Package {BiMaUmisc}

Type:	Package
Title:	BiMaU Miscellaneous
Version:	0.2.0
Maintainer:	Anna Felip-Badia <annafelipibadia@gmail.com>
Description:	Contains a function to plot publication-ready survival curves using the Kaplan-Meier method (1958) <doi:10.2307/2281868>, a function to format p-values, and a function to automatically select statistical tests for comparing continuous variables between groups, which are useful for repetitive analyses. BiMaU stands for the Biostatistics and Mathematics Research Unit at the Sant Joan de Déu - Pediatric Cancer Center Barcelona https://github.com/BiMaU-PCCB.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Depends:	R (≥ 4.1.0)
Encoding:	UTF-8
Imports:	exams, survival (≥ 3.8-6), latex2exp, plotfunctions, graphics, utils
Suggests:	gtsummary
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-09-07 07:21:21 UTC; afelipb
Author:	Anna Felip-Badia [aut, cre], Aleix Martín-Moral [aut], Sara Perez-Jaume [aut]
Repository:	CRAN
Date/Publication:	2026-09-07 08:10:09 UTC

BiMaU Miscellaneous

Description

Contains a function to plot publication-ready survival curves using the Kaplan-Meier method (1958), a function to format p-values, and a function to automatically select statistical tests for comparing continuous variables between groups, which are useful for repetitive analyses. BiMaU stands for the Biostatistics and Mathematics Research Unit at the Sant Joan de Déu - Pediatric Cancer Center Barcelona.

Functions

show.p, survival.plot, find.test

Author(s)

Maintainer: Anna Felip-Badia annafelipibadia@gmail.com (ORCID)

Authors:

• Anna Felip-Badia annafelipibadia@gmail.com (ORCID)

• Aleix Martín-Moral (ORCID)

• Sara Perez-Jaume (ORCID)

References

Kaplan, E. L., & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.

Automatically select statistical tests for quantitative variables

Description

This function evaluates quantitative variables across stratification groups and automatically determines the appropriate parametric or non-parametric hypothesis test. See Details for an exhaustive explanation of the criteria followed. Designed to work seamlessly with tbl_summary, this function generates the input lists for arguments test and test.args of the function add_p, specifying the recommended test function and any additional arguments different from the defaults.

Usage

find.test(data, variables, strata, alpha = 0.05)

Arguments

Details

If any level of strata has less than five non-missing observations, no statistical test is performed for that variable, and a warning is issued. Otherwise, the appropriate test is selected based on the following criteria:

Two Groups (strata with 2 levels):

• t-test (t.test): selected if sample sizes in both groups are \ge 30 and Shapiro-Wilk tests indicate normality in both groups. Variance equality is tested using an F-test.

• Mann-Whitney test (wilcox.test): selected if sample sizes are small, this is < 30 in some group, or if normality is rejected in either group.

More than Two Groups (strata with > 2 levels):

• ANOVA (oneway.test): selected if ANOVA model residuals adhere to a normal distribution according to the Shapiro-Wilk test. Homoscedasticity is tested via Bartlett's test.

• Kruskal-Wallis test (kruskal.test): selected if ANOVA residuals deviate from normality.

Value

A list of length 2 intended to be passed directly to arguments test and test.args of the function add_p when using tbl_summary:

test

a list of formulas matching each variable name to its selected test string (e.g., age ~ "t.test").

test.args

a list of formulas specifying additional test parameters (e.g., age ~ list(var.equal = TRUE)). Variables requiring no additional parameters are omitted from this second list.

Examples

df <- as.data.frame(gtsummary::trial)
df$trt <- factor(df$trt)
df$grade <- factor(df$grade)

# Two levels variable (trt: Drug A, Drug B)
tests2 <- find.test(df, variables = c(age, marker), strata = trt)
tests2

# Use outputs directly with tbl_summary()
gtsummary::tbl_summary(df, include = c(age, marker), by = trt) |>
  gtsummary::add_p(test = tests2$test, test.args = tests2$test.args)

# Three levels variable (grade: I, II, III)
tests3 <- find.test(df, variables = c(age, marker), strata = grade)
tests3

# Use outputs directly with tbl_summary()
gtsummary::tbl_summary(df, include = c(age, marker), by = grade) |>
  gtsummary::add_p(test = tests3$test, test.args = tests3$test.args)
  
# Using function show.p() from BiMaUmisc package
gtsummary::tbl_summary(df, include = c(age, marker), by = grade) |>
  gtsummary::add_p(test = tests3$test, test.args = tests3$test.args,
                   pvalue_fun = function(x) show.p(x))

Format p-values for display

Description

Convert p-values to a compact textual representation. The function prints decimals up to the first non-zero decimal and the following one, applying standard rounding.

Usage

show.p(p, add.p = FALSE)

Arguments

Details

The number of decimals shown depends on the magnitude of the p-value so that two meaningful digits are displayed after the leading zeros.

Specifically:

• For p \ge 0.1, two decimal places are shown.

• For 0.01 \le p < 0.1, three decimal places are shown.

• For 0.001 \le p < 0.01, four decimal places are shown.

• For 0.0001 \le p < 0.001, five decimal places are shown.

• For p < 0.0001, the string "<0.0001" is returned.

This formatting ensures that the first non-zero decimal of the p-value and the following digit are displayed, while avoiding an excessive number of leading zeros.

If add.p = TRUE, the prefix "*p*=" (or "*p*<" when applicable) is added to the formatted value. This will ensure the p appears in cursive when calling the function in line on R markdown documents.

Value

A character vector with the formatted p-values.

Examples

show.p(0.00785)
show.p(c(0.03042, 0.1579, 0.0000025))
show.p(0.00785, add.p = TRUE)
show.p(c(0.03042, 0.1579, 0.0000025), add.p = TRUE)

Plot survival curves

Description

This function produces an elegant survival curve plot. It can automatically append the number-at-risk table and display the log-tank test p-value (obtained from a call to survdiff). Highly customizable, it offers numerous arguments to easily modify the plot's appearance.

Usage

survival.plot(data, time, status, strata = 1,
              legend = TRUE, legend.pos = "bottomleft", legend.labs = NULL,
              legend.title = NULL, legend.cex = 1,
              events = FALSE, events.pos = NULL, events.text = NULL,
              mark.time = TRUE, lwd = 3, 
              risk = TRUE, risk.text = TRUE, risk.labs = NULL, risk.cex = 1, 
              p.value = TRUE, p.value.pos = NULL,
              units = "days", xlab = NULL, times = NULL,
              yscale = 1, ylab = NULL, 
              main = "Survival curve", col = NULL, ...)

Arguments

Value

A customized survival curve plot displaying the requested graphical and statistical modifications.

Note

A call to par is used in this function. Notice that the arguments font.axis, font.lab, cex.lab, las and xpd are always set to 2, 2, 1.2, 1, and TRUE, respectively. Moreover, the argument mar is always modified and depends on the number of levels in strata as well as whether or not the number-at-risk table is appended.
For optimal resolution and layout alignment when using the function in an R file, adjust the plot window. For example, ideal dimensions are approximately 7 x 5” for 24” screens and 5.9 x 4.2” for 13” screens.
For optimal resolution and layout alignment when using the function in an R markdown file or similar, use fig.dim = c(7, 5) in the corresponding chunk.

Examples

lung <- survival::cancer
lung$time_y <- lung$time/365.25
lung$sex <- factor(lung$sex, levels = 1:2, labels = c("Male", "Female"))

# no strata
survival.plot(lung, time_y, status, units = "y")
# customizing
survival.plot(lung, time_y, status, units = "y",
              xlab = "Time since diagnosis (in years)",
              yscale = 100,
              main = "Overall survival", mark.col = "darkgray")

# 2 strata
survival.plot(lung, time_y, status, sex, units = "y")
# customizing
survival.plot(lung, time_y, status, sex, 
              legend.pos = "topright", legend.title = "Sex", 
              events = TRUE, risk.text = FALSE, p.value.pos = c(2.4, 0.4),
              units = "y", xlab = "Time since diagnosis (in years)", 
              main = "Overall survival", col = c("darkviolet", "darkgreen"))