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Description

Tidy Presentation of Clinical Reporting.

Streamlined statistical reporting in 'Rmarkdown' environments. Facilitates the automated reporting of descriptive statistics, multiple univariate models, multivariable models and tables combining these outputs. Plotting functions include customisable survival curves, forest plots from logistic and ordinal regression and bivariate comparison plots.

reportRmd

Lifecycle:Stable CRANstatus metacrandownloads

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 30.002 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 30.009 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
cohort0.004 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 30.0020.004
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.
Metadata

Version

0.1.0

License

Unknown

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