Title:	Tidy Presentation of Clinical Reporting
Version:	0.1.1
Date:	2024-12-30
Description:	Streamlined statistical reporting in 'Rmarkdown' environments. Facilitates the automated reporting of descriptive statistics, multiple univariate models, multivariable models and tables combining these outputs. Plotting functions include customisable survival curves, forest plots from logistic and ordinal regression and bivariate comparison plots.
License:	MIT + file LICENSE
Suggests:	nlme, tidycmprsk, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	aod, boot, cmprsk, cowplot, dplyr, geepack, ggplot2, ggpubr, gridExtra, kableExtra, knitr, lifecycle, MASS, pander, plyr, rlang, rstatix, scales, stats, survival, tidyselect
Collate:	'helper.R' 'main.R' 'globals.R' 'data.R' 'lblCode.R' 'rm_compactsum.R' 'rm_uvsum2.R' 'autoreg.R' 'autosum.R' 'getVarLevels.R' 'rm_mvsum2.R'
Depends:	R (≥ 4.2)
LazyData:	no
VignetteBuilder:	knitr, rmarkdown
NeedsCompilation:	no
Packaged:	2025-01-22 17:07:19 UTC; lisaavery
Author:	Lisa Avery [cre, aut], Ryan Del Bel [aut], Osvaldo Espin-Garcia [aut], Katherine Lajkosz [aut], Clarina Ong [aut], Tyler Pittman [aut], Anna Santiago [aut], Yanning Wang [ctr], Jessica Weiss [aut], Wei Xu [aut]
Maintainer:	Lisa Avery <lisa.avery@uhn.ca>
Repository:	CRAN
Date/Publication:	2025-01-24 18:40:02 UTC

Add spaces to strings in LaTeX

Description

Add spaces to strings in LaTeX. Returns appends ~~~ before the string

Usage

addspace(x)

Arguments

fit box cox transformed linear model

Description

Wrapper function to fit fine and gray competing risk model using function crr from package cmprsk

Usage

boxcoxfitRx(f, data, lambda = FALSE)

Arguments

Value

a list containing the linear model (lm) object and, if requested, lambda

Capitalize a string

Description

Capitalize a string

Usage

cap(x)

Arguments

Clear variable labels from a data frame

Description

This function will remove all label attributes from variables in the data.

Usage

clear_labels(data)

Arguments

Details

To change or remove individual labels use set_labels or set_var_labels

Examples

# Set a few variable labels for ctDNA
data("ctDNA")
ctDNA <- ctDNA |> set_var_labels(
   ctdna_status="detectable ctDNA",
  cohort="A cohort label")
# Clear all variable data frames and check
clear_labels(ctDNA)

Get covariate summary dataframe

Description

Returns a dataframe corresponding to a descriptive table.

Usage

covsum(
  data,
  covs,
  maincov = NULL,
  digits = 1,
  numobs = NULL,
  markup = FALSE,
  sanitize = FALSE,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  show.tests = FALSE,
  dropLevels = TRUE,
  excludeLevels = NULL,
  full = TRUE,
  digits.cat = 0,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  include_missing = FALSE,
  percentage = c("column", "row")
)

Arguments

Details

Comparisons for categorical variables default to chi-square tests, but if there are counts of <5 then the Fisher Exact test will be used and if this is unsuccessful then a second attempt will be made computing p-values using MC simulation. If testcont='ANOVA' then the t-test with unequal variance will be used for two groups and an ANOVA will be used for three or more. The statistical test used can be displayed by specifying show.tests=TRUE.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

References

Ellis, P.D. (2010) The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.doi:10.1017/CBO9780511761676

Lakens, D. (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4; 863:1-12. doi:10.3389/fpsyg.2013.00863

See Also

fisher.test,chisq.test, wilcox.test,kruskal.test,and anova

fit crr model

Description

Wrapper function to fit fine and gray competing risk model using function crr from package cmprsk

Usage

crrRx(f, data)

Arguments

Value

a competing risk model with the call appended to the list

See Also

cmprsk::crr

Examples

# From the crr help file:
set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2,200,replace=TRUE)
cov <- matrix(runif(600),nrow=200)
dimnames(cov)[[2]] <- c('x1','x2','x3')
df <- data.frame(ftime,fstatus,cov)
m1 <- crrRx(as.formula('ftime+fstatus~x1+x2+x3'),df)
# Nicely output to report:
rm_mvsum(m1,data=df,showN = TRUE,vif=TRUE)

Tumour size change over time Longitudinal changes in tumour size since baseline for patients by changes in ctDNA status (clearance, decrease or increase) since baseline.

Description

Tumour size change over time

Longitudinal changes in tumour size since baseline for patients by changes in ctDNA status (clearance, decrease or increase) since baseline.

Usage

data('ctDNA')

Format

A data frame with 270 rows and 5 variables:

id

Patient ID

cohort

Study Cohort

ctdna_status

Change in ctDNA since baseline

time

Number of weeks on treatment

size_change

Percentage change in tumour measurement

Source

https://www.nature.com/articles/s43018-020-0096-5

Retrieve columns number from spreadsheet columns specified as unquoted letters

Description

Retrieve columns number from spreadsheet columns specified as unquoted letters

Usage

excelCol(...)

Arguments

Value

a numeric vector corresponding to columns in a spreadsheet

Examples

## Find the column numbers for excel columns AB, CE and BB
excelCol(AB,CE,bb)
## Get the columns between A and K and Z
excelCol(A-K,Z)

Retrieve spreadsheet column letter-names from columns indices

Description

Creates a vector of spreadsheet-style letter-names corresponding to column numbers

Usage

excelColLetters(columnIndices)

Arguments

Details

This is the inverse function of excelCol

Value

a character vector corresponding to the spreadsheet column headings

Examples

## Find the column numbers for excel columns AB, CE and BB
colIndices <- excelCol(AB,CE,bb)
## Go back to the column names
excelColLetters(colIndices)

Extract variable labels from labelled data frame

Description

Extract variable labels from data and return a data frame with labels

Usage

extract_labels(data, sep = "_")

Arguments

Details

All variable names will be returned, even those with no labels. If the label attribute has length greater than one the values will be concatenated and returned as a single string separated by sep

Examples

# Set a few variable labels for ctDNA
data("ctDNA")
ctDNA <- ctDNA |> set_var_labels(
   ctdna_status="detectable ctDNA",
  cohort="A cohort label")
# Extract labels
extract_labels(ctDNA)

Create a forest plot using ggplot2

Description

This function will accept a log or logistic regression fit from glm or geeglm, and display the OR or RR for each variable on the appropriate log scale.

Usage

forestplot2(
  model,
  conf.level = 0.95,
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  logScale = getOption("reportRmd.logScale", TRUE),
  nxTicks = 5
)

Arguments

Value

a plot object

Examples

data("pembrolizumab")
glm_fit = glm(orr~change_ctdna_group+sex+age+l_size,
data=pembrolizumab,family = 'binomial')
forestplot2(glm_fit)

Create a multivariable forest plot using ggplot2

Description

This function will send and take log or logistic regression fit from glm or geeglm from mvsum function, and display the OR or RR for each variable on the appropriate log scale.

Usage

forestplotMV(
  model,
  data,
  conf.level = 0.95,
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  digits = getOption("reportRmd.digits", 2),
  logScale = getOption("reportRmd.logScale", TRUE),
  nxTicks = 5,
  showN = TRUE,
  showEvent = TRUE
)

Arguments

Value

a plot object

Examples

data("pembrolizumab")
glm_fit = glm(orr~change_ctdna_group+sex+age+l_size,
data=pembrolizumab,family = 'binomial')
forestplotMV(glm_fit)

Create an univariable forest plot using ggplot2

Description

This function will send and take log or logistic regression fit from glm or geeglm from uvsum function, and display the OR or RR for each variable on the appropriate log scale.

Usage

forestplotUV(
  response,
  covs,
  data,
  id = NULL,
  corstr = NULL,
  model = "glm",
  family = NULL,
  digits = getOption("reportRmd.digits", 2),
  conf.level = 0.95,
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  logScale = getOption("reportRmd.logScale", TRUE),
  nxTicks = 5,
  showN = TRUE,
  showEvent = TRUE
)

Arguments

Value

a plot object

Examples

data("pembrolizumab")
forestplotUV(response="orr", covs=c("change_ctdna_group", "sex", "age", "l_size"),
data=pembrolizumab, family='binomial')

Combine an univariable and multivariable forest plot using ggplot2

Description

This function will take log or logistic regression fit forest plot output from forestplotUV and forestplotMV functions and display the combined adjusted and unadjusted OR or RR for each variable on the appropriate log scale. Please note that total N and reference-level N is taken from unadjusted model.

Usage

forestplotUVMV(
  UVmodel,
  MVmodel,
  model = "glm",
  family = NULL,
  digits = getOption("reportRmd.digits", 2),
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  logScale = FALSE,
  nxTicks = 5,
  showN = TRUE,
  showEvent = TRUE
)

Arguments

Value

a plot object

Examples

data("pembrolizumab")
UVp = forestplotUV(response="orr", covs=c("change_ctdna_group", "sex", "age",
"l_size"), data=pembrolizumab, family='binomial')
MVp = forestplotMV(glm(orr~change_ctdna_group+sex+age+l_size,
data=pembrolizumab,family = 'binomial'))
forestplotUVMV(UVp, MVp)

Specific p-value formatting

Description

If p < 0.001 returns "<0.001", if p < 0.01 returns p to 3 decimal places otherwise returns p to 2 decimal places

Usage

formatp(pvalues)

Arguments

Parameter Estimation for the Box-Cox Transformation

Description

This function is copied from the geoR package which has been removed from the CRAN repository.

Usage

geoR_boxcoxfit(object, xmat, lambda, lambda2 = NULL, add.to.data = 0)

Arguments

Details

For more information see: https://cran.r-project.org/web/packages/geoR/index.html

Plot KM and CIF curves with ggplot

Description

This function will plot a KM or CIF curve with option to add the number at risk. You can specify if you want confidence bands, the hazard ratio, and pvalues, as well as the units of time used.

Usage

ggkmcif(
  response,
  cov = NULL,
  data,
  type = NULL,
  pval = TRUE,
  HR = FALSE,
  HR_pval = FALSE,
  conf.curves = FALSE,
  conf.type = "log",
  table = TRUE,
  times = NULL,
  xlab = "Time",
  ylab = NULL,
  main = NULL,
  stratalabs = NULL,
  strataname = nicename(cov),
  stratalabs.table = NULL,
  strataname.table = strataname,
  median.text = FALSE,
  median.lines = FALSE,
  median.CI = FALSE,
  set.time.text = NULL,
  set.time.line = FALSE,
  set.time = 5,
  set.time.CI = FALSE,
  censor.marks = TRUE,
  censor.size = 3,
  censor.stroke = 1.5,
  fsize = 10,
  nsize = 3,
  lsize = 1,
  psize = 3.5,
  median.size = 3,
  median.pos = NULL,
  median.lsize = 1,
  set.size = 3,
  set.pos = NULL,
  set.lsize = 1,
  ylim = c(0, 1),
  col = NULL,
  linetype = NULL,
  xlim = NULL,
  legend.pos = NULL,
  pval.pos = NULL,
  plot.event = 1,
  event = c("col", "linetype"),
  flip.CIF = FALSE,
  cut = NULL,
  eventlabs = NULL,
  event.name = NULL,
  Numbers_at_risk_text = "Numbers at risk",
  HR.digits = 2,
  HR.pval.digits = 3,
  pval.digits = 3,
  median.digits = 3,
  set.time.digits = 3,
  returns = FALSE,
  print.n.missing = TRUE
)

Arguments

Details

Note that for proper pdf output of special characters the following code needs to be included in the first chunk of the rmd knitr::opts_chunk$set(dev="cairo_pdf")

Value

Nothing is returned unless returns = TRUE is used. With returns = TRUE, if table=TRUE (the default) a table style graphic with survival plot and number at risk table is returned. Otherwise a plot with the survival curves is returned.

Examples

data("pembrolizumab")
# Simple plot without confidence intervals
ggkmcif(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)

# Plot with median survival time
ggkmcif(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = TRUE,median.lines=TRUE,conf.curves=TRUE)

# Plot with specified survival times and log-log CI
ggkmcif(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = FALSE,set.time.text = 'mo OS',
set.time = c(12,24),conf.type = 'log-log',conf.curves=TRUE)

# KM plot with 95% CI and censor marks
ggkmcif(c('os_time','os_status'),'sex',data = pembrolizumab, type = 'KM',
HR=TRUE, HR_pval = TRUE, conf.curves = TRUE,conf.type='log-log',
set.time.CI = TRUE, censor.marks=TRUE)

Plot KM and CIF curves with ggplot

Description

This function will plot a KM or CIF curve with option to add the number at risk. You can specify if you want confidence bands, the hazard ratio, and pvalues, as well as the units of time used.

Usage

ggkmcif2(
  response,
  cov = NULL,
  data,
  pval = TRUE,
  conf.curves = FALSE,
  table = TRUE,
  xlab = "Time",
  ylab = NULL,
  col = NULL,
  times = NULL,
  type = NULL,
  plot.event = 1,
  returns = FALSE,
  ...
)

Arguments

Details

Note that for proper pdf output of special characters the following code needs to be included in the first chunk of the rmd knitr::opts_chunk$set(dev="cairo_pdf")

Value

ggplot object; if table = F then only curves are output; if table = T then curves and risk table are output together

Examples

# Simple plot without confidence intervals
data("pembrolizumab")
ggkmcif2(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)

# Plot with median survival time
ggkmcif2(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = TRUE,median.lines=TRUE,conf.curves=TRUE)

# Plot with specified survival times and log-log CI
ggkmcif2(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = FALSE,set.time.text = 'mo OS',
set.time = c(12,24),conf.type = 'log-log',conf.curves=TRUE)

# KM plot with 95% CI and censor marks
ggkmcif2(c('os_time','os_status'),'sex',data = pembrolizumab, type = 'KM',
HR=TRUE, HR_pval = TRUE, conf.curves = TRUE,conf.type='log-log',
set.time.CI = TRUE, censor.marks=TRUE)

Additional parameters passed to ggkmcif2

Description

This section documents the additional parameters for ggkmcif2.

Usage

ggkmcif2Parameters(
  table.height = NULL,
  HR = FALSE,
  HR_pval = FALSE,
  conf.type = "log",
  main = NULL,
  stratalabs = NULL,
  strataname,
  stratalabs.table = NULL,
  strataname.table = strataname,
  median.text = FALSE,
  median.lines = FALSE,
  median.CI = FALSE,
  set.time.text = NULL,
  set.time.line = FALSE,
  set.time = 5,
  set.time.CI = FALSE,
  censor.marks = TRUE,
  censor.size = 3,
  censor.stroke = 1.5,
  fsize = 11,
  nsize = 3,
  lsize = 1,
  psize = 3.5,
  median.size = 3,
  median.pos = NULL,
  median.lsize = 1,
  set.size = 3,
  set.pos = NULL,
  set.lsize = 1,
  ylim = c(0, 1),
  linetype = NULL,
  xlim = NULL,
  legend.pos = NULL,
  pval.pos = NULL,
  event = c("col", "linetype"),
  flip.CIF = FALSE,
  cut = NULL,
  eventlabs = NULL,
  event.name = NULL,
  Numbers_at_risk_text = "Number at risk",
  HR.digits = 2,
  HR.pval.digits = 3,
  pval.digits = 3,
  median.digits = 3,
  set.time.digits = 3,
  print.n.missing = TRUE,
  returns = FALSE
)

Arguments

Plot KM and CIF curves with ggplot

Description

This function puts together a survival curve, and a number at risk table

Usage

ggkmcif_paste(list_gg)

Arguments

Value

a gtable with three elements, the survival curve, a spacer and the number at risk table

Examples

data("pembrolizumab")
plot <- ggkmcif(response=c('pfs_time','pfs_status'),
data=pembrolizumab,returns = TRUE)

# Highlighting a section of the curve
plot[[1]] <- plot[[1]] +
ggplot2::geom_rect(xmin=4,xmax=8,ymin=0.15,ymax=0.4,alpha=0.01,fill='yellow')

# Putting the curve back together
ggkmcif_paste(plot)

Bold strings in HTML

Description

Bold strings in HTML

Usage

hbld(strings)

Arguments

Bold strings in LaTeX

Description

Bold strings in LaTeX

Usage

lbld(strings)

Arguments

Formats p-values for LaTeX

Description

Returns <0.001 if pvalue is <0.001. Else rounds the pvalue to specified significant digits. Will bold the p-value if it is <= 0.05

Usage

lpvalue(x, sigdigits = 2)

Arguments

Output a table for multivariate or univariate regression models

Description

A dataframe corresponding to a univariate or multivariate regression table. If for_plot = TRUE, estimates and confidence interval bounds will also be displayed separately for easy plotting.

Usage

m_summary(
  model,
  CIwidth = 0.95,
  digits = 2,
  vif = FALSE,
  whichp = "levels",
  for_plot = FALSE
)

Arguments

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned. For negative binomial models a deviance test is used.

If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.

As of R 4.4.0 the likelihood profiles are included in base R.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

Examples

## Not run: data("pembrolizumab")
uv_lm <- lm(age~sex,data=pembrolizumab)
m_summary(uv_lm, digits = 3, for_plot = FALSE)

mv_binom <- glm(orr~age+sex+cohort,family = 'binomial',data = pembrolizumab)
m_summary(mv_binom, whichp = "both", for_plot = TRUE)
## End(Not run)

Get multivariate summary dataframe

Description

Returns a dataframe with the model summary and global p-value for multi-level variables.

Usage

mvsum(
  model,
  data,
  digits = getOption("reportRmd.digits", 2),
  showN = TRUE,
  showEvent = TRUE,
  markup = TRUE,
  sanitize = TRUE,
  nicenames = TRUE,
  CIwidth = 0.95,
  vif = TRUE
)

Arguments

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned.

If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.

VIF for competing risk models is computed by fitting a linear model with a dependent variable comprised of the sum of the model independent variables and then calculating VIF from this linear model.

References

John Fox & Georges Monette (1992) Generalized Collinearity Diagnostics, Journal of the American Statistical Association, 87:417, 178-183, DOI: 10.1080/01621459.1992.10475190

John Fox and Sanford Weisberg (2019). An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage.

Combine two table columns into a single column with levels of one nested within levels of the other.

Description

This function accepts a data frame (via the data argument) and combines two columns into a single column with values from the head_col serving as headers and values of the to_col displayed underneath each header. The resulting table is then passed to outTable for printing and output, to use the grouped table as a data frame specify tableOnly=TRUE. By default the headers will be bolded and the remaining values indented.

Usage

nestTable(
  data,
  head_col,
  to_col,
  colHeader = "",
  caption = NULL,
  indent = TRUE,
  boldheaders = TRUE,
  hdr_prefix = "",
  hdr_suffix = "",
  digits = getOption("reportRmd.digits", 2),
  tableOnly = FALSE,
  fontsize
)

Arguments

Details

Note that it is possible to combine multiple tables (more than two) with this function.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

## Investigate models to predict baseline ctDNA and tumour size and display together
## (not clinically useful!)
data(pembrolizumab)
fit1 <- lm(baseline_ctdna~age+l_size+pdl1,data=pembrolizumab)
m1 <- rm_mvsum(fit1,tableOnly=TRUE)
m1$Response = 'ctDNA'
fit2 <- lm(l_size~age+baseline_ctdna+pdl1,data=pembrolizumab)
m2 <- rm_mvsum(fit2,tableOnly=TRUE)
m2$Response = 'Tumour Size'
nestTable(rbind(m1,m2),head_col='Response',to_col='Covariate')

Round retaining digits

Description

Round retaining digits

Usage

niceNum(x, digits = 2)

Arguments

Lean strings for printing

Description

Returns strings with . and _ replaced by a space. This is nice when printing column names of your dataframe in a report

Usage

nicename(strings, check_numbers = TRUE)

Arguments

Print tables to PDF/Latex HTML or Word

Description

Output the table nicely to whatever format is appropriate. This is the output function used by the rm_* printing functions.

Usage

outTable(
  tab,
  row.names = NULL,
  to_indent = numeric(0),
  bold_headers = TRUE,
  rows_bold = numeric(0),
  bold_cells = NULL,
  caption = NULL,
  digits = getOption("reportRmd.digits", 2),
  align,
  applyAttributes = TRUE,
  keep.rownames = FALSE,
  nicenames = TRUE,
  fontsize,
  chunk_label,
  format = NULL
)

Arguments

Details

Entire rows can be bolded, or specific cells. Currently indentation refers to the first column only. By default, underscores in column names are converted to spaces. To disable this set rm_ to FALSE

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

# To make custom changes or change the fontsize in PDF/HTML
data("pembrolizumab")
tab <- rm_covsum(data=pembrolizumab,maincov = 'change_ctdna_group',
covs=c('age','sex','pdl1','tmb','l_size'),show.tests=TRUE,tableOnly = TRUE)
outTable(tab, fontsize=7)

# To bold columns with the variable names
rows_bold <- c(1,4,7,10,13)
outTable(tab,rows_bold = rows_bold)

# To bold the estimates for male/female
bold_cells <- as.matrix(expand.grid(5:6,1:ncol(tab)))
outTable(tab,bold_cells= bold_cells)

# Output the above table to HTML or LaTeX
#cat(outTable(tab=tab)) #Knits to specified global setting
#cat(outTable(tab, format="html"), file = "tab.html") #HTML output
#cat(outTable(tab, format="latex"), file = "tab.tex") #LaTeX output

Survival data Survival status and ctDNA levels for patients receiving pembrolizumab

Description

Survival data

Survival status and ctDNA levels for patients receiving pembrolizumab

Usage

data('pembrolizumab')

Format

A data frame with 94 rows and 15 variables:

id

Patient ID

age

Age at study entry

sex

Patient Sex

cohort

Study Cohort

l_size

Target lesion size at baseline

pdl1

PD L1 percent

tmb

log of TMB

baseline_ctdna

Baseline ctDNA

change_ctdna_group

Did ctDNA increase or decrease from baseline to cycle 3

orr

Objective Response

cbr

Clinical Beneficial Response

os_status

Overall survival status

os_time

Overall survival time in months

pfs_status

Progression free survival status

pfs_time

Progression free survival time in months

Source

https://www.nature.com/articles/s43018-020-0096-5

Plot multiple bivariate relationships in a single plot

Description

This function is designed to accompany rm_uvsum as a means of visualising the results, and uses similar syntax.

Usage

plotuv(
  response,
  covs,
  data,
  showN = FALSE,
  showPoints = TRUE,
  na.rm = TRUE,
  response_title = NULL,
  return_plotlist = FALSE,
  ncol = 2,
  p_margins = c(0, 0.2, 1, 0.2),
  bpThreshold = 20,
  mixed = TRUE,
  violin = FALSE,
  position = c("dodge", "stack", "fill"),
  use_labels = TRUE
)

Arguments

Details

Plots are displayed as follows: If response is continuous For a numeric predictor scatterplot For a categorical predictor: If 20+ observations available boxplot, otherwise dotplot with median line If response is a factor For a numeric predictor: If 20+ observations available boxplot, otherwise dotplot with median line For a categorical predictor barplot Response variables are shown on the ordinate (y-axis) and covariates on the abscissa (x-axis)

Variable names are replaced by their labels if available, or by tidy versions if not. Set use_labels=FALSE to use the variable names.

Value

a list containing plots for each variable in covs

See Also

ggplot2::ggplot and ggpubr::ggarrange replace_plot_labels

Examples

## Run multiple univariate analyses on the pembrolizumab dataset to predict cbr and
## then visualise the relationships.
data("pembrolizumab")
rm_uvsum(data=pembrolizumab,
response='cbr',covs=c('age','sex','l_size','baseline_ctdna'))
plotuv(data=pembrolizumab,  response='cbr',
covs=c('age','sex','l_size','baseline_ctdna'),showN=TRUE)

Round and paste with parentheses

Description

Round and paste with parentheses

Usage

psthr(x, y = 2)

Arguments

Paste with parentheses

Description

Paste with parentheses

Usage

pstprn(x)

Arguments

Formats p-values

Description

Returns <0.001 if pvalue is <0.001. Else rounds the pvalue to specified significant digits

Usage

pvalue(x, digits)

Arguments

Replace variable names with labels in ggplot

Description

If the data stored in a ggplot object has variable labels then this will replace the variable names with the variable labels. If no labels are set then the variable names will be tidied and a nicer version used.

Usage

replace_plot_labels(plot)

Arguments

See Also

set_var_labels() for setting individual variable labels, set_labels() for setting variable labels using a data frame, extract_labels() for creating a data frame of all variable labels, clear_labels() for removing variable labels

Examples

## Not run: 
data("pembrolizumab")
p <- ggplot(pembrolizumab,aes(x=change_ctdna_group,y=baseline_ctdna)) +
geom_boxplot()
replace_plot_labels(p)
pembrolizumab <- set_var_labels(pembrolizumab,
change_ctdna_group="Change in ctDNA group")
p <- ggplot(pembrolizumab,aes(x=change_ctdna_group,y=baseline_ctdna)) +
geom_boxplot()
replace_plot_labels(p)
# Can also be used with a pipe, but expression needs to be wrapped in a brace
(ggplot(pembrolizumab,aes(x=change_ctdna_group,y=baseline_ctdna)) +
geom_boxplot()) |> replace_plot_labels()

## End(Not run)

Summarize cumulative incidence by group

Description

Displays event counts and event rates at specified time points for the entire cohort and by group. Gray's test of differences in cumulative incidence is displayed.

Usage

rm_cifsum(
  data,
  time,
  status,
  group = NULL,
  eventcode = 1,
  cencode = 0,
  eventtimes,
  eventtimeunit,
  eventtimeLbls = NULL,
  CIwidth = 0.95,
  unformattedp = FALSE,
  na.action = "na.omit",
  showCounts = TRUE,
  showGraystest = TRUE,
  digits = 2,
  caption = NULL,
  tableOnly = FALSE
)

Arguments

Value

A character vector of the event table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

library(survival)
data(pbc)

# Event probabilities at various time points with replacement time labels
rm_cifsum(data=pbc,time='time',status='status',
eventtimes=c(1825,3650),eventtimeLbls=c(5,10),eventtimeunit='yr')

# Event probabilities by one group
rm_cifsum(data=pbc,time='time',status='status',group='trt',
eventtimes=c(1825,3650),eventtimeunit='day')


# Event probabilities by multiple groups
rm_cifsum(data=pbc,time='time',status='status',group=c('trt','sex'),
eventtimes=c(1825,3650),eventtimeunit='day')

Output a compact summary table

Description

Outputs a table formatted for pdf, word or html output with summary statistics

Usage

rm_compactsum(
  data,
  xvars,
  grp,
  use_mean,
  caption = NULL,
  tableOnly = FALSE,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  nicenames = TRUE,
  iqr = TRUE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  show.sumstats = FALSE,
  show.tests = FALSE,
  full = TRUE,
  percentage = "col"
)

Arguments

Details

Comparisons for categorical variables default to chi-square tests, but if there are counts of <5 then the Fisher Exact test will be used. For grouping variables with two levels, either t-tests (mean) or wilcoxon tests (median) will be used for numerical variables. Otherwise, ANOVA (mean) or Kruskal- Wallis tests will be used. The statistical test used can be displayed by specifying show.tests = TRUE. Statistical tests and effect sizes for grp and/ or xvars with less than 2 counts in any level will not be shown.

Effect sizes are calculated as Cohen d for between group differences if the variable is summarised with the mean, otherwise Wilcoxon R if summarised with a median. Cramer's V is used for categorical variables, omega is used for differences in means among more than two groups and epsilon for differences in medians among more than two groups. Confidence intervals are calculated using bootstrapping.

tidyselect can only be used for xvars and grp arguments. Additional arguments (digits, use_mean) must be passed in using characters if variable names are used.

Value

A character vector of the table source code, unless tableOnly = TRUE in which case a data frame is returned. The output has the following attribute:

• "description", which describes what is included in the output table and the type of statistical summary for each covariate. When applicable, the types of statistical tests used will be included. If effSize = TRUE, the effect sizes for each covariate will also be mentioned.

References

Smithson, M. (2002). Noncentral Confidence Intervals for Standardized Effect Sizes. (07/140 ed., Vol. 140). SAGE Publications. doi:10.4135/9781412983761.n4

Steiger, J. H. (2004). Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis. Psychological Methods, 9(2), 164–182. doi:10.1037/1082-989X.9.2.164

Kelley, T. L. (1935). An Unbiased Correlation Ratio Measure. Proceedings of the National Academy of Sciences - PNAS, 21(9), 554–559. doi:10.1073/pnas.21.9.554

Okada, K. (2013). Is Omega Squared Less Biased? A Comparison of Three Major Effect Size Indices in One-Way ANOVA. Behavior Research Methods, 40(2), 129-147.

Breslow, N. (1970). A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika, 57(3), 579-594.

FRITZ, C. O., MORRIS, P. E., & RICHLER, J. J. (2012). Effect Size Estimates: Current Use, Calculations, and Interpretation. Journal of Experimental Psychology. General, 141(1), 2–18. doi:10.1037/a0024338

Examples

data("pembrolizumab")
rm_compactsum(data = pembrolizumab, xvars = c("age",
"change_ctdna_group", "l_size", "pdl1"), grp = "sex", use_mean = "age",
digits = c("age" = 2, "l_size" = 3), digits.cat = 1, iqr = TRUE,
show.tests = TRUE)

# Other Examples (not run)
## Include the summary statistic in the variable column
#rm_compactsum(data = pembrolizumab, xvars = c("age",
#"change_ctdna_group"), grp = "sex", use_mean = "age", show.sumstats=TRUE)

## To show effect sizes
#rm_compactsum(data = pembrolizumab, xvars = c("age",
#"change_ctdna_group"), grp = "sex", use_mean = "age", digits = 2,
#effSize = TRUE, show.tests = TRUE)

## To return unformatted p-values
#rm_compactsum(data = pembrolizumab, xvars = c("l_size",
#"change_ctdna_group"), grp = "cohort", effSize = TRUE, unformattedp = TRUE)

## Using tidyselect
#pembrolizumab |> rm_compactsum(xvars = c(age, sex, pdl1), grp = cohort,
#effSize = TRUE)

Outputs a descriptive covariate table

Description

Returns a data frame corresponding to a descriptive table.

Usage

rm_covsum(
  data,
  covs,
  maincov = NULL,
  caption = NULL,
  tableOnly = FALSE,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  show.tests = FALSE,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  full = TRUE,
  include_missing = FALSE,
  percentage = c("column", "row"),
  dropLevels = TRUE,
  excludeLevels = NULL,
  numobs = NULL,
  fontsize,
  chunk_label
)

Arguments

Details

Comparisons for categorical variables default to chi-square tests, but if there are counts of <5 then the Fisher Exact test will be used and if this is unsuccessful then a second attempt will be made computing p-values using MC simulation. If testcont='ANOVA' then the t-test with unequal variance will be used for two groups and an ANOVA will be used for three or more. The statistical test used can be displayed by specifying show.tests=TRUE.

Effect size can be obtained when p-value is requested.

Further formatting options are available using tableOnly=TRUE and outputting the table with a call to outTable.

A newer version of this function is rm_compactsum which is more flexible and displays few rows of output.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

References

Ellis, P.D. (2010) The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.doi:10.1017/CBO9780511761676

Lakens, D. (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4; 863:1-12. doi:10.3389/fpsyg.2013.00863

See Also

covsum,fisher.test, chisq.test, wilcox.test, kruskal.test, anova, rstatix::cramer_v, rstatix:eta_squared, and outTable

Examples

data("pembrolizumab")
rm_covsum(data=pembrolizumab, maincov = 'orr',
covs=c('age','sex','pdl1','tmb','l_size','change_ctdna_group'),
show.tests=TRUE)

# To Show Effect Sizes
rm_covsum(data=pembrolizumab, maincov = 'orr',
covs=c('age','sex'),
effSize=TRUE)

# To make custom changes or change the fontsize in PDF/HTML
tab <- rm_covsum(data=pembrolizumab,maincov = 'change_ctdna_group',
covs=c('age','sex','pdl1','tmb','l_size'),show.tests=TRUE,tableOnly = TRUE)
outTable(tab, fontsize=7)

# To return unformatted p-values
tab <- rm_covsum(data=pembrolizumab, maincov = 'orr',
covs=c('age','sex','pdl1','tmb','l_size','change_ctdna_group'),
show.tests=TRUE,unformattedp=TRUE,tableOnly=TRUE)
outTable(tab,digits=5)
outTable(tab,digits=5, applyAttributes=FALSE) # remove bold/indent

Format a regression model nicely for 'Rmarkdown'

Description

Multivariable (or univariate) regression models are re-formatted for reporting and a global p-value is added for the evaluation of factor variables.

Multivariable (or univariate) regression models are re-formatted for reporting and a global p-value is added for the evaluation of factor variables.

Usage

rm_mvsum(
  model,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  vif = TRUE,
  whichp = c("levels", "global", "both"),
  caption = NULL,
  tableOnly = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  nicenames = TRUE,
  chunk_label,
  fontsize
)

rm_mvsum(
  model,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  vif = TRUE,
  whichp = c("levels", "global", "both"),
  caption = NULL,
  tableOnly = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  nicenames = TRUE,
  chunk_label,
  fontsize
)

Arguments

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned. For negative binomial models a deviance test is used.

If the variance inflation factor is requested (VIF=T, default) then a generalised VIF will be calculated in the same manner as the car package.

As of version 0.1.1 if global p-values are requested they will be included in the p-value column.

As of R 4.4.0 profile likelihood confidence intervals will be calculated automatically and there is no longer an option to force Wald tests.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned. For negative binomial models a deviance test is used.

If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

References

John Fox & Georges Monette (1992) Generalized Collinearity Diagnostics, Journal of the American Statistical Association, 87:417, 178-183, doi:10.1080/01621459.1992.10475190

John Fox and Sanford Weisberg (2019). An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage.

John Fox & Georges Monette (1992) Generalized Collinearity Diagnostics, Journal of the American Statistical Association, 87:417, 178-183, doi:10.1080/01621459.1992.10475190

John Fox and Sanford Weisberg (2019). An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage.

Examples

data("pembrolizumab")
glm_fit = glm(change_ctdna_group~sex:age+baseline_ctdna+l_size,
data=pembrolizumab,family = 'binomial')
rm_mvsum(glm_fit)

#linear model with p-value adjustment
lm_fit=lm(baseline_ctdna~age+sex+l_size+tmb,data=pembrolizumab)
rm_mvsum(lm_fit,p.adjust = "bonferroni")
#Coxph
require(survival)
res.cox <- coxph(Surv(os_time, os_status) ~ sex+age+l_size+tmb, data = pembrolizumab)
rm_mvsum(res.cox, vif=TRUE)
data("pembrolizumab")
glm_fit = glm(change_ctdna_group~sex:age+baseline_ctdna+l_size,
data=pembrolizumab,family = 'binomial')
rm_mvsum(glm_fit)

#linear model with p-value adjustment
lm_fit=lm(baseline_ctdna~age+sex+l_size+tmb,data=pembrolizumab)
rm_mvsum(lm_fit,p.adjust = "bonferroni")
#Coxph
require(survival)
res.cox <- coxph(Surv(os_time, os_status) ~ sex+age+l_size+tmb, data = pembrolizumab)
rm_mvsum(res.cox, vif=TRUE)

Display event counts, expected event counts and logrank test of differences

Description

This is a wrapper function around the survdiff function to display overall event rates and group-specific rates along with the log-rank test of a difference in survival between groups in a single table suitable for markdown output. Median survival times are included by default but can be removed setting median=FALSE

Usage

rm_survdiff(
  data,
  time,
  status,
  covs,
  strata,
  includeVarNames = FALSE,
  digits = 1,
  showCols = c("N", "Observed", "Expected"),
  CIwidth = 0.95,
  conf.type = "log",
  caption = NULL,
  tableOnly = FALSE,
  fontsize
)

Arguments

Value

A character vector of the survival table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

survival::survdiff

Examples

#' # Differences between sex
data("pembrolizumab")
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs='sex',digits=1)

# Differences between sex, stratified by cohort
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs='sex',strata='cohort',digits=1)
# Differences between sex/cohort groups
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs=c('sex','cohort'),digits=1)

Summarise survival data by group

Description

Displays event counts, median survival time and survival rates at specified times points for the entire cohort and by group. The logrank test of differences in survival curves is displayed.

Usage

rm_survsum(
  data,
  time,
  status,
  group = NULL,
  survtimes = NULL,
  survtimeunit,
  survtimesLbls = NULL,
  CIwidth = 0.95,
  unformattedp = FALSE,
  conf.type = "log",
  na.action = "na.omit",
  showCounts = TRUE,
  showLogrank = TRUE,
  eventProb = FALSE,
  digits = getOption("reportRmd.digits", 2),
  caption = NULL,
  tableOnly = FALSE,
  fontsize
)

Arguments

Details

This summary table is supplied for simple group comparisons only. To examine differences in groups with stratification see rm_survdiff. To summarise differences in survival rates controlling for covariates see rm_survtime.

Value

A character vector of the survival table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

survival::survfit

Examples

# Simple median survival table
data("pembrolizumab")
rm_survsum(data=pembrolizumab,time='os_time',status='os_status')

# Survival table with yearly survival rates
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
survtimes=c(12,24),survtimesLbls=1:2, survtimeunit='yr')

#Median survival by group
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',group='sex')

# Survival Summary by cohort, displayed in years
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
group="cohort",survtimes=seq(12,72,12),
survtimesLbls=seq(1,6,1),
survtimeunit='years')

# Survival Summary by Sex and ctDNA group
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
group=c('sex','change_ctdna_group'),survtimes=c(12,24),survtimeunit='mo')

Display survival rates and events for specified times

Description

This is a wrapper for the survfit function to output a tidy display for reporting. Either Kaplan Meier or Cox Proportional Hazards models may be used to estimate the survival probabilities.

Usage

rm_survtime(
  data,
  time,
  status,
  covs = NULL,
  strata = NULL,
  type = "KM",
  survtimes,
  survtimeunit,
  strata.prefix = NULL,
  survtimesLbls = NULL,
  showCols = c("At Risk", "Events", "Censored"),
  CIwidth = 0.95,
  conf.type = "log",
  na.action = "na.omit",
  showCounts = TRUE,
  digits = getOption("reportRmd.digits", 2),
  caption = NULL,
  tableOnly = FALSE,
  fontsize
)

Arguments

Details

If covariates are supplied then a Cox proportional hazards model is fit for the entire cohort and each strata. Otherwise the default is for Kaplan-Meier estimates. Setting type = 'PH' will force a proportional hazards model.

Value

A character vector of the survival table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

survival::survfit

Examples

# Kaplan-Mieir survival probabilities with time displayed in years
data("pembrolizumab")
rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
strata="cohort",type='KM',survtimes=seq(12,72,12),
survtimesLbls=seq(1,6,1),
survtimeunit='years')

# Cox Proportional Hazards survivial probabilities
rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
strata="cohort",type='PH',survtimes=seq(12,72,12),survtimeunit='months')

# Cox Proportional Hazards survivial probabilities controlling for age
rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
covs='age',strata="cohort",survtimes=seq(12,72,12),survtimeunit='months')

Combine univariate and multivariable regression tables

Description

This function will combine rm_uvsum and rm_mvsum outputs into a single table. The tableOnly argument must be set to TRUE when tables to be combined are created. The resulting table will be in the same order as the uvsum table and will contain the same columns as the uvsum and mvsum tables, but the p-values will be combined into a single column. There must be a variable overlapping between the uvsum and mvsum tables and all variables in the mvsum table must also appear in the uvsum table.

Usage

rm_uv_mv(
  uvsumTable,
  mvsumTable,
  covTitle = "",
  vif = FALSE,
  showN = FALSE,
  showEvent = FALSE,
  caption = NULL,
  tableOnly = FALSE,
  chunk_label,
  fontsize
)

Arguments

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

rm_uvsum,rm_mvsum

Examples

require(survival)
data("pembrolizumab")
uvTab <- rm_uvsum(response = c('os_time','os_status'),
covs=c('age','sex','baseline_ctdna','l_size','change_ctdna_group'),
data=pembrolizumab,tableOnly=TRUE)
mv_surv_fit <- coxph(Surv(os_time,os_status)~age+sex+
baseline_ctdna+l_size+change_ctdna_group, data=pembrolizumab)
uvTab <- rm_mvsum(mv_surv_fit)

#linear model
uvtab<-rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,tableOnly=TRUE)
lm_fit=lm(baseline_ctdna~age+sex+l_size+tmb,data=pembrolizumab)
mvtab<-rm_mvsum(lm_fit,tableOnly = TRUE)
rm_uv_mv(uvtab,mvtab,tableOnly=TRUE)

#logistic model
uvtab<-rm_uvsum(response = 'os_status',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,family = binomial,tableOnly=TRUE)
logis_fit<-glm(os_status~age+sex+l_size+pdl1+tmb,data = pembrolizumab,family = 'binomial')
mvtab<-rm_mvsum(logis_fit,tableOnly = TRUE)
rm_uv_mv(uvtab,mvtab,tableOnly=TRUE)

Output several univariate models nicely in a single table

Description

#'A table with the model parameters from running separate univariate models on each covariate. For factors with more than two levels a Global p-value is returned.

A table with the model parameters from running separate univariate models on each covariate. For factors with more than two levels a Global p-value is returned.

Usage

rm_uvsum(
  response,
  covs,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  caption = NULL,
  tableOnly = FALSE,
  removeInf = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  whichp = c("levels", "global", "both"),
  chunk_label,
  gee = FALSE,
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset = NULL,
  strata = 1,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  fontsize,
  forceWald = FALSE
)

rm_uvsum(
  response,
  covs,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  caption = NULL,
  tableOnly = FALSE,
  removeInf = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  whichp = c("levels", "global", "both"),
  chunk_label,
  gee = FALSE,
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset = NULL,
  strata = 1,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  fontsize,
  forceWald = FALSE
)

Arguments

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned.

As of version 0.1.1 if global p-values are requested they will be included in the p-value column.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

tidyselect can only be used for response and covs variables. Additional arguments must be passed in using characters

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

uvsum,lm,glm, cmprsk::crr, survival::coxph, nlme::lme, geepack::geeglm, MASS::glm.nb

covsum,fisher.test, chisq.test, wilcox.test, kruskal.test, anova, rstatix::cramer_v, rstatix:eta_squared, and outTable

Examples

# Examples are for demonstration and are not meaningful
# Coxph model with 90% CI
data("pembrolizumab")
rm_uvsum(response = c('os_time','os_status'),
covs=c('age','sex','baseline_ctdna','l_size','change_ctdna_group'),
data=pembrolizumab,CIwidth=.9)

# Linear model with default 95% CI
rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab)

# Logistic model with default 95% CI
rm_uvsum(response = 'os_status',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,family = binomial)
# Poisson models returned as model list
mList <- rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab, returnModels=TRUE)
#'
# GEE on correlated outcomes
data("ctDNA")
rm_uvsum(response = 'size_change',
covs=c('time','ctdna_status'),
gee=TRUE,
id='id', corstr="exchangeable",
family=gaussian("identity"),
data=ctDNA,showN=TRUE)

# Using tidyselect
pembrolizumab |> rm_uvsum(response = sex,
covs = c(age, cohort))
# Examples are for demonstration and are not meaningful
# Coxph model with 90% CI
data("pembrolizumab")
rm_uvsum(response = c('os_time','os_status'),
covs=c('age','sex','baseline_ctdna','l_size','change_ctdna_group'),
data=pembrolizumab,CIwidth=.9)

# Linear model with default 95% CI
rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab)

# Logistic model with default 95% CI
rm_uvsum(response = 'os_status',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,family = binomial)
# Poisson models returned as model list
mList <- rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab, returnModels=TRUE)
#'
# GEE on correlated outcomes
data("ctDNA")
rm_uvsum(response = 'size_change',
covs=c('time','ctdna_status'),
gee=TRUE,
id='id', corstr="exchangeable",
family=gaussian("identity"),
data=ctDNA,showN=TRUE)

Replace dollar signs with html for proper HTML output

Description

Replace dollar signs with html for proper HTML output

Usage

rmds(s)

Arguments

Sanitizes strings to not break LaTeX

Description

Strings with special characters will break LaTeX if returned 'asis' by knitr. This happens every time we use one of the main reportRmd functions. We first sanitize our strings with this function to stop LaTeX from breaking.

Usage

sanitizestr(str)

Arguments

Output a scrollable table

Description

This function accepts the output of a aa call to knitr::kable or reportRmd::outTable and, if the output format is html, will produce a scrollable table. Otherwise a regular table will be output for pandoc/latex

Usage

scrolling_table(knitrTable, pixelHeight = 500)

Arguments

Examples

data("pembrolizumab")
tab <- rm_covsum(data=pembrolizumab,maincov = 'change_ctdna_group',
covs=c('age','cohort','sex','pdl1','tmb','l_size'),full=F)
scrolling_table(tab,pixelHeight=300)

Set variable labels

Description

Assign variable labels to a data.frame from a lookup table.

Usage

set_labels(data, names_labels)

Arguments

Details

Useful if variable labels have been imported from a data dictionary. The first column in names_labels must contain the variable name and the second column the variable label. The column names are not used.

If no label is provided then the existing label will not be changed. To remove a label set the label to NA.

See Also

set_var_labels() for setting individual variable labels, extract_labels() for creating a data frame of all variable labels, clear_labels() for removing variable labels

Examples

data("ctDNA")
# create data frame with labels
lbls <- data.frame(c1=c('cohort','size_change'),
c2=c('Cancer cohort','Change in tumour size'))
# set labels and return labelled data frame
set_labels(ctDNA,lbls)

Set variable labels

Description

Set variable labels for a data frame using name-label pairs.

Usage

set_var_labels(data, ...)

Arguments

Details

If no label is provided for a variable then the existing label will not be changed. To remove a label set the label to NA.

See Also

set_labels() for setting variable labels using a data frame, extract_labels() for creating a data frame of all variable labels, clear_labels() for removing variable labels

Examples

# set labels using name-label pairs
# and return labelled data frame
data("ctDNA")
ctDNA |> set_var_labels(
   ctdna_status="detectable ctDNA",
  cohort="A cohort label")

Aligning models and parameters

Description

Aligning models and parameters

Usage

uvmodels

Format

A data frame with 16 rows and 5 variables:

type

model type

family

model linking family

gee

boolean indicating if gee are used

autoreg_class

class of model fit

beta

description of output parameter

Source

internal

Get univariate summary dataframe

Description

Returns a dataframe corresponding to a univariate regression table

Usage

uvsum(
  response,
  covs,
  data,
  digits = getOption("reportRmd.digits", 2),
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset = NULL,
  gee = FALSE,
  strata = 1,
  markup = TRUE,
  sanitize = TRUE,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  forceWald
)

Arguments

Details

Univariate summaries for a number of covariates, the type of model can be specified. If unspecified the function will guess the appropriate model based on the response variable.

Confidence intervals are extracted using confint where possible. Otherwise Student t distribution is used for linear models and the Normal distribution is used for proportions.

returnModels can be used to return a list of the univariate models, which will be the same length as covs. The data used to run each model will include all cases with observations on the response and covariate. For gee models the data are re-ordered so that the ids appear sequentially and proper estimates are given.

See Also

lm,glm,cmprsk::crr,survival::coxph, nlme::lme,geepack::geeglm,MASS::polr,MASS::glm.nb

Create a summary table for an individual covariate

Description

Create a summary table for an individual covariate

Usage

xvar_function(
  xvar,
  data,
  grp,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  iqr = TRUE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  show.tests = FALSE,
  percentage = "col"
)

Arguments

Value

A data frame is returned

Helper function: xvar_function.default

Description

Helper function: xvar_function.default

Usage

## Default S3 method:
xvar_function(xvar, ...)

Arguments