reportRmd

The goal of reportRmd is to automate the reporting of clinical data in Quarto/Rmarkdown environments. Functions include table one-style summary statistics, compilation of multiple univariate models, tidy output of multivariable models and side by side comparisons of univariate and multivariable models. Plotting functions include customisable survival curves, forest plots, and automated bivariate plots.

Installation

Installing from CRAN:

install.packages('reportRmd')

You can install the development version of reportRmd from GitHub with:

# install.packages("devtools")
devtools::install_github("biostatsPMH/reportRmd", ref="development", build_vignettes = TRUE)

New Features (v0.1.3)

rm_mvsum() and forestplotMV() now support include_unadjusted=TRUE to display univariate (unadjusted) and multivariable (adjusted) estimates side-by-side
forestplotMV() data parameter is now optional - data extracted automatically from model objects

Documentation

For the CRAN version:

Online Documentation

For the Development version run the following and select HTML on the webpage

browseVignettes("reportRmd")

Examples

Summary statistics by Sex

library(reportRmd)
data("pembrolizumab")

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

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value	StatTest
Age at study entry				0.30	Wilcoxon Rank Sum
Mean (sd)	57.9 (12.8)	56.9 (12.6)	59.3 (13.1)
Median (Min,Max)	59.1 (21.1, 81.8)	56.6 (34.1, 78.2)	61.2 (21.1, 81.8)
PD L1 percent				0.76	Wilcoxon Rank Sum
Mean (sd)	13.9 (29.2)	15.0 (30.5)	12.1 (27.3)
Median (Min,Max)	0 (0, 100)	0.5 (0.0, 100.0)	0 (0, 100)
Missing	1	0	1
Did ctDNA increase or decrease from baseline to cycle 3				0.84	Chi Sq
Decrease from baseline	33 (45)	19 (48)	14 (42)
Increase from baseline	40 (55)	21 (52)	19 (58)
Missing	21	18	3

Compact Table

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

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value	Missing
Age at study entry	59.1 (49.5-68.7)	56.6 (45.8-67.8)	61.2 (52.0-69.4)	0.30	0
PD L1 percent	0.0 (0.0-10.0)	0.5 (0.0-13.8)	0.0 (0.0-4.5)	0.76	1
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline	40 (55%)	21 (52%)	19 (58%)	0.84	21

Switching between function

As of v0.1.3 you can now use xvars and grp as aliases for covs and maincov in rm_covsum.

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

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value	StatTest
Age at study entry				0.30	Wilcoxon Rank Sum
Mean (sd)	57.9 (12.8)	56.9 (12.6)	59.3 (13.1)
Median (Min,Max)	59.1 (21.1, 81.8)	56.6 (34.1, 78.2)	61.2 (21.1, 81.8)
PD L1 percent				0.76	Wilcoxon Rank Sum
Mean (sd)	13.9 (29.2)	15.0 (30.5)	12.1 (27.3)
Median (Min,Max)	0 (0, 100)	0.5 (0.0, 100.0)	0 (0, 100)
Missing	1	0	1
Did ctDNA increase or decrease from baseline to cycle 3				0.84	Chi Sq
Decrease from baseline	33 (45)	19 (48)	14 (42)
Increase from baseline	40 (55)	21 (52)	19 (58)
Missing	21	18	3

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

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value	StatTest
Age at study entry				0.30	Wilcoxon Rank Sum
Mean (sd)	57.9 (12.8)	56.9 (12.6)	59.3 (13.1)
Median (Min,Max)	59.1 (21.1, 81.8)	56.6 (34.1, 78.2)	61.2 (21.1, 81.8)
PD L1 percent				0.76	Wilcoxon Rank Sum
Mean (sd)	13.9 (29.2)	15.0 (30.5)	12.1 (27.3)
Median (Min,Max)	0 (0, 100)	0.5 (0.0, 100.0)	0 (0, 100)
Missing	1	0	1
Did ctDNA increase or decrease from baseline to cycle 3				0.84	Chi Sq
Decrease from baseline	33 (45)	19 (48)	14 (42)
Increase from baseline	40 (55)	21 (52)	19 (58)
Missing	21	18	3

Using Variable Labels

var_names <- data.frame(var=c("age","pdl1","change_ctdna_group"),
                          label=c('Age at study entry',
                                  'PD L1 percent',
                                  'ctDNA change from baseline to cycle 3'))

pembrolizumab <- set_labels(pembrolizumab,var_names)

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

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value
Age at study entry				0.30
Mean (sd)	57.9 (12.8)	56.9 (12.6)	59.3 (13.1)
Median (Min,Max)	59.1 (21.1, 81.8)	56.6 (34.1, 78.2)	61.2 (21.1, 81.8)
PD L1 percent				0.76
Mean (sd)	13.9 (29.2)	15.0 (30.5)	12.1 (27.3)
Median (Min,Max)	0 (0, 100)	0.5 (0.0, 100.0)	0 (0, 100)
Missing	1	0	1
ctDNA change from baseline to cycle 3				0.84
Decrease from baseline	33 (45)	19 (48)	14 (42)
Increase from baseline	40 (55)	21 (52)	19 (58)
Missing	21	18	3

Multiple Univariate Regression Analyses

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

	OR(95%CI)	p-value	N	Event
Age at study entry	0.96 (0.91, 1.00)	0.089	94	78
PD L1 percent	0.97 (0.95, 0.98)	<0.001	93	77
ctDNA change from baseline to cycle 3			73	58
Decrease from baseline	Reference		33	19
Increase from baseline	28.74 (5.20, 540.18)	0.002	40	39

Tidy multivariable analysis

glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
               family='binomial',
               data = pembrolizumab)
rm_mvsum(glm_fit,showN=T)

	OR(95%CI)	p-value	N	Event	VIF
ctDNA change from baseline to cycle 3			73	58	1.03
Decrease from baseline	Reference		33	19
Increase from baseline	23.92 (3.69, 508.17)	0.006	40	39
PD L1 percent	0.97 (0.95, 0.99)	0.011	73	58	1.24
Age at study entry	0.94 (0.87, 1.00)	0.078	73	58	1.23

Combining univariate and multivariable models

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,tableOnly = TRUE)

rm_uv_mv(uvsumTable,mvsumTable)

	Unadjusted OR(95%CI)	p	Adjusted OR(95%CI)	p (adj)
Age at study entry	0.96 (0.91, 1.00)	0.089
sex
Female	Reference
Male	0.41 (0.13, 1.22)	0.11
PD L1 percent	0.97 (0.95, 0.98)	<0.001	0.98 (0.95, 1.00)	0.024
ctDNA change from baseline to cycle 3
Decrease from baseline	Reference		Reference
Increase from baseline	28.74 (5.20, 540.18)	0.002	24.71 (4.19, 479.13)	0.004

Simple survival summary table

Shows events, median survival, survival rates at different times and the log rank test.

 rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
 group="cohort",survtimes=c(12,24),
 survtimesLbls=c(1,2),
 survtimeunit='yr')

Group	Events/Total	Median (95%CI)	1yr (95% CI)	2yr (95% CI)
A	12/16	8.30 (4.24, Not Estimable)	0.38 (0.20, 0.71)	0.23 (0.09, 0.59)
B	16/18	8.82 (4.67, 20.73)	0.32 (0.16, 0.64)	0.06 (9.6e-03, 0.42)
C	12/18	17.56 (7.95, Not Estimable)	0.61 (0.42, 0.88)	0.44 (0.27, 0.74)
D	4/12	Not Estimable (6.44, Not Estimable)	0.67 (0.45, 0.99)	0.67 (0.45, 0.99)
E	20/30	14.26 (9.69, Not Estimable)	0.63 (0.48, 0.83)	0.34 (0.20, 0.57)
		Log Rank Test	ChiSq	11.3 on 4 df
			p-value	0.023

Summarise Cumulative incidence

library(survival)
data(pbc)
rm_cifsum(data=pbc,time='time',status='status',group=c('trt','sex'),
eventtimes=c(1825,3650),eventtimeunit='day')
#> 106 observations with missing data were removed.

Strata	Event/Total	1825day (95% CI)	3650day (95% CI)
1, f	7/137	0.04 (0.01, 0.08)	0.06 (0.03, 0.12)
1, m	3/21	0.10 (0.02, 0.27)	0.16 (0.03, 0.36)
2, f	9/139	0.05 (0.02, 0.09)	0.09 (0.04, 0.17)
2, m	0/15	0e+00 (NA, NA)	0e+00 (NA, NA)
	Gray’s Test	ChiSq	3.3 on 3 df
		p-value	0.35

Plotting survival curves

ggkmcif2(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)
#> Warning: Vectorized input to `element_text()` is not officially supported.
#> ℹ Results may be unexpected or may change in future versions of ggplot2.

Plotting odds ratios

Forest plots can display multivariable results, or include univariate estimates for comparison:

require(ggplot2)
#> Loading required package: ggplot2
# Multivariable only
forestplotMV(glm_fit)
#> Warning: Vectorized input to `element_text()` is not officially supported.
#> ℹ Results may be unexpected or may change in future versions of ggplot2.

# With unadjusted estimates
forestplotMV(glm_fit, data = pembrolizumab, include_unadjusted = TRUE)
#> Fitting univariate models for each predictor
#> Note: Adjusted model N=73 may differ from unadjusted model N=93 due to missing data in covariates
#> Warning: Vectorized input to `element_text()` is not officially supported.
#> ℹ Results may be unexpected or may change in future versions of ggplot2.

Plotting bivariate relationships

These plots are designed for quick inspection of many variables, not for publication.

require(ggplot2)
plotuv(data=pembrolizumab, response='orr',
covs=c('age','cohort','pdl1','change_ctdna_group'))
#> Boxplots not shown for categories with fewer than 20 observations.
#> Boxplots not shown for categories with fewer than 20 observations.

Replacing variable names with labels in ggplot

data("mtcars")
mtcars <- mtcars |> 
  dplyr::mutate(cyl = as.factor(cyl)) |>
  set_labels(data.frame(var=c("hp","mpg","cyl"),
                        label=c('Horsepower',
                                'Miles per gallon',
                                'Number of cylinders'))) 
p <- mtcars |> 
ggplot(aes(x=hp, y=mpg, color=cyl, shape=cyl)) +
    geom_point()
replace_plot_labels(p)

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reportRmd

Installation

New Features (v0.1.3)

Documentation

Examples

Summary statistics by Sex

Compact Table

Switching between function

Using Variable Labels

Multiple Univariate Regression Analyses

Tidy multivariable analysis

Combining univariate and multivariable models

Simple survival summary table

Summarise Cumulative incidence

Plotting survival curves

Plotting odds ratios

Plotting bivariate relationships

Replacing variable names with labels in ggplot

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reportRmd

Installation

New Features (v0.1.3)

Documentation

Examples

Summary statistics by Sex

Compact Table

Switching between function

Using Variable Labels

Multiple Univariate Regression Analyses

Tidy multivariable analysis

Combining univariate and multivariable models

Simple survival summary table

Summarise Cumulative incidence

Plotting survival curves

Plotting odds ratios

Plotting bivariate relationships

Replacing variable names with labels in ggplot