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Micromets_v1.Rmd
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---
title: "Micrometastatic Axillary Nodal Disease Project"
author: "Anni Liu"
date: "`r format(Sys.Date(), '%B %d, %Y')`"
knit: knitautomator::knit_filename
output:
word_document:
fig_caption: no
fig_height: 4
fig_width: 4
highlight: null
toc: yes
reference_docx: manuscript_style_V0.docx
html_document: default
params:
date.analysis: !r format(Sys.Date(), "%Y%B%D")
plot.fig: TRUE
results.folder: FALSE
editor_options:
chunk_output_type: console
---
```{r shortcut, include=FALSE}
#################################################################
## RStudio keyboard shortcut ##
#################################################################
# Cursor at the beginning of a command line: Ctrl+A
# Cursor at the end of a command line: Ctrl+E
# Clear all the code from your console: Ctrl+L
# Create a pipe operator %>%: Ctrl+Shift+M (Windows) or Cmd+Shift+M (Mac)
# Knit a document (knitr): Ctrl+Shift+K (Windows) or Cmd+Shift+K (Mac)
# Comment or uncomment current selection: Ctrl+Shift+C (Windows) or Cmd+Shift+C (Mac)
```
```{r analysis_plan, include=FALSE}
#################################################################
## Analysis plan ##
#################################################################
##------New update------
# Use the Axillary micromets v3.xlsx dataset, which excludes patients with bilateral cancer at the time of diagnosis and those who received neoadjuvant therapy. The sample size is 102.
##------Previous update------
# Use the univariate log-rank test and Cox-proportional hazards model to investigate the association between each potential predictor (i.e., age, gender, adjuvant radiation) on the composite outcomes of interest (i.e., axillary/local/distant recurrence and death)
## Review the distributions of recurrence and death
## table(dat0$Axillary_recurrence_2 , dat0$Death_2, useNA = "ifany")
## No Yes
## No 115 5
## Yes 1 1
## <NA> 6 0
## table(dat0$Local_recurrence_2, dat0$Death_2, useNA = "ifany")
## No Yes
## No 108 4
## Yes 8 2
## <NA> 6 0
## table(dat0$Distant_recurrence_2, dat0$Death_2, useNA = "ifany")
## No Yes
## No 106 0
## Yes 11 6
## <NA> 5 0
##
## Add the following variables:
## ARDeath: 1 if Axillary_recurrence_2 == "Yes" or Death_2 == "Yes"
## 0 if Axillary_recurrence_2 == "No" and Death_2 == "No"
## NA if Axillary_recurrence_2 == NA
## LRDeath: 1 if Local_recurrence_2 == "Yes" or Death_2 == "Yes"
## 0 if Local_recurrence_2 == "No" and Death_2 == "No"
## NA if Local_recurrence_2 == NA
## DRDeath: 1 if Distant_recurrence_2 == "Yes" or Death_2 == "Yes"
## 0 if Distant_recurrence_2 == "No" and Death_2 == "No"
## NA if Distant_recurrence_2 == NA
## T2ARDeath: `Time_to_AXR` if Axillary_recurrence_2 == "Yes" and Death_2 == "No"
## `Time_to_AXR` if Axillary_recurrence_2 == "Yes" and Death_2 == "Yes"
## `Time_to_death` if Death_2 == "Yes" and Axillary_recurrence_2 == "No"
## `Follow_up_time..days.` if ARDeath == 0
## NA if ARDeath == NA
## T2LRDeath: `Time_to_LR` if Local_recurrence_2 == "Yes" and Death_2 == "No"
## `Time_to_LR` if Local_recurrence_2 == "Yes" and Death_2 == "Yes"
## `Time_to_death` if Death_2 == "Yes" and Local_recurrence_2 == "No"
## `Follow_up_time..days.` if LRDeath == 0
## NA if LRDeath == NA
## T2DRDeath: `Time_to_DR` if Distant_recurrence_2 == "Yes" and Death_2 == "No"
## `Time_to_DR` if Distant_recurrence_2 == "Yes" and Death_2 == "Yes"
## `Time_to_death` if Death_2 == "Yes" and Distant_recurrence_2 == "No"
## `Follow_up_time..days.` if DRDeath == 0
## NA if DRDeath == NA
## Note: 1 means the composite endpoint occurs; 0 means the censoring occurs
```
```{r attach_libraries, include=FALSE}
##################################################################
## Attach libraries ##
##################################################################
easypackages::libraries("BTKR", "multcomp", "readxl", "tidyverse",
"bannerCommenter", "survival")
source("../BTKR_function/fsmry.dmgrph.rowprop.R")
```
```{r check_data, include=FALSE}
##################################################################
## Load data for checking ##
##################################################################
dat0 = dat.work = dget("../data/derived/2022Dec24_dat_micromets.Rdata")
```
```{r global_options, include=FALSE}
#################################################################
## Automator ##
#################################################################
if (params$plot.fig) {
dir.fig <- paste0("../report/figs_", params$date)
if (!dir.exists(dir.fig)) {
# If the figure directory does not exist, we create a new directory under the folder report using the name figs + current date passed from the params$date in YAML
dir.create(dir.fig)
}
knitr::opts_chunk$set( # Setting parameters when figures are plotted
fig.width = 6, fig.height = 6, fig.path = dir.fig, dev = "png", dpi = 300,
echo = FALSE, warning = FALSE, message = FALSE,
cache = FALSE,
comment = ""
)
} else { # Setting parameters when figures are not plotted
knitr::opts_chunk$set(
echo = FALSE, warning = FALSE, message = FALSE,
cache = FALSE,
comment = ""
)
}
if (params$results.folder) { # Suitable when the results need to be stored outside the microsoft word report
dir.result <- paste0("../report/results_", params$date)
if (!dir.exists(dir.result)) {
# If the directory does not exist, we create a new directory under the folder report using the name results + current date passed from the params$date in YAML
dir.create(dir.result)
}
}
loc.perl <- "C:/cygwin64/bin/perl"
```
# Data preparation
In our univariate survival analysis, the axillary/local/distant recurrence-free survival time is defined as a composite endpoint which combines the time to axillary/local/distant recurrence, time to death, and time to last follow-up into a univariate time to the first event or censoring. To illustrate how this combination of time works, we enumerate different scenarios for calculating the axillary recurrence-free survival time. The same logic applies to the other two types of recurrence (e.g., local recurrence and distant recurrence).
For axillary recurrence-free survival time:
• Given that 1 patient has the axillary recurrence but does not die, the time to axillary recurrence is treated as the survival time.
• Given that 5 patients die without the axillary recurrence, the time to death is treated as their survival time.
• Given that 1 patient first has the axillary recurrence and then dies, the time to axillary recurrence is treated as the survival time.
• Given that 115 patients neither have the axillary recurrence or die, the time to last follow-up is treated as their survival time.
• Given that 6 patients die but miss the information on the axillary recurrence, their survival time is treated as not available.
```{r, clean_data_start, eval=FALSE}
##################################################################
## Clean data ##
##################################################################
# table(dat0$Age_Dx_Grp_2, useNA = "ifany")
dat0 <- readxl::read_xlsx(
"../data/raw/Axillary micromets\ v3.xlsx", # \ escape
sheet = "Coded v3",
range = c("A1:BA129"),
col_names = T,
na = c("Unknown", "unknown", "N/A", "n/a")
) %>%
mutate(
Age_dx_grp_1 = case_when(Age_Dx_10yr_Groups == 2 ~ "20-29",
Age_Dx_10yr_Groups == 3 ~ "30-39",
Age_Dx_10yr_Groups == 4 ~ "40-49",
Age_Dx_10yr_Groups == 5 ~ "50-59",
Age_Dx_10yr_Groups == 6 ~ "60-69",
Age_Dx_10yr_Groups == 7 ~ "70-79",
Age_Dx_10yr_Groups == 8 ~ "80-89",
Age_Dx_10yr_Groups == 9 ~ "90+") %>%
factor(levels = c("20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+")
)) %>%
mutate(
Age_dx_grp_2 = ifelse(Age_Dx < 40, "<40", "≥40") %>%
factor(levels = c("<40", "≥40")
)) %>%
mutate(
Age_dx_grp_3 = ifelse(Age_Dx < 45, "<45", "≥45") %>%
factor(levels = c("<45", "≥45")
)) %>%
mutate(
Age_dx_grp_4 = ifelse(Age_Dx < 50, "<50", "≥50") %>%
factor(levels = c("<50", "≥50")
)) %>%
mutate(
Race_2 = case_when(Race == 0 ~ "Asian",
Race == 1 ~ "Non-hispanic black",
Race == 2 ~ "Non-hispanic white",
Race == 3 ~ "Hispanic",
Race == 4 ~ "Other",
Race == 5 ~ as.character(NA)) %>%
factor(levels = c("Asian", "Non-hispanic black", "Non-hispanic white", "Hispanic", "Other")
)) %>%
mutate(
Sex_2 = ifelse(Sex == 0, "Female", "Male") %>%
factor(levels = c("Female", "Male")
)) %>%
mutate(
Menopausal_2 = case_when(Menopausal == 0 ~ "Pre",
Menopausal == 1 ~ "Post",
Menopausal == 2 ~ as.character(NA)) %>%
factor(levels = c("Pre", "Post")
)) %>%
mutate(
Screened_2 = case_when(Screened == 0 ~ "No",
Screened == 1 ~ "Yes",
Screened == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Multiple_index_2 = ifelse(Multiple_index == 0, "No", "Yes") %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Bilateral_cancer_2 = ifelse(Bilateral_Cancer == 0, "No", "Yes") %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Index_status_2 = ifelse(Idx_status == 0, "New primary cancer", "Local recurrence") %>%
factor(levels = c("New primary cancer", "Local recurrence")
)) %>%
mutate(
Prior_breast_cancer_2 = ifelse(Prior_breast_Ca == 0, "No", "Yes") %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Tumor_type_2 = case_when(Tumor_type == 0 ~ "DCIS",
Tumor_type == 1 ~ "IDC",
Tumor_type == 2 ~ "ILC",
Tumor_type == 3 ~ "IDC/ILC",
Tumor_type == 4 ~ "Mammary",
Tumor_type == 5 ~ "Other") %>%
factor(levels = c("DCIS", "IDC", "ILC", "IDC/ILC", "Mammary", "Other")
)) %>%
mutate(
Nodal_mets_dx_2 = ifelse(`Nodal_mets _at_Dx` == 0, "No", "Yes") %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Histo_grade_2 = case_when(Histo_grade == 1 ~ "Grade1",
Histo_grade == 2 ~ "Grade2",
Histo_grade == 3 ~ "Grade3",
Histo_grade == 4 ~ as.character(NA)) %>%
factor(levels = c("Grade1", "Grade2", "Grade3")
)) %>%
mutate(
ER_2 = case_when(ER == 0 ~ "Negative",
ER == 1 ~ "Positive",
ER == 2 ~ as.character(NA)) %>%
factor(levels = c("Negative", "Positive")
)) %>%
mutate(
PR_2 = case_when(PR == 0 ~ "Negative",
PR == 1 ~ "Positive",
PR == 2 ~ as.character(NA)) %>%
factor(levels = c("Negative", "Positive")
)) %>%
mutate(
HER2_2 = case_when(HER2 == 0 ~ "Negative",
HER2 == 1 ~ "Positive",
HER2 == 2 ~ as.character(NA)) %>%
factor(levels = c("Negative", "Positive")
)) %>%
mutate(
TNBC = case_when(
# Check the distributions of estrogen receptor, progesterone receptor, and protein HER2
# with(dat0, table(ER_2, PR_2, HER2_2, useNA = "ifany"))
is.na(ER_2) & is.na(PR_2) & HER2_2 %in% c("Negative", NA) ~ as.character(NA),
ER_2 == "Negative" & PR_2 == "Negative" & HER2_2 == "Negative" ~ "Triple negative",
TRUE ~ "Non-triple negative") %>%
factor(levels = c("Triple negative", "Non-triple negative")
)) %>%
mutate(
Ki67_2 = case_when(Ki_67 == 0 ~ "Ki67≤10%",
Ki_67 == 1 ~ "Ki67>10%",
Ki_67 == 2 ~ as.character(NA)) %>%
factor(levels = c("Ki67≤10%", "Ki67>10%")
)) %>%
mutate(
LVI_2 = case_when(LVI == 0 ~ "No",
LVI == 1 ~ "Yes",
LVI == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
ALND_2 = case_when(Completion_ALND == 0 ~ "No",
Completion_ALND == 1 ~ "Yes",
Completion_ALND == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
N_clinical_2 = gsub("c1|c2A", "c1/c2", `N Class - Clinical`) %>%
factor(levels = c("c0", "c1/c2", "cX"),
labels = c("cN0", "cN1/cN2", "cNX") # Large sample size -> reference group
)) %>%
mutate(
T_clinical_2 = gsub("1|c0|c1|c1A|c1b|c1B|c1c|c1C|c1mi|c2|cTis|pIS|Tis", "T0/Tis/T1/T2",
gsub("c3|c4D", "T3/T4",
gsub("cx|cX", "TX", `T Class - Clinical`))) %>%
factor(levels = c("T0/Tis/T1/T2", "T3/T4", "TX")) # Large sample size -> reference group
) %>%
mutate(
Neoadjuvant_chemo_2 = case_when(Neoadjuvent_Chemo == 0 ~ "No",
Neoadjuvent_Chemo == 1 ~ "Yes",
Neoadjuvent_Chemo == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Neoadjuvant_endocrine_2 = case_when(Neoadjuvent_endocrine == 0 ~ "No",
Neoadjuvent_endocrine == 1 ~ "Yes",
Neoadjuvent_endocrine == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Neoadjuvant_treatment = case_when(Neoadjuvant_chemo_2 == "Yes" ~ "ChemoYes",
Neoadjuvant_endocrine_2 == "Yes" ~ "EndocrineYes",
Neoadjuvant_chemo_2 == "Yes" & Neoadjuvant_endocrine_2 == "Yes" ~ "Both",
Neoadjuvant_chemo_2 == "No" & Neoadjuvant_endocrine_2 == "No" ~ "Neither") %>%
factor(levels = c("ChemoYes", "EndocrineYes", "Both", "Neither")
)) %>%
mutate(
Neoadjuvant_antiHER2_2 = case_when(`Neoadjuvent_anti-HER2` == 0 ~ "No",
`Neoadjuvent_anti-HER2` == 1 ~ "Yes",
`Neoadjuvent_anti-HER2` == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Neoadjuvant_XRT_2 = case_when(Neoadjuvent_XRT == 0 ~ "No",
Neoadjuvent_XRT == 1 ~ "Yes",
Neoadjuvent_XRT == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Adjuvant_chemo_2 = case_when(Adjuvent_Chemo == 0 ~ "No",
Adjuvent_Chemo == 1 ~ "Yes",
Adjuvent_Chemo == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Adjuvant_endocrine_2 = case_when(Adjuvent_Endocrine == 0 ~ "No",
Adjuvent_Endocrine == 1 ~ "Yes",
Adjuvent_Endocrine == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Adjuvant_treatment = case_when(Adjuvant_chemo_2 == "Yes" ~ "ChemoYes",
Adjuvant_endocrine_2 == "Yes" ~ "EndocrineYes",
Adjuvant_chemo_2 == "Yes" & Adjuvant_endocrine_2 == "Yes" ~ "Both",
Adjuvant_chemo_2 == "No" & Adjuvant_endocrine_2 == "No" ~ "Neither") %>%
factor(levels = c("ChemoYes", "EndocrineYes", "Both", "Neither")
)) %>%
mutate(
Adjuvant_antiHER2_2 = case_when(`Adjuvent_anti-HER2` == 0 ~ "No",
`Adjuvent_anti-HER2` == 1 ~ "Yes",
`Adjuvent_anti-HER2` == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Adjuvant_XRT_2 = case_when(Adjuvent_XRT == 0 ~ "No",
Adjuvent_XRT == 1 ~ "Yes",
Adjuvent_XRT == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Local_recurrence_2 = case_when(`Local recurrence` == 0 ~ "No",
`Local recurrence` == 1 ~ "Yes",
`Local recurrence` == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Axillary_recurrence_2 = case_when(`Axillary Recurrence` == 0 ~ "No",
`Axillary Recurrence` == 1 ~ "Yes",
`Axillary Recurrence` == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Distant_recurrence_2 = case_when(DR == 0 ~ "No",
DR == 1 ~ "Yes",
DR == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
New_breast_cancer_2 = case_when(NewBC == 0 ~ "No",
NewBC == 1 ~ "Yes",
NewBC == 2 ~ as.character(NA)) %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
Addition_pos_node_2 = ifelse(`how many additional positive nodes removed` %in% c(NA, 0), "No", "Yes") %>%
factor(levels = c("No", "Yes")
)) %>%
mutate(
ALND_adjuvantXRT = case_when(ALND_2 == "No" & Adjuvant_XRT_2 == "No" ~ "No ALND, no adjuvant XRT",
ALND_2 == "No" & Adjuvant_XRT_2 == "Yes" ~ "No ALND, yes adjuvant XRT",
ALND_2 == "Yes" & Adjuvant_XRT_2 == "No" ~ "Yes ALND, no adjuvant XRT",
ALND_2 == "Yes" & Adjuvant_XRT_2 == "Yes" ~ "Yes ALND, yes adjuvant XRT") %>%
factor(levels = c("No ALND, no adjuvant XRT",
"No ALND, yes adjuvant XRT",
"Yes ALND, no adjuvant XRT",
"Yes ALND, yes adjuvant XRT")
)) %>%
mutate(
ALND_age40 = case_when(ALND_2 == "No" & Age_dx_grp_2 == "<40" ~ "No ALND, age<40",
ALND_2 == "No" & Age_dx_grp_2 == "≥40" ~ "No ALND, age≥40",
ALND_2 == "Yes" & Age_dx_grp_2 == "<40" ~ "Yes ALND, age<40",
ALND_2 == "Yes" & Age_dx_grp_2 == "≥40" ~ "Yes ALND, age≥40") %>%
factor(levels = c("No ALND, age<40",
"No ALND, age≥40",
"Yes ALND, age<40",
"Yes ALND, age≥40")
)) %>%
mutate(
ALND_age45 = case_when(ALND_2 == "No" & Age_dx_grp_3 == "<45" ~ "No ALND, age<45",
ALND_2 == "No" & Age_dx_grp_3 == "≥45" ~ "No ALND, age≥45",
ALND_2 == "Yes" & Age_dx_grp_3 == "<45" ~ "Yes ALND, age<45",
ALND_2 == "Yes" & Age_dx_grp_3 == "≥45" ~ "Yes ALND, age≥45") %>%
factor(levels = c("No ALND, age<45",
"No ALND, age≥45",
"Yes ALND, age<45",
"Yes ALND, age≥45")
)) %>%
mutate(
ALND_age50 = case_when(ALND_2 == "No" & Age_dx_grp_4 == "<50" ~ "No ALND, age<50",
ALND_2 == "No" & Age_dx_grp_4 == "≥50" ~ "No ALND, age≥50",
ALND_2 == "Yes" & Age_dx_grp_4 == "<50" ~ "Yes ALND, age<50",
ALND_2 == "Yes" & Age_dx_grp_4 == "≥50" ~ "Yes ALND, age≥50") %>%
factor(levels = c("No ALND, age<50",
"No ALND, age≥50",
"Yes ALND, age<50",
"Yes ALND, age≥50")
)) %>%
mutate(
ALND_adjuvantXRT_age40 = case_when(
ALND_adjuvantXRT == "No ALND, no adjuvant XRT" & Age_dx_grp_2 == "<40" ~ "No ALND, no adjuvant XRT, age<40",
ALND_adjuvantXRT == "No ALND, no adjuvant XRT" & Age_dx_grp_2 == "≥40" ~ "No ALND, no adjuvant XRT, age≥40",
ALND_adjuvantXRT == "No ALND, yes adjuvant XRT" & Age_dx_grp_2 == "<40" ~ "No ALND, yes adjuvant XRT, age<40",
ALND_adjuvantXRT == "No ALND, yes adjuvant XRT" & Age_dx_grp_2 == "≥40" ~ "No ALND, yes adjuvant XRT, age≥40",
ALND_adjuvantXRT == "Yes ALND, no adjuvant XRT" & Age_dx_grp_2 == "<40" ~ "Yes ALND, no adjuvant XRT, age<40",
ALND_adjuvantXRT == "Yes ALND, no adjuvant XRT" & Age_dx_grp_2 == "≥40" ~ "Yes ALND, no adjuvant XRT, age≥40",
ALND_adjuvantXRT == "Yes ALND, yes adjuvant XRT" & Age_dx_grp_2 == "<40" ~ "Yes ALND, Yes adjuvant XRT, age<40",
ALND_adjuvantXRT == "Yes ALND, yes adjuvant XRT" & Age_dx_grp_2 == "≥40" ~ "Yes ALND, Yes adjuvant XRT, age≥40") %>%
factor(levels = c("No ALND, no adjuvant XRT, age<40",
"No ALND, no adjuvant XRT, age≥40",
"No ALND, yes adjuvant XRT, age<40",
"No ALND, yes adjuvant XRT, age≥40",
"Yes ALND, no adjuvant XRT, age<40",
"Yes ALND, no adjuvant XRT, age≥40",
"Yes ALND, Yes adjuvant XRT, age<40",
"Yes ALND, Yes adjuvant XRT, age≥40")
)) %>%
mutate(
ALND_adjuvantXRT_age45 = case_when(
ALND_adjuvantXRT == "No ALND, no adjuvant XRT" & Age_dx_grp_3 == "<45" ~ "No ALND, no adjuvant XRT, age<45",
ALND_adjuvantXRT == "No ALND, no adjuvant XRT" & Age_dx_grp_3 == "≥45" ~ "No ALND, no adjuvant XRT, age≥45",
ALND_adjuvantXRT == "No ALND, yes adjuvant XRT" & Age_dx_grp_3 == "<45" ~ "No ALND, yes adjuvant XRT, age<45",
ALND_adjuvantXRT == "No ALND, yes adjuvant XRT" & Age_dx_grp_3 == "≥45" ~ "No ALND, yes adjuvant XRT, age≥45",
ALND_adjuvantXRT == "Yes ALND, no adjuvant XRT" & Age_dx_grp_3 == "<45" ~ "Yes ALND, no adjuvant XRT, age<45",
ALND_adjuvantXRT == "Yes ALND, no adjuvant XRT" & Age_dx_grp_3 == "≥45" ~ "Yes ALND, no adjuvant XRT, age≥45",
ALND_adjuvantXRT == "Yes ALND, yes adjuvant XRT" & Age_dx_grp_3 == "<45" ~ "Yes ALND, Yes adjuvant XRT, age<45",
ALND_adjuvantXRT == "Yes ALND, yes adjuvant XRT" & Age_dx_grp_3 == "≥45" ~ "Yes ALND, Yes adjuvant XRT, age≥45") %>%
factor(levels = c("No ALND, no adjuvant XRT, age<45",
"No ALND, no adjuvant XRT, age≥45",
"No ALND, yes adjuvant XRT, age<45",
"No ALND, yes adjuvant XRT, age≥45",
"Yes ALND, no adjuvant XRT, age<45",
"Yes ALND, no adjuvant XRT, age≥45",
"Yes ALND, Yes adjuvant XRT, age<45",
"Yes ALND, Yes adjuvant XRT, age≥45")
)) %>%
mutate(
ALND_adjuvantXRT_age50 = case_when(
ALND_adjuvantXRT == "No ALND, no adjuvant XRT" & Age_dx_grp_4 == "<50" ~ "No ALND, no adjuvant XRT, age<50",
ALND_adjuvantXRT == "No ALND, no adjuvant XRT" & Age_dx_grp_4 == "≥50" ~ "No ALND, no adjuvant XRT, age≥50",
ALND_adjuvantXRT == "No ALND, yes adjuvant XRT" & Age_dx_grp_4 == "<50" ~ "No ALND, yes adjuvant XRT, age<50",
ALND_adjuvantXRT == "No ALND, yes adjuvant XRT" & Age_dx_grp_4 == "≥50" ~ "No ALND, yes adjuvant XRT, age≥50",
ALND_adjuvantXRT == "Yes ALND, no adjuvant XRT" & Age_dx_grp_4 == "<50" ~ "Yes ALND, no adjuvant XRT, age<50",
ALND_adjuvantXRT == "Yes ALND, no adjuvant XRT" & Age_dx_grp_4 == "≥50" ~ "Yes ALND, no adjuvant XRT, age≥50",
ALND_adjuvantXRT == "Yes ALND, yes adjuvant XRT" & Age_dx_grp_4 == "<50" ~ "Yes ALND, Yes adjuvant XRT, age<50",
ALND_adjuvantXRT == "Yes ALND, yes adjuvant XRT" & Age_dx_grp_4 == "≥50" ~ "Yes ALND, Yes adjuvant XRT, age≥50") %>%
factor(levels = c("No ALND, no adjuvant XRT, age<50",
"No ALND, no adjuvant XRT, age≥50",
"No ALND, yes adjuvant XRT, age<50",
"No ALND, yes adjuvant XRT, age≥50",
"Yes ALND, no adjuvant XRT, age<50",
"Yes ALND, no adjuvant XRT, age≥50",
"Yes ALND, Yes adjuvant XRT, age<50",
"Yes ALND, Yes adjuvant XRT, age≥50")
)) %>%
mutate(Death_2 = ifelse(Death_from_BC == 0, "No", "Yes") %>%
factor(levels = c("No", "Yes")
)) %>%
data.frame()
##################################################################
## Recode ARDeath/LRDeath/DRDeath ##
##################################################################
table(dat0$Axillary_recurrence_2 , dat0$Death_2, useNA = "ifany")
# No Yes
# No 115 5
# Yes 1 1
# <NA> 6 0
dat0$ARDeath <- NA
dat0$ARDeath[dat0$Axillary_recurrence_2 == "Yes" | dat0$Death_2 == "Yes"] <- 1
dat0$ARDeath[dat0$Axillary_recurrence_2 == "No" & dat0$Death_2 == "No"] <- 0
table(dat0$ARDeath, useNA = "ifany")
# 0 1 <NA>
# 115 7 6
table(dat0$Local_recurrence_2 , dat0$Death_2, useNA = "ifany")
# No Yes
# No 108 4
# Yes 8 2
# <NA> 6 0
dat0$LRDeath <- NA
dat0$LRDeath[dat0$Local_recurrence_2 == "Yes" | dat0$Death_2 == "Yes"] <- 1
dat0$LRDeath[dat0$Local_recurrence_2 == "No" & dat0$Death_2 == "No"] <- 0
table(dat0$LRDeath, useNA = "ifany")
# 0 1 <NA>
# 108 14 6
table(dat0$Distant_recurrence_2 , dat0$Death_2, useNA = "ifany")
# No Yes
# No 106 0
# Yes 11 6
# <NA> 5 0
dat0$DRDeath <- NA
dat0$DRDeath[dat0$Distant_recurrence_2 == "Yes" | dat0$Death_2 == "Yes"] <- 1
dat0$DRDeath[dat0$Distant_recurrence_2 == "No" & dat0$Death_2 == "No"] <- 0
table(dat0$DRDeath, useNA = "ifany")
# 0 1 <NA>
# 106 17 5
##################################################################
## Recode T2ARDeath/T2LRDeath/T2DRDeath ##
##################################################################
dat0 <- dat0 %>%
mutate(
T2ARDeath = case_when(
(Axillary_recurrence_2 == "Yes" & Death_2 == "No") ~ `Time_to_AXR`,
(Axillary_recurrence_2 == "Yes" & Death_2 == "Yes") ~ `Time_to_AXR`,
(Death_2 == "Yes" & Axillary_recurrence_2 == "No") ~ `Time_to_death`,
ARDeath == 0 ~ `Follow_up_time..days.`))
# Check the number of patients not at risk
summary(dat0$T2ARDeath)
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 122 1337 2706 2780 4017 6230 6
dat0 <- dat0 %>%
mutate(
T2LRDeath = case_when(
(Local_recurrence_2 == "Yes" & Death_2 == "No") ~ `Time_to_LR`,
(Local_recurrence_2 == "Yes" & Death_2 == "Yes") ~ `Time_to_LR`,
(Death_2 == "Yes" & Local_recurrence_2 == "No") ~ `Time_to_death`,
LRDeath == 0 ~ `Follow_up_time..days.`))
summary(dat0$T2LRDeath)
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 122 1199 2536 2696 3941 6230 6
dat0 <- dat0 %>%
mutate(
T2DRDeath = case_when(
(Distant_recurrence_2 == "Yes" & Death_2 == "No") ~ `Time_to_DR`,
(Distant_recurrence_2 == "Yes" & Death_2 == "Yes") ~ `Time_to_DR`,
(Death_2 == "Yes" & Distant_recurrence_2 == "No") ~ `Time_to_death`,
DRDeath == 0 ~ `Follow_up_time..days.`))
summary(dat0$T2DRDeath)
# Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
# 122 1193 2573 2704 3904 6230 5
#################################################################
## Fix column names and orders ##
#################################################################
names(dat0) <-
sapply(strsplit(names(dat0), split = "\\."), function(x)
paste(x[x != ""], collapse = "."))
dat0 <- dat0 %>%
select(
"Record.ID", everything()
)
#################################################################
## Save data ##
#################################################################
date.analysis <- format(Sys.Date(), "%Y%b%d")
dput(dat0, paste0("../data/derived/", date.analysis, "_dat_micromets.Rdata"))
write.csv(dat0, paste0("../data/derived/", date.analysis, "_dat_micromets.csv"), row.names = F)
```
```{r load_cleaned_data}
##################################################################
## Load cleaned data in Rdata ##
##################################################################
dat.work <- dget("../data/derived/2022Dec24_dat_micromets.Rdata")
```
```{r, results="hide"}
#################################################################
## Set study variables ##
#################################################################
vars.all <- c(
### Demographical variables
# "Age_Dx",
"Age_dx_grp_1", "Age_dx_grp_2", "Age_dx_grp_3", "Age_dx_grp_4",
"Race_2", "Sex_2", "Menopausal_2", "Follow_up_time.days",
### Disease history variables
"T_clinical_2", "N_clinical_2",
"ER_2", "PR_2", "HER2_2", "TNBC",
"Tumor_Size.mm",
"Tumor_type_2",
"Nodal_mets_dx_2",
"Histo_grade_2",
"Ki67_2",
"LVI_2",
"Multiple_index_2",
"Bilateral_cancer_2",
"Index_status_2",
"Prior_breast_cancer_2",
### Screening/diagnosis/treatment variables
"Screened_2",
"ALND_2",
"ALND_age40", "ALND_age45", "ALND_age50",
"Addition_pos_node_2",
"Adjuvant_XRT_2", "Adjuvant_treatment",
"Adjuvant_chemo_2", "Adjuvant_endocrine_2", "Adjuvant_antiHER2_2",
"Neoadjuvant_XRT_2", "Neoadjuvant_treatment",
"Neoadjuvant_chemo_2", "Neoadjuvant_endocrine_2", "Neoadjuvant_antiHER2_2",
"ALND_adjuvantXRT",
"ALND_adjuvantXRT_age40", "ALND_adjuvantXRT_age45", "ALND_adjuvantXRT_age50",
### Disease outcome variables
"Axillary_recurrence_2", "Local_recurrence_2", "Distant_recurrence_2",
"New_breast_cancer_2"
)
# Use 0 or 1 to indicate if the variable is continuous or categorical
vars.cat <- rep(1, length(vars.all))
vars.cat[vars.all %in% c("Age_Dx", "Follow_up_time.days", "Tumor_Size.mm")] <- 0
```
The association between a categorical variable and a grouping variable (i.e., completion of axillary lymph node dissection (ALND)) was examined using the Fisher’s exact test. Difference in the value of a continuous variable among patients of different groups was examined using the Wilcoxon rank sum test (p-value is aligned with the median (IQR) summmaries in the following table). For variable with missing values, the difference in the proportion of missingness across different groups was also examined.
In our univariate survival analysis, the axillary/local/distant recurrence-free survival time is defined as a composite endpoint which combines the time to axillary/local/distant recurrence, time to death, and time to last follow-up into a univariate time to the first event or censoring. To illustrate how this combination of time works, we enumerate different scenarios for calculating the axillary recurrence-free survival time. The same logic applies to the other two types of recurrence (i.e., local recurrence and distant recurrence).
For axillary recurrence-free survival time:
* Given that 1 patient has the axillary recurrence but does not die, the time to axillary recurrence is treated as the survival time.
* Given that 5 patients die without the axillary recurrence, the time to death is treated as their survival time.
* Given that 1 patient first has the axillary recurrence and then dies, the time to axillary recurrence is treated as the survival time.
* Given that 115 patients neither have the axillary recurrence or die, the time to last follow-up is treated as their survival time.
* Given that 6 patients die but miss the information on the axillary recurrence, their survival time is treated as not available.
# Patient characteristics tables
## Patients characteristics by axillary lymph node dissection (column proportion)
```{r, results="hide"}
# Create a summary table by ALND_2
vars.cat.rm <- which(vars.all %in% c(
"ALND_2",
"ALND_adjuvantXRT",
"ALND_age40",
"ALND_age45",
"ALND_age50",
"ALND_adjuvantXRT_age40",
"ALND_adjuvantXRT_age45",
"ALND_adjuvantXRT_age50"))
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all[-vars.cat.rm],
vars.cat = vars.cat[-vars.cat.rm],
by = "ALND_2")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection (row proportion)
```{r, results="hide"}
vars.cat.rm <- which(vars.all %in% c(
"ALND_2",
"ALND_adjuvantXRT",
"ALND_age40",
"ALND_age45",
"ALND_age50",
"ALND_adjuvantXRT_age40",
"ALND_adjuvantXRT_age45",
"ALND_adjuvantXRT_age50",
"Neoadjuvant_treatment")) # Error in fisher.test(y.by.grp, simulate.p.value = TRUE) : need 2 or more non-zero row marginals
out <- fsmry.dmgrph.rowprop(dat = dat.work,
vars = vars.all[-vars.cat.rm],
vars.cat = vars.cat[-vars.cat.rm],
by = "ALND_2")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by post-mastectomy radiation therapy (column proportion)
```{r, results="hide"}
# Create a summary table by Adjuvant_XRT_2
vars.cat.rm <- which(vars.all %in% c(
"Adjuvant_XRT_2",
"ALND_adjuvantXRT",
"ALND_age40",
"ALND_age45",
"ALND_age50",
"ALND_adjuvantXRT_age40",
"ALND_adjuvantXRT_age45",
"ALND_adjuvantXRT_age50"))
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all[-vars.cat.rm],
vars.cat = vars.cat[-vars.cat.rm],
by = "Adjuvant_XRT_2")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by post-mastectomy radiation therapy (row proportion)
```{r, results="hide"}
vars.cat.rm <- which(vars.all %in% c(
"ALND_2",
"ALND_adjuvantXRT",
"ALND_age40",
"ALND_age45",
"ALND_age50",
"ALND_adjuvantXRT_age40",
"ALND_adjuvantXRT_age45",
"ALND_adjuvantXRT_age50",
"Neoadjuvant_treatment")) # Error in fisher.test(y.by.grp, simulate.p.value = TRUE) : need 2 or more non-zero row marginals
out <- fsmry.dmgrph.rowprop(dat = dat.work,
vars = vars.all[-vars.cat.rm],
vars.cat = vars.cat[-vars.cat.rm],
by = "Adjuvant_XRT_2")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection and post-mastectomy radiation therapy
```{r, results="hide"}
# Create a summary table by ALND_adjuvantXRT
vars.all2 <- c(
# "Age_Dx",
"Age_dx_grp_1", "Age_dx_grp_2", "Age_dx_grp_3", "Age_dx_grp_4",
"Race_2", "Sex_2", "Menopausal_2",
# "Follow_up_time.days",
"T_clinical_2", "N_clinical_2",
"ER_2", "PR_2", "HER2_2", "TNBC",
# "Tumor_Size.mm",
"Tumor_type_2",
"Nodal_mets_dx_2",
"Histo_grade_2",
"Ki67_2",
"LVI_2",
"Multiple_index_2",
"Bilateral_cancer_2",
"Index_status_2",
"Prior_breast_cancer_2",
"Screened_2",
"ALND_2",
"ALND_age40", "ALND_age45", "ALND_age50",
"Addition_pos_node_2",
"Adjuvant_XRT_2", "Adjuvant_treatment",
"Adjuvant_chemo_2", "Adjuvant_endocrine_2", "Adjuvant_antiHER2_2",
"Neoadjuvant_XRT_2", "Neoadjuvant_treatment",
"Neoadjuvant_chemo_2", "Neoadjuvant_endocrine_2", "Neoadjuvant_antiHER2_2",
"ALND_adjuvantXRT",
"ALND_adjuvantXRT_age40", "ALND_adjuvantXRT_age45", "ALND_adjuvantXRT_age50",
"Axillary_recurrence_2", "Local_recurrence_2", "Distant_recurrence_2",
"New_breast_cancer_2"
)
vars.cat.rm <- which(vars.all2 %in% c(
"Adjuvant_XRT_2",
"ALND_adjuvantXRT",
"ALND_age40",
"ALND_age45",
"ALND_age50",
"ALND_adjuvantXRT_age40",
"ALND_adjuvantXRT_age45",
"ALND_adjuvantXRT_age50"))
#################################################################
## Problem ##
#################################################################
# Error in wilcox.test.default(y1, y2, exact = F) :
# not enough (non-missing) 'x' observations
# In addition: Warning messages:
# 1: In `==.default`(grp, grp.names1) :
# longer object length is not a multiple of shorter object length
# 2: In is.na(e1) | is.na(e2) :
# longer object length is not a multiple of shorter object length
# Create an indicator [0, 1] for all categorical variables
vars.cat <- rep(1, length(vars.all2))
vars.cat[vars.all2 %in% c("Age_Dx", "Follow_up_time.days", "Tumor_Size.mm")] <- 0
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all2[-vars.cat.rm],
vars.cat = vars.cat[-vars.cat.rm],
by = "ALND_adjuvantXRT")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection and age (<40 versus ≥40)
```{r, results="hide"}
# Create a summary table by ALND_age40
vars.cat.rm.2 <- c(
vars.cat.rm,
which(vars.all2 %in% c("Age_dx_grp_1", "Age_dx_grp_2", "Age_dx_grp_3", "Age_dx_grp_4")))
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all2[-vars.cat.rm.2],
vars.cat = vars.cat[-vars.cat.rm.2],
by = "ALND_age40")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection, post-mastectomy radiation therapy, and age (<40 versus ≥40)
```{r, results="hide"}
# Create a summary table by ALND_adjuvantXRT_age40
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all2[-vars.cat.rm.2],
vars.cat = vars.cat[-vars.cat.rm.2],
by = "ALND_adjuvantXRT_age40")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection and age (<45 versus ≥45)
```{r, results="hide"}
# Create a summary table by ALND_age45
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all2[-vars.cat.rm.2],
vars.cat = vars.cat[-vars.cat.rm.2],
by = "ALND_age45")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection, post-mastectomy radiation therapy, and age (<45 versus ≥45)
```{r, results="hide"}
# Create a summary table by ALND_adjuvantXRT_age45
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all2[-vars.cat.rm.2],
vars.cat = vars.cat[-vars.cat.rm.2],
by = "ALND_adjuvantXRT_age45")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection and age (<50 versus ≥50)
```{r, results="hide"}
# Create a summary table by ALND_age50"
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all2[-vars.cat.rm.2],
vars.cat = vars.cat[-vars.cat.rm.2],
by = "ALND_age50")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
## Patients characteristics by axillary lymph node dissection, post-mastectomy radiation therapy, and age (<50 versus ≥50)
```{r, results="hide"}
# Create a summary table by ALND_adjuvantXRT_age50
out <- fsmry.dmgrph(dat = dat.work,
vars = vars.all2[-vars.cat.rm.2],
vars.cat = vars.cat[-vars.cat.rm.2],
by = "ALND_adjuvantXRT_age50")
```
```{r, results="asis"}
knitr::kable(out[[1]], row.names = F)
```
# Univariate analysis for recurrence free survival
Note that patients without time to axillary/local/distant recurrence data were not included in the analysis. The association between each variable and axillary/local/distant recurrence free survival was assessed using the log rank test. The Cox-proportional hazards model was used to estimate the HR and corresponding 95% CI for each unit increase of a continuous variable, or with respect to the reference category for a categorical variable. For each variable, if there were missing values, the association between missingness and axillary/local/distant recurrence free survival was also examined.
## Associations between variables and axillary recurrence
```{r, results="hide"}
##################################################################
## Reset the Age_dx_grp_1 reference level ##
##################################################################
# In cox regression modeling, to prevent the 95% CI of HR estimate approximating infinity, the reference category of a covariate may have the **adequate sample size** and/or **adequate outcomes**.
# How to define the **adequacy**?
# An events per variable of 10 is widely advocated as the rule of thumb for multivariable Cox regression analyses.
# Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5045274/
table(dat.work$Age_dx_grp_1, dat.work$Axillary_recurrence_2)
# No Yes
# 40-49 39* 0*
# 20-29 6 0
# 30-39 16 0
# 50-59 26* 1*
# 60-69 20 0
# 70-79 13* 1*
# 80-89 0 0
# 90+ 0 0
# dat.work$Age_dx_grp_1 <- relevel(dat.work$Age_dx_grp_1, ref = "40-49")
# |**Age_dx_grp_1** | | | | |P=0.394 |
# |40-49 |reference | | | | |
# |20-29 |1.00e+00 |0.00 |Inf |P=1.000 | |
# |30-39 |3.06e+08 |0.00 |Inf |P=0.999 | |
# |50-59 |3.79e+08 |0.00 |Inf |P=0.999 | |
# |60-69 |4.63e+08 |0.00 |Inf |P=0.999 | |
# |70-79 |7.29e+08 |0.00 |Inf |P=0.999 | |
# |80-89 |NA |NA |NA |P= NA | |
# |90+ |NA |NA |NA |P= NA | |
# dat.work$Age_dx_grp_1 <- relevel(dat.work$Age_dx_grp_1, ref = "50-59")
# |**Age_dx_grp_1** | | | | |P=0.394 |
# |50-59 |reference | | | | |
# |20-29 |0.00 |0.00 |Inf |P=0.999 | |
# |30-39 |0.81 |0.07 |8.91 |P=0.861 | |
# |40-49 |0.00 |0.00 |Inf |P=0.999 | |
# |60-69 |1.22 |0.17 |8.69 |P=0.841 | |
# |70-79 |1.92 |0.27 |13.71 |P=0.514 | |
# |80-89 |NA |NA |NA |P= NA | |
# |90+ |NA |NA |NA |P= NA | |
# dat.work$Age_dx_grp_1 <- factor(dat.work$Age_dx_grp_1,
# levels = c("20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+")) # Recover the original level
dat.work$Age_dx_grp_1 <- relevel(dat.work$Age_dx_grp_1, ref = "70-79")
# |**Age_dx_grp_1** | | | | |P=0.394 |
# |70-79 |reference | | | | |
# |20-29 |0.00 |0.00 |Inf |P=0.999 | |
# |30-39 |0.42 |0.04 |4.65 |P=0.479 | |
# |40-49 |0.00 |0.00 |Inf |P=0.999 | |
# |50-59 |0.52 |0.07 |3.70 |P=0.514 | |
# |60-69 |0.64 |0.09 |4.53 |P=0.651 | |
# |80-89 |NA |NA |NA |P= NA | |
# |90+ |NA |NA |NA |P= NA | |
#################################################################
## !!!Reset the Race_2 reference level ##
#################################################################
table(dat.work$Race_2, dat.work$Axillary_recurrence_2)
# No Yes
# Asian 5 0
# Non-hispanic black 7* 1*
# Non-hispanic white 93* 1*
# Hispanic 5 0
# Other 0 0
dat.work$Race_2 <- factor(dat.work$Race_2, levels = c("Non-hispanic white", "Non-hispanic black", "Asian", "Hispanic", "Other"))
# |**Race_2** | | | | |P=0.105 |
# |Non-hispanic white |reference | | | | |
# |Non-hispanic black |5.32 |1.03 |27.52 |P=0.046* | |
# |Asian |0.00 |0.00 |Inf |P=0.999 | |
# |Hispanic |0.00 |0.00 |Inf |P=0.999 | |
# |Other |NA |NA |NA |P= NA | |
# |*var.missing* | | | | |P=0.466 |
# |non.missing |reference | | | | |
# |missing |0.00 |0.00 |Inf |P=0.999 | |