reportRmd Package

In reportRmd: Tidy Presentation of Clinical Reporting

library(reportRmd)
# knitr::opts_chunk$set(message = FALSE, warning = FALSE,dev="cairo_pdf")
knitr::opts_chunk$set(message = FALSE, warning = FALSE)

Overview

reportRmd is a package designed to facilitate the reporting of common statistical outputs easily in RMarkdown documents. The package supports pdf, html and word output without any changes to the body of the report. The main features are Table 1 style summaries, combining multiple univariate regression models into a single table, tidy multivariable model output and combining univariate and multivariable regressions into a single table. Single table summaries of median survival times and survival probabilities are also provided. A highly customisable survival curve function, based on ggplot2 can be used to create publication-quality plots. Visualisation plots are also available for bivariate relationships and logistic regression models.

A word of caution:

The reportRmd package is designed to facilitate statistical reporting and does not provide any checks regarding the suitability of the models fit.

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Summary statistics

Basic summary statistics

data("pembrolizumab")
rm_covsum(data=pembrolizumab, 
covs=c('age','sex'))

Set IQR = T for interquartile range instead of Min/Max

rm_covsum(data=pembrolizumab, 
covs=c('age','sex'),IQR=TRUE)

Or all.stats=T for both

rm_covsum(data=pembrolizumab, 
covs=c('age','sex'),all.stats = TRUE)

Summaries By Group

This will produce summary statistics by Sex

rm_covsum(data=pembrolizumab, maincov = 'sex',
covs=c('age','pdl1','change_ctdna_group'))

Showing Statistical Tests

To indicate which statistical test was used use show.tests=TRUE

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

Including Effect Sizes

Effect sizes can be added with effSize = TRUE. Effect size measures include the Wilcoxon r for Wilcoxon rank-sum test, Cohen's d for t-test, Omega for ANOVA, Epsilon for Kruskal Wallis test, and Cramer's V for categorical variables.

rm_covsum(data=pembrolizumab, maincov = 'sex',
covs=c('age','change_ctdna_group'),
effSize=TRUE)

Parametric vs Non-parametric Comparisons

Group comparisons are non-parametric by default, specify testcont='ANOVA' for t-tests/ANOVA

rm_covsum(data=pembrolizumab, maincov = 'sex',
covs=c('age','pdl1'),
testcont='ANOVA',
show.tests=TRUE, effSize=TRUE)

Row vs Column Summaries

The default is to indicate percentages by columns (ie. percentages within columns add to 100)

rm_covsum(data=pembrolizumab, maincov = 'sex',
covs=c('cohort'),
pvalue = FALSE)

But you can also specify to show by row instead

rm_covsum(data=pembrolizumab, maincov = 'sex',
covs=c('cohort'),
pvalue = FALSE,
percentage='row')

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Compact summary statistics

Basic summary statistics

rm_compactsum(data = pembrolizumab, xvars = c("change_ctdna_group", "l_size"))

Set iqr = T for interquartile range instead of Min/Max

rm_compactsum(data=pembrolizumab, xvars = c("change_ctdna_group", "l_size"), iqr=TRUE)

Or all.stats=T for both

rm_compactsum(data=pembrolizumab, xvars = c("change_ctdna_group", "l_size"), all.stats = T)

Summaries By Group

This will produce summary statistics by Sex:

rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex')

Showing Statistical Tests

To indicate which statistical test was used use show.tests=TRUE

rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', show.tests=TRUE)

Including Effect Sizes

Effect sizes can be added with effSize = TRUE. If show.tests = TRUE as well, the effStat will also be shown:

rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', effSize = T, show.tests = T)

Using Mean Summary Statistic Instead of Median

The default summary statistic for numerical variables is median. To specify which numerical variables should have mean displayed instead, change the use_mean argument. Unspecified xvars will use the default median

rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', use_mean = c("pdl1"), effSize = T, show.tests = T)

Custom Digits Argument

The digits and digits.cat arguments can be changed to a custom numerical value. The default digits is 1, the default digits.cat is 0

rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', use_mean = c("pdl1"), digits = 2, digits.cat = 1, effSize = T, show.tests = T)

To specify custom digit arguments for different numerical variables, change the digits argument. Unspecified variables will use the default value.

rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','l_size'), grp = 'sex', digits = c("age" = 3, "l_size" = 2), effSize = T, show.tests = T)

Row vs Column Summaries

The default is to indicate percentages by columns (ie. percentages within columns add to 100). But you can also specify to show by row instead

rm_compactsum(data=pembrolizumab, xvars=c('change_ctdna_group','orr'), grp = 'cohort', effSize = T, show.tests = T, percentage = "row")

Viewing the Self-Generated Description

To view the self-generated description for the summary table:

summary_tab <- rm_compactsum(data=pembrolizumab, xvars=c('change_ctdna_group','orr', 'age'), grp = 'cohort', effSize = T, show.tests = T)
cat(attr(summary_tab, "description"))

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Univariate regression

Combining multiple univariate regression analyses into a single table. The function will try to determine the most appropriate model from the data.

rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','pdl1','change_ctdna_group'))

Simple Linear Regression

If the response is continuous linear regression is the default. Using type = 'linear' will ensure linear regression.

rm_uvsum(data=pembrolizumab, response='l_size',
covs=c('age','cohort'))

Logistic Regression

If the response is binomial, logistic regression will be run (or specified with type = 'logistic').

rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','cohort'))

Poisson Regression

If the response is integer, poisson regression will be run (or specified with type = 'poisson').

pembrolizumab$Counts <- rpois(nrow(pembrolizumab),lambda = 3)
rm_uvsum(data=pembrolizumab, response='Counts',covs=c('age','cohort'))

offset terms can be specified as well, but must correspond to a variable in the data set

pembrolizumab$length_followup <- rnorm(nrow(pembrolizumab),mean = 72,sd=3)
pembrolizumab$log_length_followup <- log(pembrolizumab$length_followup)
rm_uvsum(data=pembrolizumab, response='Counts',covs=c('age','cohort'),
         offset = "log_length_followup")

Negative Binomial Regression

To run negative binomial regression instead specify type = 'negbin'

rm_uvsum(data=pembrolizumab, response='Counts', type='negbin',
         covs=c('age','cohort'),
         offset = "log_length_followup")

Survival Analysis

If two response variables are specified and then survival analysis is run (specified with type='coxph').

rm_uvsum(data=pembrolizumab, response=c('os_time','os_status'),
covs=c('age','pdl1','change_ctdna_group'),whichp = "levels")

Competing Risk

Competing risk models need to be explicitly specified using type='crr'.

rm_uvsum(data=pembrolizumab, response=c('os_time','os_status'),
covs=c('age','pdl1','change_ctdna_group'),
type='crr')

GEE Models

Correlated observations can be handled using GEE

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)

Returning Model Objects

If you want to check the underlying models, set returnModels = TRUE

 rm_uvsum(response = 'orr',
 covs=c('age'),
 data=pembrolizumab,returnModels = TRUE)

The data analysed can be examined by interrogating the data object appended to each model

mList <-  rm_uvsum(response = 'orr',
 covs=c('age'),
 data=pembrolizumab,returnModels = TRUE)
head(mList$age$data)

Adjusting p-values

Multiple comparisons can be controlled for with the p.adjust argument, which accepts any of the options from the p.adjust function.

 rm_uvsum(response = 'orr',
 covs=c('age','sex','pdl1'),
 data=pembrolizumab,p.adjust = 'fdr')

Note: The raw p-value column is suppressed when there are categorical variables with >2 levels, to prevent three columns of p-values from appearing.

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Multivariable analysis

To create a nice display for multivariable models the multivariable model first needs to be fit.

By default, the variance inflation factor will be shown to check for multicollinearity. To suppress this column set vif=FALSE. Note: variance inflation factors are not computed (yet) for multilevel or GEE models.

glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
               family='binomial',
               data = pembrolizumab)
rm_mvsum(glm_fit, showN = TRUE, vif=TRUE)

p-values can be adjusted for multiple comparisons using any of the options available in the p.adjust function. This argument is also available for univariate models run with rm_uvsum.

rm_mvsum(glm_fit, showN = TRUE, vif=TRUE,p.adjust = 'holm')

Combining univariate and multivariable models

To display both models in a single table run the rm_uvsum and rm_mvsum functions with tableOnly=TRUE and combine.

uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE)

glm_fit <- glm(orr~change_ctdna_group+pdl1,
               family='binomial',
               data = pembrolizumab)
mvsumTable <- rm_mvsum(glm_fit, showN = TRUE,tableOnly = TRUE)

rm_uv_mv(uvsumTable,mvsumTable)

Note: This can also be done with adjusted p-values, but when combined the raw p-values are dropped.

uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE,p.adjust='holm')

glm_fit <- glm(orr~change_ctdna_group+pdl1,
               family='binomial',
               data = pembrolizumab)
mvsumTable <- rm_mvsum(glm_fit,tableOnly = TRUE,p.adjust='holm')

rm_uv_mv(uvsumTable,mvsumTable)

Changing the output

If you need to make changes to the tables, setting tableOnly=TRUE will return a data frame for any of the rm_ functions. Changes can be made, and the table output using outTable()

mvsumTable <- rm_mvsum(glm_fit, showN = TRUE,tableOnly = TRUE)
names(mvsumTable)[1] <-'Predictor'
outTable(mvsumTable)

Combining tables

Tables can be nested with the nestTable() function

cohortA <- rm_uvsum(data=subset(pembrolizumab,cohort=='A'), 
                     response = 'pdl1',
                     covs=c('age','sex'),
                     tableOnly = T)
cohortA$Cohort <- 'Cohort A'
cohortE <- rm_uvsum(data=subset(pembrolizumab,cohort=='E'), 
                     response = 'pdl1',
                     covs=c('age','sex'),
                     tableOnly = T)
cohortE$Cohort <- 'Cohort E'
nestTable(rbind(cohortA,cohortE),head_col = 'Cohort',to_col = 'Covariate')

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Scrollable Tables

If you are rendering to html format you can use the scrollTable function to create a scrollable table. This is useful for tables with many rows. If the output format is not html then a regular table will be displayed.

long_table <- rm_compactsum(data=pembrolizumab,xvars = c(age,sex,cohort,pdl1,tmb,baseline_ctdna,change_ctdna_group,orr,cbr))
scrolling_table(long_table,pixelHeight = 300)

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Simple Survival Summaries

Displaying survival probabilities at different times by sex using Kaplan Meier estimates

rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
 group="sex",survtimes=seq(12,36,12),survtimeunit='months')

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Survival Times in Long Format

Displaying survival probabilities at different times by sex using Cox PH estimates

rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
 strata="sex",survtimes=c(12,24),survtimeunit='mo',type='PH')

Survival Times With Covariate Adjustments

Displaying survival probabilities at different times by sex, adjusting for age using Cox PH estimates

rm_survtime(data=pembrolizumab,time='os_time',status='os_status', covs='age',
 strata="sex",survtimes=c(12,24),survtimeunit='mo',type='PH')

Stratified Survival Summary

To combine estimates across strata

rm_survdiff(data=pembrolizumab,time='os_time',status='os_status', 
            covs='sex',strata='cohort',digits=1)

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Working with Labels

New in version 0.1.0 is the ability to incorporate variable labels into summary tables (but not yet all plots). If variables contain a label attribute this will be displayed automatically, to disable this set nicenames=F

Variable labels will be shown in the nicenames argument is set to TRUE (the default). Variable labels are set using the label attribute of individual variables (assigned using reportRmd or another package like haven).

reportRmd supports the addition, removal and export of labels using the following functions:

• set_labels will set labels for a data frame from a lookup table
• set_var_labels allows you to set individual variable labels to a data frame
• clear_labels removes all labels from a data frame
• export_labels extracts variable labels from a data frame and returns a data frame of variable names and variable labels

Worked Example

Get some descriptive stats for the ctDNA data that comes with the package. The nicenames argument is TRUE by default so underscores are replaced by spaces

rm_covsum(data=ctDNA,
          covs=c('cohort','ctdna_status','size_change'))

set_labels {#sec-set_labels}

If we have a lookup table of variable names and labels that we imported from a data dictionary we can set the variable labels for the data frame and these will be used in the rm_ functions

ctDNA_names <- data.frame(var=names(ctDNA),
                          label=c('Patient ID',
                                  'Study Cohort',
                                  'Change in ctDNA since baseline',
                                  'Number of weeks on treatment',
                                  'Percentage change in tumour measurement'))
ctDNA <- set_labels(ctDNA,ctDNA_names)

rm_covsum(data=ctDNA,
          covs=c('cohort','ctdna_status','size_change'))

set_var_labels {#sec-set_var_labels}

Individual labels can be changed with with the set_var_labels command

ctDNA <- set_var_labels(ctDNA,
                        cohort="A new cohort label")
rm_covsum(data=ctDNA,
          covs=c('cohort','ctdna_status','size_change'))

extract_labels {#sec-extract_labels}

Extract the variable labels to a data frame

var_labels <- extract_labels(ctDNA)
var_labels

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replace_plot_labels {#sec-replace_plot_labels}

This function will accept a ggplot plot and replace the variable names (x-axis, y-axis and legend) with the variable labels. This is useful for more professional looking plots.

library(ggplot2)
p <- ggplot(data=ctDNA,aes(x=ctdna_status,y=size_change,colour=cohort))+
  geom_point()
replace_plot_labels(p)

clear_labels {#sec-clear_labels}

Or clear them all

ctDNA <- clear_labels(ctDNA)

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Plotting Functions

{width=100%}{width=100%}

Plotting bivariate relationships

These plots are designed for quick inspection of many variables, not for publication. This is the plotting version of rm_uvsum. As of 0.1.1 the variable names will be replaced by variable labels if they exist.

plotuv(data=pembrolizumab, response='orr',
covs=c('age','cohort','pdl1','change_ctdna_group'))

plotuv(data=pembrolizumab, response='orr',
covs=c('age','cohort','pdl1','change_ctdna_group'))
ggsave('images/plotuv.png',scale = 0.5,dpi = 300)

{width=100%}

The plotuv function can also be used without a response variable to display summary of variables

plotuv(data = pembrolizumab, covs=c('age','cohort','pdl1','change_ctdna_group'), showN = T)

plotuv(data = pembrolizumab, covs=c('age','cohort','pdl1','change_ctdna_group'), showN = T)
ggsave('images/plotuv_nores.png',scale = 0.5,dpi = 300)

{width=100%}

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Plotting survival curves

Survival curves are now ggplot2-based, the older version, ggkmcif is deprecated from version 0.1.0

ggkmcif2(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)

ggkmcif2(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)
ggsave('images/ggkmcif.png',dpi=300,width = 5,height = 5)
p <- ggkmcif2(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab,returns = T)
plot(p[[1]])
ggsave('images/ggkmcif_sm.png',scale = 0.5)

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Forest Plots

Similar to rm_uvsum and rm_mvsum, forest plots can be created from univariate or multivariable models. forestplot2 is deprecated from version 0.1.0. Variable labels are not yet incorporated into the forest plots.

This will default to a log scale, but can be set to linear using logScale=FALSE

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

forestplotUV(response="orr", covs=c("change_ctdna_group", "sex", "age", "l_size"),
data=pembrolizumab, family='binomial')
ggsave('images/forestuv.png', scale = 0.5)

{width=100%}

Multivariable Model Forest Plot

glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
               family='binomial',
               data = pembrolizumab)
forestplotMV(glm_fit)

glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
               family='binomial',
               data = pembrolizumab)
forestplotMV(glm_fit)
ggsave('images/forestmv.png', scale = 0.5)

{width=100%}

Combining univariable and multivariable models into a single plot

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)

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)
ggsave('images/forestuvmv.png', scale = 0.5)

{width=100%}

This can also be done with linear scale odds ratios. Number of subjects and/or number of events can also be turned off, as well as different colours used.

uvFP <- forestplotUV(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'))

glm_fit <- glm(orr~change_ctdna_group+pdl1,
               family='binomial',
               data = pembrolizumab)
mvFP <- forestplotMV(glm_fit)

forestplotUVMV(uvFP,mvFP,showN=F,showEvent=F,colours=c("orange","black","blue"),logScale=F)

uvFP <- forestplotUV(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'))

glm_fit <- glm(orr~change_ctdna_group+pdl1,
               family='binomial',
               data = pembrolizumab)
mvFP <- forestplotMV(glm_fit)

forestplotUVMV(uvFP,mvFP,showN=F,showEvent=F,colours=c("orange","black","blue"),logScale=F)
ggsave('images/forestuvmvlin.png', scale = 0.5)

{width=100%}

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Miscellaneous Functions

excelCol

To identify the number of a column given the Excel column header

excelCol(G,AB,Az)

excelColLetters

To identify the Excel header given a columns number

excelColLetters(c(7,28,52))

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Options

The following options can be set:

• reportRmd.digits, default is 2. Sets the default number of digits in output functions. Does not affect p-values.
• reportRmd.forceWald, default is FALSE. If set to TRUE then Wald CI, as opposed to likelihood profile CI will be used in the rm_uvsum function.
• reportRmd.logScale, default is TRUE. The scale of the forest plots will default to log unless otherwise specified.

Example:

 rm_uvsum(response = 'baseline_ctdna',
 covs=c('age','sex','l_size','pdl1','tmb'),
 data=pembrolizumab)

 options('reportRmd.digits'=1) 

rm_uvsum(response = 'baseline_ctdna',
 covs=c('age','sex','l_size','pdl1','tmb'),
 data=pembrolizumab)

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PDF Output

For pdf to be correctly generate when using survival curves it is recommended that the cairo format be used. This can be specified with the following command in the setup code chunk:

knitr::opts_chunk$set(message = FALSE, warning = FALSE,dev="cairo_pdf")

Data Sets

pembrolizumab

Survival status and ctDNA levels for patients receiving pembrolizumab

A data frame with 94 rows and 15 variables:

• id Patient ID
• age Age at study entry
• sex Patient Sex
• cohort Study Cohort: A = Squamous cell carcinoma of soft pallate, B = Triple negative breast cancer, C = Ovarian, high grade serous, D = Melanoma, E = Other Solid Tumor
• 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, 0 = alive, 1 = deceased
• os_time Overall survival time in months
• pfs_status Progression free survival status, 0 = progression free, 1 = progressed
• pfs_time Progression free survival time in months

ctDNA

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

A data frame with 270 rows and 5 variables:

• id Patient ID
• cohort Study Cohort: A = Squamous cell carcinoma of soft pallate, B = Triple negative breast cancer, C = Ovarian, high grade serous, D = Melanoma, E = Other Solid Tumor
• ctdna_status Change in ctDNA since baseline
• time Number of weeks on treatment
• size_change Percentage change in tumour measurement

reportRmd documentation built on April 4, 2025, 2:03 a.m.