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)
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Full Sample (n=94)
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Female (n=58)
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Male (n=36)
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p-value
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StatTest
|
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age
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|
|
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0.30
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Wilcoxon Rank Sum
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Mean (sd)
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57.9 (12.8)
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56.9 (12.6)
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59.3 (13.1)
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Median (Min,Max)
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59.1 (21.1, 81.8)
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56.6 (34.1, 78.2)
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61.2 (21.1, 81.8)
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pdl1
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0.76
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Wilcoxon Rank Sum
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Mean (sd)
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13.9 (29.2)
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15.0 (30.5)
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12.1 (27.3)
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Median (Min,Max)
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0 (0, 100)
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0.5 (0.0, 100.0)
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0 (0, 100)
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Missing
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1
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0
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1
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change ctdna group
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0.84
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Chi Sq
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Decrease from baseline
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33 (45)
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19 (48)
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14 (42)
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Increase from baseline
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40 (55)
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21 (52)
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19 (58)
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Missing
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21
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18
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3
|
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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'))
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Full Sample (n=94)
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Female (n=58)
|
Male (n=36)
|
p-value
|
|
Age at study entry
|
|
|
|
0.30
|
|
Mean (sd)
|
57.9 (12.8)
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56.9 (12.6)
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59.3 (13.1)
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|
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Median (Min,Max)
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59.1 (21.1, 81.8)
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56.6 (34.1, 78.2)
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61.2 (21.1, 81.8)
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PD L1 percent
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0.76
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Mean (sd)
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13.9 (29.2)
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15.0 (30.5)
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12.1 (27.3)
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Median (Min,Max)
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0 (0, 100)
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0.5 (0.0, 100.0)
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0 (0, 100)
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Missing
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1
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0
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1
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ctDNA change from baseline to cycle
3
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0.84
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Decrease from baseline
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33 (45)
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19 (48)
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14 (42)
|
|
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Increase from baseline
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40 (55)
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21 (52)
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19 (58)
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|
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Missing
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21
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18
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3
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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...
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OR(95%CI)
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p-value
|
N
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Event
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Age at study entry
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0.96 (0.91, 1.00)
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0.089
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94
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78
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PD L1 percent
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0.97 (0.95, 0.98)
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\<0.001
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93
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77
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ctDNA change from baseline to cycle
3
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0.002
|
73
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58
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Decrease from baseline
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Reference
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33
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19
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Increase from baseline
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28.74 (5.20, 540.18)
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|
40
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39
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Tidy multivariable analysis
glm_fit <- glm(orr~change_ctdna_group+pdl1+cohort,
family='binomial',
data = pembrolizumab)
rm_mvsum(glm_fit,showN=T)
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OR(95%CI)
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p-value
|
N
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Event
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VIF
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ctDNA change from baseline to cycle
3
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0.009
|
73
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58
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1.00
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Decrease from baseline
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Reference
|
|
33
|
19
|
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Increase from baseline
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19.99 (2.08, 191.60)
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|
40
|
39
|
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PD L1 percent
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0.97 (0.95, 1.00)
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0.066
|
73
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58
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1.18
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cohort
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0.004
|
73
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58
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1.04
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A
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Reference
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14
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11
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B
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2.6e+07 (0e+00, Inf)
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1.00
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11
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11
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C
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4.2e+07 (0e+00, Inf)
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1.00
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10
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10
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D
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0.07 (4.2e-03, 1.09)
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0.057
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10
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3
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E
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0.44 (0.04, 5.10)
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0.51
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28
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23
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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)
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Unadjusted OR(95%CI)
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p
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Adjusted OR(95%CI)
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p (adj)
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Age at study entry
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0.96 (0.91, 1.00)
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0.089
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sex
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0.11
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Female
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Reference
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Male
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0.41 (0.13, 1.22)
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PD L1 percent
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0.97 (0.95, 0.98)
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\<0.001
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0.98 (0.96, 1.00)
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0.024
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ctDNA change from baseline to cycle
3
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0.002
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|
0.004
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Decrease from baseline
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Reference
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Reference
|
|
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Increase from baseline
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28.74 (5.20, 540.18)
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24.71 (2.87, 212.70)
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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')
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Group
|
Events/Total
|
Median (95%CI)
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1yr (95% CI)
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2yr (95% CI)
|
|
A
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12/16
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8.30 (4.24, NA)
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0.38 (0.20, 0.71)
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0.23 (0.09, 0.59)
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B
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16/18
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8.82 (4.67, 20.73)
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0.32 (0.16, 0.64)
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0.06 (9.6e-03, 0.42)
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C
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12/18
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17.56 (7.95, NA)
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0.61 (0.42, 0.88)
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0.44 (0.27, 0.74)
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D
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4/12
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NA (6.44, NA)
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0.67 (0.45, 0.99)
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0.67 (0.45, 0.99)
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E
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20/30
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14.26 (9.69, NA)
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0.63 (0.48, 0.83)
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0.34 (0.20, 0.57)
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|
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Log Rank Test
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ChiSq
|
11.3 on 4 df
|
|
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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
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0.04 (0.01, 0.08)
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0.06 (0.03, 0.12)
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|
1, m
|
3/21
|
0.10 (0.02, 0.27)
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0.16 (0.03, 0.36)
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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.
