- tbl_summary(data = data_full %>%
filter(Hospital == "WCM") %>%
dplyr::select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Race.4")),
by = "Race.4",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 10. Chinese and Non-Chinese patient characteristics at WCM.")
table_ten
```
## Compare Chinese and Non-Chinese patient characteristics at NYPQ
```{r echo=FALSE}
table_eleven <- tbl_summary(data = data_full %>%
filter(Hospital == "NYPQ") %>%
dplyr::select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Race.4")),
by = "Race.4",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",
Subtype ~ "Tumor Subtype, n (%)"),
type = all_continuous() ~ "continuous2",
statistic = list(all_continuous() ~ c("{median} ({min}, {max})", "{mean}+/-{sd}")),
missing_text = "Missing, n") %>%
modify_table_body(
dplyr::mutate,
label = case_when(label == "Median (Range)" ~ "Median (range)",
label == "Mean+/-SD" ~ "Mean+/-sd",
TRUE ~ label)) %>%
modify_footnote(update = everything() ~ NA) %>%
bold_labels() %>%
add_p(pvalue_fun = function(x) style_pvalue(x, digits = 3),
test.args = all_tests("fisher.test") ~ list(simulate.p.value = TRUE, B = 5000)) %>%
as_flex_table() %>%
bold(bold = TRUE, part = "header") %>%
set_caption(caption = "Table 11. Chinese and Non-Chinese patient characteristics at NYPQ.")
table_eleven
```
## Compare Non-Chinese patient characteristics between two hospitals
```{r echo=FALSE}
table_twelve <- tbl_summary(data = data_full %>%
filter(Race.4 == "Non-Chinese") %>%
dplyr::select(c("Age.Dx",
"RaceEthnicity.1", "RaceEthnicity.2", "Race.Asian",
"Language",
"BMI",
"Insure.type",
"MMGSD", "MMGO", "MRISD", "MMGD",
"T.stage", "T.stage.2", "T.stage.3", "N.stage", "N.stage.2", "Clinic.stage",
"ER", "PR", "HER2", "Subtype",
"Hospital")),
by = "Hospital",
label = list(Age.Dx ~ "Age at Diagnosis",
RaceEthnicity.1 ~ "Race/Ethnicity 1, n (%)",
RaceEthnicity.2 ~ "Race/Ethnicity 2, n (%)",
Race.Asian ~ "Race/Ethnicity 3, n (%)",
Language ~ "Patient Language, n (%)",
BMI ~ "Body Mass Index",
Insure.type ~ "Type of Insurance, n (%)",
MMGSD ~ "Mammo Screen-Detected, n (%)",
MMGO ~ "Mammo-Occult, n (%)",
MRISD ~ "MRI Screen-Detected, n (%)",
MMGD ~ "Mammographic Density, n (%)",
T.stage ~ "Clinical T Stage, n (%)",
T.stage.2 ~ "Clinical T Stage 2, n (%)",
T.stage.3 ~ "Clinical T Stage 3, n (%)",
N.stage ~ "Clinical N Stage, n (%)",
N.stage.2 ~ "Clinical N Stage 2, n (%)",
Clinic.stage ~ "Clinical Stage, n (%)",
ER ~ "Estrogen Receptor, n (%)",
PR ~ "Progesterone Receptor, n (%)",
HER2 ~ "Human Epidermal Growth Factor Receptor 2, n (%)",