reportRmd

The goal of reportRmd is to automate the reporting of clinical data in 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")

New Features

• new compact summary table function rm_compactsum
• main functions are now pipeable
• new function to use variable labels in ggplots replace_plot_labels

Documentation

Online Documentation

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				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)
pdl1				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
change ctdna group				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	59.1 (49.5-68.7)	56.6 (45.8-67.8)	61.2 (52.0-69.4)	0.30	0
pdl1	0.0 (0.0-10.0)	0.5 (0.0-13.8)	0.0 (0.0-4.5)	0.76	1
change ctdna group - Increase from baseline	40 (55%)	21 (52%)	19 (58%)	0.84	21

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'))
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...

	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		0.002	73	58
Decrease from baseline	Reference		33	19
Increase from baseline	28.74 (5.20, 540.18)		40	39

Tidy multivariable analysis

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

	OR(95%CI)	p-value	N	Event
ctDNA change from baseline to cycle 3		0.009	73	58
Decrease from baseline	Reference		33	19
Increase from baseline	19.99 (2.08, 191.60)		40	39
PD L1 percent	0.97 (0.95, 1.00)	0.066	73	58
cohort			73	58
A	Reference		14	11
B	2.6e+07 (0e+00, Inf)	1.00	11	11
C	4.2e+07 (0e+00, Inf)	1.00	10	10
D	0.07 (4.2e-03, 1.09)	0.057	10	3
E	0.44 (0.04, 5.10)	0.51	28	23

Combining univariate and multivariable models

uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE)
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...

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		0.11
Female	Reference
Male	0.41 (0.13, 1.22)
PD L1 percent	0.97 (0.95, 0.98)	\<0.001	0.98 (0.96, 1.00)	0.024
ctDNA change from baseline to cycle 3		0.002		0.004
Decrease from baseline	Reference		Reference
Increase from baseline	28.74 (5.20, 540.18)		24.71 (2.87, 212.70)

Simple survival summary table

Shows events, median survival, survival rates at different times and the log rank test. Does not allow for covariates or strata, just simple tests between groups

 rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
 group="cohort",survtimes=c(12,24),
# group="cohort",survtimes=seq(12,36,12),
# survtimesLbls=seq(1,3,1),
 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)

Plotting odds ratios

require(ggplot2)
#> Loading required package: ggplot2
forestplotMV(glm_fit)
#> Warning in forestplotMV(glm_fit): NAs introduced by coercion

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)