The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

Survival curves have been improved and now return ggplots
Variable labels will now be automatically output in tables

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

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	VIF
ctDNA change from baseline to cycle 3		0.009	73	58	1.00
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	1.18
cohort		0.004	73	58	1.04
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, NA)	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, NA)	0.61 (0.42, 0.88)	0.44 (0.27, 0.74)
D	4/12	NA (6.44, NA)	0.67 (0.45, 0.99)	0.67 (0.45, 0.99)
E	20/30	14.26 (9.69, NA)	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
forestplot2(glm_fit)
#> Warning: `forestplot2()` was deprecated in reportRmd 0.1.0.
#> ℹ Please use `forestplotUV()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.