Package 'survout'

Modify the Output for Uni-variable and Multi-variable Competing Risk Analysis (Categorical Only)

Description

This function generates a table of competing risk analysis result with number of patients,number of event, number of competing event,

Usage

crisk_cat(
  csurv,
  cevent,
  cvars,
  gnames,
  month = 0,
  y1 = TRUE,
  y2 = TRUE,
  y5 = TRUE
)

Arguments

csurv	the duration of follow-up time in months.
cevent	the status indicator, which is generally 0 = alive, 1 = event, 2 = other event
cvars	a vector, which has the variable's values (categorical only)
gnames	a text string, which is the name of the variable.
month	a number to get the month-rate of competing risk.
y1	logical value indicating whether the 1-year competing risk rate should be reported.
y2	logical value indicating whether the 2-year competing risk rate should be reported.
y5	logical value indicating whether the 5-year competing risk rate should be reported.

Value

a tibble of competing risk analysis output.

Examples

Dat <- MASS::Melanoma
Dat$time <- Dat$time/30.5
output <- crisk_cat(Dat$time, Dat$status, Dat$ulcer, "ulcer")

---

Modify the Output for Uni-variable and Multi-variable Competing Risk Analysis (Continuous and Ordinal Only)

Description

This function generates a table of competing risk analysis result with HR (95% Confidence Interval),P value.

Usage

crisk_con(csurv, cevent, cvars, gnames)

Arguments

csurv	the duration of follow-up time in months.
cevent	the status indicator, which is generally 0 = alive, 1 = event, 2 = other event
cvars	a matrix, which has the variables' values (continuous and ordinal only)
gnames	a text vector, which are the names of the variables.

Value

a dataframe containing HRs (with 95% Confidence Intervals) and P values

Examples

Dat <- MASS::Melanoma
Dat$time <- Dat$time/30.5
X      <- cbind(Dat$age, Dat$thickness)
Gnames <- c('age', 'thickness')
output <- crisk_con(Dat$time, Dat$status, X, Gnames)

---

Modify the Output for a Multi-variable Competing Risk Analysis .

Description

Create a table with the general multi-variable competing risk analysis results, including the HR (95 percent CI), P value.

Usage

crisk_multi(dat, csurv, cevent, convars = NULL, catvars = NULL)

Arguments

dat	a data.frame in which to interpret the variables.
csurv	this is the follow up time.
cevent	the status indicator, normally 0=alive, 1=dead.
convars	a vector of con variable names.
catvars	a vector of cat variable names.

Value

a tibble of competing risk analysis output.

Examples

Dat <- MASS::Melanoma
Dat$time <- Dat$time/30.5
con_var <- c("age","thickness")
cat_var <- c("sex","ulcer")
multi_out <- crisk_multi(Dat, "time", "status", catvars = cat_var, convars =con_var)

---

Modify the Output for Multiple Uni-variable Competing Risk Analysis .

Description

This function generates a table of competing risk analysis result with number of patients, number of event, number of competing event,

Usage

crisk_multiuni(
  dat,
  csurv,
  cevent,
  catvars = NULL,
  convars = NULL,
  ordvars = NULL,
  y1 = TRUE,
  y2 = TRUE,
  y5 = TRUE,
  month = 0
)

Arguments

dat	a data.frame in which to interpret the variables.
csurv	this is the follow up time.
cevent	the status indicator, normally 0=alive, 1=dead.
catvars	a vector of cat variable names.
convars	a vector of con variable names.
ordvars	a vector of ordinal variable names.
y1	logical value indicating whether the 1-year competing risk rate should be reported.
y2	logical value indicating whether the 2-year competing risk rate should be reported.
y5	logical value indicating whether the 5-year competing risk rate should be reported.
month	a number to get the month-rate of competing risk.

Value

a tibble of competing risk analysis output.

Examples

Dat <- MASS::Melanoma
Dat$time <- Dat$time/30.5
Dat$ulcer <- as.factor(Dat$ulcer)
con_var <- c("age")
ord_var <- c("ulcer")
cat_var <- c("sex")
uni_out <- crisk_multiuni(Dat, "time", "status",cat_var, con_var, ord_var)

---

Modify the Output for Uni-variable and Multi-variable Competing Risk Analysis (Ordinal Only)

Description

This function generates a table of competing risk analysis result with number of patients, number of event, number of competing event,

Usage

crisk_ord(
  csurv,
  cevent,
  cvars,
  gnames,
  month = 0,
  y1 = TRUE,
  y2 = TRUE,
  y5 = TRUE
)

Arguments

csurv	the duration of follow-up time in months.
cevent	the status indicator, which is generally 0 = alive, 1 = event, 2 = other event
cvars	a vector, which has the variable's values (ordinal only)
gnames	a text string, which is the name of the variable.
month	a number to get the month-rate of competing risk.
y1	logical value indicating whether the 1-year competing risk rate should be reported.
y2	logical value indicating whether the 2-year competing risk rate should be reported.
y5	logical value indicating whether the 5-year competing risk rate should be reported.

Value

a tibble of competing risk analysis output.

Examples

Dat <- MASS::Melanoma
Dat$time <- Dat$time/30.5
output <- crisk_ord(Dat$time, Dat$status, as.factor(Dat$year), "year")

---

Make An Integer Matrix Out of A Factor Variable.

Description

Create an indicator matrix of dimension length(x) x (nlevels(x)-1) with the column corresponding to the baseline level removed (by default the first level is used as baseline).

Usage

factor2ind(x, baseline)

Arguments

x	a variable.
baseline	a string indicating the reference level.

Value

a matrix

Examples

x = gl(4, 2, labels = c( "A", "B", "C", "D"))
factor2ind(x)
factor2ind(x, "C")

---

Export the A Single Dataframe to An Excel Sheet

Description

The function saves a dataframe into an excel sheet with a predetermined format.

Usage

p2excel(
  tabname = "Default",
  datastable,
  tablename = "Default",
  filename = "Default.xlsx"
)

Arguments

tabname	a string with the tab's name.
datastable	the dataframe that will be exported to Excel.
tablename	a string containing the table label and title, which will appear as the first row
filename	the name of the spreadsheet

Value

a spreadsheet containing an exported tables

---

Prepare to Export the Dataframe to An Excel Sheet.

Description

The function saves the dataframe as a tab and prepares it for output into an excel sheet with a predetermined format.

Usage

p2excel_pre(tabname = "Default", datastable, tablename = "Default", filename)

Arguments

tabname	a string with the tab's name.
datastable	the dataframe that will be exported to Excel.
tablename	a string containing the table label and title, which will appear as the first row
filename	the name of the spreadsheet

Value

a spreadsheet containing all of the exported tables

Examples

Dat <- survival::lung
results <- surv_uni_cat(Dat, "time", "status", "sex", report_index = TRUE)
wb <- openxlsx::createWorkbook()
wb <- p2excel_pre("survival_results",results,"Table 1. Overall Survival anlaysis",wb)
## Not run:
## saveWorkbook(wb, file = "os.xlsx", overwrite = TRUE)
## End(Not run)

---

Modify the Output for a Multi-variable Survival Analysis.

Description

Create a table with the general multi-variable survival analysis results, including the HR (95 percent CI), P value.

Usage

surv_multi(...)

Arguments

...	arguments will be passed to coxph

Value

a dataframe containing coxph output that includes variable names, HRs (95

Examples

Dat <- survival::lung
surv_multi(survival::Surv(time, status) ~ as.factor(sex) + age + meal.cal, data = Dat)

---

Modify the Output for Multiple Uni-variable Survival Analysis

Description

This function generates a table with the general survival analysis results, including the number of total patients, the number of sevents, the estimated median, the 1,2,5 year rate, the HR (95 percent confidence interval), the P value, the AIC, and the C index. This function just modifies the output table's format.

Usage

surv_multiuni(
  dat,
  stime,
  sevent,
  catvars = NULL,
  convars = NULL,
  y1 = TRUE,
  y2 = TRUE,
  y5 = TRUE,
  medianCI = FALSE,
  report_index = FALSE
)

Arguments

dat	a dat.frame.
stime	the duration of follow-up time in months.
sevent	the status indicator, which is generally 0 = alive, 1 = dead.
catvars	a vector of categorical variable names.
convars	a vector of continuous variables names.
y1	logical value indicating whether the 1-year survival rate should be reported.
y2	logical value indicating whether the 2-year survival rate should be reported.
y5	logical value indicating whether the 5-year survival rate should be reported.
medianCI	logical value indicating whether the 95 percent confidence interval of projected median survival should be reported.
report_index	logical value indicating if to report the show AIC and C index.

Value

A tibble of survival output

Examples

Dat <- survival::lung
convars <- c("age","meal.cal")
catvars <- c("sex")
surv_multiuni(Dat, "time", "status", catvars, convars, medianCI = TRUE)

---

Modify the Survival Output for a Categorical Variable.

Description

This function generates a table with the general survival analysis results, including the number of total patients, the number of sevents, the estimated median, the 1,2,5 year rate, the HR (95 percent confidence interval), the P value, the AIC, and the C index. This function just modifies the output table's format.

Usage

surv_uni_cat(
  dat,
  stime,
  sevent,
  svar,
  month = 0,
  medianCI = TRUE,
  y1 = TRUE,
  y2 = TRUE,
  y5 = TRUE,
  report_index = FALSE
)

Arguments

dat	a data.frame.
stime	the duration of follow-up time in months.
sevent	the status indicator, which is generally 0 = alive, 1 = dead.
svar	a variable name.
month	a number to get the month-rate of survival.
medianCI	logical value indicating whether the 95 percent confidence interval of projected median survival should be reported.
y1	logical value indicating whether the 1-year survival rate should be reported.
y2	logical value indicating whether the 2-year survival rate should be reported.
y5	logical value indicating whether the 5-year survival rate should be reported.
report_index	logical value indicating if to report the show AIC and C index.

Value

a tibble of survival output

Examples

Dat <- survival::lung
surv_uni_cat(Dat, "time", "status", "sex", report_index = TRUE)

---

Modify the Survival Output for a Continuous Variable.

Description

This function generates a table with the general survival analysis results, including the number of total patients, the number of events, the P value, the AIC, and the C index. This function just modifies the output table's format.

Usage

surv_uni_con(dat, stime, sevent, svar, report_index = FALSE)

Arguments

dat	a data.frame.
stime	the duration of follow-up time in months.
sevent	the status indicator, which is generally 0 = alive, 1 = dead.
svar	a variable name.
report_index	logical value indicating if to report the show AIC and C index.

Value

a tibble of survival results.

Examples

Dat <- survival::lung
surv_uni_con(Dat, "time", "status", "age",report_index = TRUE)

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