# reportRmd Package

# Overview

reportRmd is a package designed to facilitate the reporting of common statistical outputs easily in RMarkdown documents. The package supports pdf, html and word output without any changes to the body of the report. The main features are Table 1 style summaries, combining multiple univariate regression models into a single table, tidy multivariable model output and combining univariate and multivariable regressions into a single table. Single table summaries of median survival times and survival probabilities are also provided. A highly customisable survival curve function, based on ggplot2 can be used to create publication-quality plots. Visualisation plots are also available for bivariate relationships and logistic regression models.

A word of caution:

The reportRmd package is designed to facilitate statistical reporting and does not provide any checks regarding the suitability of the models fit.

# Summary statistics

Basic summary statistics

    data("pembrolizumab")
    rm_covsum(data=pembrolizumab,
    covs=c('age','sex'))

| n=94
--- | ---
Age at study entry |
Mean (sd) | 57.9 (12.8)
Median (Min,Max) | 59.1 (21.1, 81.8)
Patient Sex |
Female | 58 (62)
Male | 36 (38)

Set `IQR = T` for interquartile range instead of Min/Max

    rm_covsum(data=pembrolizumab,
    covs=c('age','sex'),IQR=TRUE)

| n=94
--- | ---
Age at study entry |
Mean (sd) | 57.9 (12.8)
Median (Q1,Q3) | 59.1 (49.5, 68.7)
Patient Sex |
Female | 58 (62)
Male | 36 (38)

Or `all.stats=T` for both

    rm_covsum(data=pembrolizumab,
    covs=c('age','sex'),all.stats = TRUE)

| n=94
--- | ---
Age at study entry |
Mean (sd) | 57.9 (12.8)
Median (Q1,Q3) | 59.1 (49.5, 68.7)
Range (min, max) | (21.1, 81.8)
Patient Sex |
Female | 58 (62)
Male | 36 (38)

## Summaries By Group

This will produce summary statistics by Sex

    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 |
Did ctDNA increase or decrease 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 |

## Showing Statistical Tests

To indicate which statistical test was used use `show.tests=TRUE`

    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 |  |

## Including Effect Sizes

Effect sizes can be added with `effSize = TRUE`. Effect size measures include the Wilcoxon r for Wilcoxon rank-sum test, Cohen’s d for t-test, Omega for ANOVA, Epsilon for Kruskal Wallis test, and Cramer’s V for categorical variables.

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

| Full Sample (n=94) | Female (n=58) | Male (n=36) | p-value | Effect Size
--- | --- | --- | --- | --- | ---
Age at study entry |  |  |  | 0.30 | 0.11
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) |  |
Did ctDNA increase or decrease from baseline to cycle 3 |  |  |  | 0.84 | 0.020
Decrease from baseline | 33 (45) | 19 (48) | 14 (42) |  |
Increase from baseline | 40 (55) | 21 (52) | 19 (58) |  |
Missing | 21 | 18 | 3 |  |

## Parametric vs Non-parametric Comparisons

Group comparisons are non-parametric by default, specify `testcont='ANOVA'` for t-tests/ANOVA

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

| Full Sample (n=94) | Female (n=58) | Male (n=36) | p-value | Effect Size | StatTest
--- | --- | --- | --- | --- | --- | ---
Age at study entry |  |  |  | 0.39 | 0.18 | t-test, Cohen’s d
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 |  |  |  | 0.63 | 0.100 | t-test, Cohen’s d
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 |  |  |

## Row vs Column Summaries

The default is to indicate percentages by columns (ie. percentages within columns add to 100)

    rm_covsum(data=pembrolizumab, maincov = 'sex',
    covs=c('cohort'),
    pvalue = FALSE)

| Full Sample (n=94) | Female (n=58) | Male (n=36)
--- | --- | --- | ---
Study Cohort |  |  |
A | 16 (17) | 3 (5) | 13 (36)
B | 18 (19) | 18 (31) | 0 (0)
C | 18 (19) | 18 (31) | 0 (0)
D | 12 (13) | 7 (12) | 5 (14)
E | 30 (32) | 12 (21) | 18 (50)

But you can also specify to show by row instead

    rm_covsum(data=pembrolizumab, maincov = 'sex',
    covs=c('cohort'),
    pvalue = FALSE,
    percentage='row')

| Full Sample (n=94) | Female (n=58) | Male (n=36)
--- | --- | --- | ---
Study Cohort |  |  |
A | 16 | 3 (19) | 13 (81)
B | 18 | 18 (100) | 0 (0)
C | 18 | 18 (100) | 0 (0)
D | 12 | 7 (58) | 5 (42)
E | 30 | 12 (40) | 18 (60)

# Compact summary statistics

Basic summary statistics

    rm_compactsum(data = pembrolizumab, xvars = c("change_ctdna_group", "l_size"))

| Full Sample (n=94) | Missing
--- | --- | ---
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (55%) | 21
Target lesion size at baseline | 73.5 (49.2-108.8) | 0

Set `iqr = T` for interquartile range instead of Min/Max

    rm_compactsum(data=pembrolizumab, xvars = c("change_ctdna_group", "l_size"), iqr=TRUE)

| Full Sample (n=94) | Missing
--- | --- | ---
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (55%) | 21
Target lesion size at baseline | 73.5 (49.2-108.8) | 0

Or `all.stats=T` for both

    rm_compactsum(data=pembrolizumab, xvars = c("change_ctdna_group", "l_size"), all.stats = T)

| Full Sample (n=94) | Missing
--- | --- | ---
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (55%) | 21
Target lesion size at baseline |  | 0
Mean (sd) | 87.9 (59.6) |
Median (Q1-Q3) | 73.5 (49.2-108.8) |
Range (min-max) | (11.0-387.0) |

## Summaries By Group

This will produce summary statistics by Sex:

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

| 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

## Showing Statistical Tests

To indicate which statistical test was used use `show.tests=TRUE`

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

| Full Sample (n=94) | Female (n=58) | Male (n=36) | p-value | Missing | pTest
--- | --- | --- | --- | --- | --- | ---
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 | Wilcoxon Rank Sum
PD L1 percent | 0.0 (0.0-10.0) | 0.5 (0.0-13.8) | 0.0 (0.0-4.5) | 0.76 | 1 | Wilcoxon Rank Sum
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (55%) | 21 (52%) | 19 (58%) | 0.84 | 21 | ChiSq

## Including Effect Sizes

Effect sizes can be added with `effSize = TRUE`. If `show.tests = TRUE` as well, the effStat will also be shown:

    rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', effSize = T, show.tests = T)

| Full Sample (n=94) | Female (n=58) | Male (n=36) | p-value | Effect Size (95% CI) | Missing | pTest | effStat
--- | --- | --- | --- | --- | --- | --- | --- | ---
Age at study entry | 59.1 (49.5-68.7) | 56.6 (45.8-67.8) | 61.2 (52.0-69.4) | 0.30 | 0.11 (-0.08, 0.21) | 0 | Wilcoxon Rank Sum | Wilcoxon r
PD L1 percent | 0.0 (0.0-10.0) | 0.5 (0.0-13.8) | 0.0 (0.0-4.5) | 0.76 | 0.03 (-0.17, 0.06) | 1 | Wilcoxon Rank Sum | Wilcoxon r
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (55%) | 21 (52%) | 19 (58%) | 0.84 | 0.02 (-0.18, 0.05) | 21 | ChiSq | Cramer’s V

## Using Mean Summary Statistic Instead of Median

The default summary statistic for numerical variables is median. To specify which numerical variables should have mean displayed instead, change the `use_mean` argument. Unspecified xvars will use the default median

    rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', use_mean = c("pdl1"), effSize = T, show.tests = T)

| Full Sample (n=94) | Female (n=58) | Male (n=36) | p-value | Effect Size (95% CI) | Missing | pTest | effStat
--- | --- | --- | --- | --- | --- | --- | --- | ---
Age at study entry | 59.1 (49.5-68.7) | 56.6 (45.8-67.8) | 61.2 (52.0-69.4) | 0.30 | 0.11 (-0.09, 0.21) | 0 | Wilcoxon Rank Sum | Wilcoxon r
PD L1 percent | 13.9 (29.2) | 15.0 (30.5) | 12.1 (27.3) | 0.63 | 0.10 (-0.37, 0.20) | 1 | t-test | Cohen’s d
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (55%) | 21 (52%) | 19 (58%) | 0.84 | 0.02 (-0.19, 0.05) | 21 | ChiSq | Cramer’s V

## Custom Digits Argument

The digits and digits.cat arguments can be changed to a custom numerical value. The default digits is 1, the default digits.cat is 0

    rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', use_mean = c("pdl1"), digits = 2, digits.cat = 1, effSize = T, show.tests = T)

| Full Sample (n=94) | Female (n=58) | Male (n=36) | p-value | Effect Size (95% CI) | Missing | pTest | effStat
--- | --- | --- | --- | --- | --- | --- | --- | ---
Age at study entry | 59.12 (49.54-68.71) | 56.64 (45.80-67.83) | 61.16 (51.99-69.44) | 0.30 | 0.11 (-0.10, 0.21) | 0 | Wilcoxon Rank Sum | Wilcoxon r
PD L1 percent | 13.90 (29.24) | 15.02 (30.55) | 12.06 (27.26) | 0.63 | 0.10 (-0.38, 0.20) | 1 | t-test | Cohen’s d
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (54.8%) | 21 (52.5%) | 19 (57.6%) | 0.84 | 0.02 (-0.19, 0.05) | 21 | ChiSq | Cramer’s V

To specify custom digit arguments for different numerical variables, change the `digits` argument. Unspecified variables will use the default value.

    rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','l_size'), grp = 'sex', digits = c("age" = 3, "l_size" = 2), effSize = T, show.tests = T)

| Full Sample (n=94) | Female (n=58) | Male (n=36) | p-value | Effect Size (95% CI) | Missing | pTest | effStat
--- | --- | --- | --- | --- | --- | --- | --- | ---
Age at study entry | 59.121 (49.542-68.708) | 56.643 (45.800-67.834) | 61.164 (51.992-69.438) | 0.30 | 0.11 (-0.09, 0.21) | 0 | Wilcoxon Rank Sum | Wilcoxon r
PD L1 percent | 0.0 (0.0-10.0) | 0.5 (0.0-13.8) | 0.0 (0.0-4.5) | 0.76 | 0.03 (-0.16, 0.05) | 1 | Wilcoxon Rank Sum | Wilcoxon r
Target lesion size at baseline | 73.50 (49.25-108.75) | 68.00 (44.25-97.75) | 93.00 (65.50-121.00) | 0.066 | 0.19 (-0.01, 0.36) | 0 | Wilcoxon Rank Sum | Wilcoxon r

## Row vs Column Summaries

The default is to indicate percentages by columns (ie. percentages within columns add to 100). But you can also specify to show by row instead

    rm_compactsum(data=pembrolizumab, xvars=c('change_ctdna_group','orr'), grp = 'cohort', effSize = T, show.tests = T, percentage = "row")

| Full Sample (n=94) | A (n=16) | B (n=18) | C (n=18) | D (n=12) | E (n=30) | p-value | Effect Size (95% CI) | pTest | effStat | Missing
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 | 8 (20%) | 7 (18%) | 5 (12%) | 2 (5%) | 18 (45%) | 0.18 | 0.30 (0.21, 0.51) | Fisher Exact | Cramer’s V | 21
Objective Response - SD/PD | 78 | 13 (17%) | 18 (23%) | 18 (23%) | 4 (5%) | 25 (32%) | <0.001 | 0.55 (0.76, 1.02) | Fisher Exact | Cramer’s V | 0

## Viewing the Self-Generated Description

To view the self-generated description for the summary table:

    summary_tab <- rm_compactsum(data=pembrolizumab, xvars=c('change_ctdna_group','orr', 'age'), grp = 'cohort', effSize = T, show.tests = T)
    cat(attr(summary_tab, "description"))

## Using Tidyselect

Both `rm_covsum` and `rm_compactsum` support tidyselect syntax, allowing you to use bare column names instead of character strings. This also enables piping with `|>`:

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

| 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

The same tidyselect syntax works with `rm_covsum`:

    pembrolizumab |> rm_covsum(covs = c(age, pdl1, change_ctdna_group), maincov = sex)

| 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 |
Did ctDNA increase or decrease 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 |

The `xvars`/`grp` aliases can also be used with `rm_covsum` for a consistent interface:

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

| 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 |
Did ctDNA increase or decrease 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 |

## Adding Header Rows

Header rows can be added to any table using the `header_above` argument in `outTable()`. This works for HTML, PDF and Word output. The argument takes a named vector where the names are the header labels and the values are the number of columns each label spans.

    tab <- rm_covsum(data = pembrolizumab, maincov = cohort,
                      covs = c(age, pdl1, sex), full = FALSE,
                      pvalue = FALSE, tableOnly = TRUE)

    outTable(tab, header_above = c(" " = 1, "Cohorts A-C" = 3, "Cohorts D,E" = 2))

| Cohorts A-C | Cohorts D,E
--- | --- | ---
Covariate | A (n=16) | B (n=18) | C (n=18) | D (n=12) | E (n=30)
Age at study entry |  |  |  |  |
Mean (sd) | 62.9 (6.1) | 56.3 (14.0) | 57.9 (10.8) | 63.8 (10.0) | 53.7 (15.3)
Median (Min,Max) | 62.1 (51.0, 70.9) | 56.6 (35.5, 74.8) | 53.9 (36.5, 74.0) | 64.6 (45.0, 78.2) | 51.8 (21.1, 81.8)
PD L1 percent |  |  |  |  |
Mean (sd) | 32.8 (42.1) | 7.5 (21.3) | 7.9 (10.2) | 25.8 (40.2) | 7.2 (24.0)
Median (Min,Max) | 2 (0, 100) | 0 (0, 90) | 2.5 (0.0, 30.0) | 1 (0, 95) | 0 (0, 95)
Missing | 1 | 0 | 0 | 0 | 0
Patient Sex |  |  |  |  |
Female | 3 (19) | 18 (100) | 18 (100) | 7 (58) | 12 (40)
Male | 13 (81) | 0 (0) | 0 (0) | 5 (42) | 18 (60)

# Univariate regression

Combining multiple univariate regression analyses into a single table. The function will try to determine the most appropriate model from the data.

    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
Did ctDNA increase or decrease 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

## Simple Linear Regression

If the response is continuous linear regression is the default. Using `type = 'linear'` will ensure linear regression.

    rm_uvsum(data=pembrolizumab, response='l_size',
    covs=c('age','cohort'))

| Estimate(95%CI) | p-value | N
--- | --- | --- | ---
Age at study entry | -0.58 (-1.54, 0.38) | 0.23 | 94
Study Cohort |  |  | 94
A | Reference |  | 16
B | -38.04 (-74.95, -1.13) | 0.044 | 18
C | 20.35 (-16.56, 57.26) | 0.28 | 18
D | -24.79 (-65.82, 16.23) | 0.23 | 12
E | 31.69 (-1.56, 64.95) | 0.062 | 30

## Logistic Regression

If the response is binomial, logistic regression will be run (or specified with `type = 'logistic'`).

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

| OR(95%CI) | p-value | N | Event
--- | --- | --- | --- | ---
Age at study entry | 0.96 (0.91, 1.00) | 0.089 | 94 | 78
Study Cohort |  |  | 94 | 78
A | Reference |  | 16 | 13
B | 7.3e+07 (8.5e-76, NA) | 0.99 | 18 | 18
C | 7.3e+07 (7.7e-74, NA) | 0.99 | 18 | 18
D | 0.12 (0.02, 0.60) | 0.015 | 12 | 4
E | 1.15 (0.21, 5.49) | 0.86 | 30 | 25

## Poisson Regression

If the response is integer, poisson regression will be run (or specified with `type = 'poisson'`).

    pembrolizumab$Counts <- rpois(nrow(pembrolizumab),lambda = 3)
    rm_uvsum(data=pembrolizumab, response='Counts',covs=c('age','cohort'))

| RR(95%CI) | p-value | N
--- | --- | --- | ---
Age at study entry | 1.01 (1.00, 1.02) | 0.18 | 94
Study Cohort |  |  | 94
A | Reference |  | 16
B | 0.94 (0.65, 1.36) | 0.74 | 18
C | 0.92 (0.63, 1.34) | 0.67 | 18
D | 0.84 (0.54, 1.28) | 0.42 | 12
E | 0.84 (0.60, 1.19) | 0.31 | 30

offset terms can be specified as well, but must correspond to a variable in the data set

    pembrolizumab$length_followup <- rnorm(nrow(pembrolizumab),mean = 72,sd=3)
    pembrolizumab$log_length_followup <- log(pembrolizumab$length_followup)
    rm_uvsum(data=pembrolizumab, response='Counts',covs=c('age','cohort'),
             offset = "log_length_followup")

| RR(95%CI) | p-value | N
--- | --- | --- | ---
Age at study entry | 1.01 (1.00, 1.02) | 0.18 | 94
Study Cohort |  |  | 94
A | Reference |  | 16
B | 0.93 (0.64, 1.35) | 0.70 | 18
C | 0.93 (0.64, 1.35) | 0.70 | 18
D | 0.84 (0.54, 1.28) | 0.42 | 12
E | 0.85 (0.61, 1.20) | 0.35 | 30

## Negative Binomial Regression

To run negative binomial regression instead specify `type = 'negbin'`

    rm_uvsum(data=pembrolizumab, response='Counts', type='negbin',
             covs=c('age','cohort'),
             offset = "log_length_followup")

| RR(95%CI) | p-value | N
--- | --- | --- | ---
Age at study entry | 1.01 (1.00, 1.02) | 0.18 | 94
Study Cohort |  |  | 94
A | Reference |  | 16
B | 0.93 (0.64, 1.35) | 0.70 | 18
C | 0.93 (0.64, 1.35) | 0.70 | 18
D | 0.84 (0.54, 1.28) | 0.42 | 12
E | 0.85 (0.61, 1.20) | 0.35 | 30

## Survival Analysis

If two response variables are specified and then survival analysis is run (specified with `type='coxph'`).

    rm_uvsum(data=pembrolizumab, response=c('os_time','os_status'),
    covs=c('age','pdl1','change_ctdna_group'),whichp = "levels")

| HR(95%CI) | p-value | N | Event
--- | --- | --- | --- | ---
Age at study entry | 0.99 (0.97, 1.01) | 0.16 | 94 | 64
PD L1 percent | 0.99 (0.98, 1.00) | 0.026 | 93 | 63
Did ctDNA increase or decrease from baseline to cycle 3 |  |  | 73 | 46
Decrease from baseline | Reference |  | 33 | 14
Increase from baseline | 3.06 (1.62, 5.77) | <0.001 | 40 | 32

## Competing Risk

Competing risk models need to be explicitly specified using `type='crr'`.

    rm_uvsum(data=pembrolizumab, response=c('os_time','os_status'),
    covs=c('age','pdl1','change_ctdna_group'),
    type='crr')

| HR(95%CI) | p-value | N | Event
--- | --- | --- | --- | ---
Age at study entry | 0.99 (0.97, 1.00) | 0.15 | 94 | 64
PD L1 percent | 0.99 (0.98, 1.00) | 0.017 | 93 | 63
Did ctDNA increase or decrease from baseline to cycle 3 |  |  | 73 | 46
Decrease from baseline | Reference |  | 33 | 14
Increase from baseline | 3.06 (1.64, 5.69) | <0.001 | 40 | 32

## GEE Models

Correlated observations can be handled using GEE

    data("ctDNA")
     rm_uvsum(response = 'size_change',
     covs=c('time','ctdna_status'),
     gee=TRUE,
     id='id', corstr="exchangeable",
     family=gaussian("identity"),
     data=ctDNA,showN=TRUE)

| Estimate(95%CI) | p-value | N (obs|clusters)
--- | --- | --- | ---
Number of weeks on treatment | -0.12 (-0.44, 0.19) | 0.44 | 262|72
Change in ctDNA since baseline |  |  | 262|72
Clearance | Reference |  | 134|14
No clearance, decrease from baseline | 61.29 (37.49, 85.09) | <0.001 | 42|17
No clearance, increase from baseline | 82.52 (64.75, 100.28) | <0.001 | 86|41

## MICE output

Outputs from multiple imputations can be summarised as follows:

    miss_data <- pembrolizumab
    miss_data$age[sample(1:nrow(pembrolizumab),50,replace=F)] <- NA
    miss_data$cohort[sample(1:nrow(pembrolizumab),50,replace=F)] <- NA
    require(mice)
    ini <- mice::mice(miss_data, maxit = 0)
    pred_matrix <- ini$predictorMatrix
    pred_matrix[1, ] <- 0
    pred_matrix[, 1] <- 0
    imp <- mice::mice(miss_data, m = 5, predictorMatrix = pred_matrix, printFlag = FALSE)
    imputed_datasets <- purrr::map(1:5, \(m) mice::complete(imp, m))

    fits <- lapply(imputed_datasets, function(dat) {
      glm(orr ~ age+cohort, data = dat,family='binomial')
    })
    model <- mice::as.mira(fits)
    rm_mvsum(model)

## Returning Model Objects

If you want to check the underlying models, set `returnModels = TRUE`

     rm_uvsum(response = 'orr',
     covs=c('age'),
     data=pembrolizumab,returnModels = TRUE)

    ## $age
    ##
    ## Call:  glm(formula = orr ~ age, family = binomial, data = data)
    ##
    ## Coefficients:
    ## (Intercept)          age
    ##     4.12269     -0.04231
    ##
    ## Degrees of Freedom: 93 Total (i.e. Null);  92 Residual
    ## Null Deviance:       85.77
    ## Residual Deviance: 82.53     AIC: 86.53

The data analysed can be examined by interrogating the data object appended to each model

    mList <-  rm_uvsum(response = 'orr',
     covs=c('age'),
     data=pembrolizumab,returnModels = TRUE)
    head(mList$age$data)

    ## # A tibble: 6 × 18
    ##   id       age sex   cohort l_size  pdl1   tmb baseline_ctdna change_ctdna_group
    ##   <fct>  <dbl> <fct> <fct>   <dbl> <dbl> <dbl>          <dbl> <fct>
    ## 1 INS-A…  62.1 Male  A         140     2 0.574         114.   Increase from bas…
    ## 2 INS-A…  62.2 Male  A         108     0 0.921           4.26 Increase from bas…
    ## 3 INS-A…  70.9 Male  A          11   100 0.375         205.   Decrease from bas…
    ## 4 INS-A…  57.9 Male  A          39    45 0.824         169.   Increase from bas…
    ## 5 INS-A…  65.7 Fema… A          90     0 0.114         425.   Decrease from bas…
    ## 6 INS-A…  60.9 Male  A         124     2 0.717          15.8  Increase from bas…
    ## # ℹ 9 more variables: orr <fct>, cbr <fct>, os_status <dbl>, os_time <dbl>,
    ## #   pfs_status <dbl>, pfs_time <dbl>, Counts <int>, length_followup <dbl>,
    ## #   log_length_followup <dbl>

## Adjusting p-values

Multiple comparisons can be controlled for with the p.adjust argument, which accepts any of the options from the `p.adjust` function.

     rm_uvsum(response = 'orr',
     covs=c('age','sex','pdl1'),
     data=pembrolizumab,p.adjust = 'fdr')

| OR(95%CI) | p-value | N | Event
--- | --- | --- | --- | ---
Age at study entry | 0.96 (0.91, 1.00) | 0.11 | 94 | 78
Patient Sex |  |  | 94 | 78
Female | Reference |  | 58 | 51
Male | 0.41 (0.13, 1.22) | 0.11 | 36 | 27
PD L1 percent | 0.97 (0.95, 0.98) | <0.001 | 93 | 77

Note: The raw p-value column is suppressed when there are categorical variables with >2 levels, to prevent three columns of p-values from appearing.

# Multivariable analysis

To create a nice display for multivariable models the multivariable model first needs to be fit.

By default, the variance inflation factor will be shown to check for multicollinearity. To suppress this column set `vif=FALSE`. Note: variance inflation factors are not computed (yet) for multilevel or GEE models.

    glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
                   family='binomial',
                   data = pembrolizumab)
    rm_mvsum(glm_fit, showN = TRUE, vif=TRUE)

| OR(95%CI) | p-value | N | Event | VIF
--- | --- | --- | --- | --- | ---
Did ctDNA increase or decrease 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

p-values can be adjusted for multiple comparisons using any of the options available in the `p.adjust` function. This argument is also available for univariate models run with rm_uvsum.

    rm_mvsum(glm_fit, showN = TRUE, vif=TRUE,p.adjust = 'holm')

| OR(95%CI) | p-value | N | Event | VIF
--- | --- | --- | --- | --- | ---
Did ctDNA increase or decrease from baseline to cycle 3 |  |  | 73 | 58 | 1.03
Decrease from baseline | Reference |  | 33 | 19 |
Increase from baseline | 23.92 (3.69, 508.17) | 0.018 | 40 | 39 |
PD L1 percent | 0.97 (0.95, 0.99) | 0.022 | 73 | 58 | 1.24
Age at study entry | 0.94 (0.87, 1.00) | 0.078 | 73 | 58 | 1.23

## Including Unadjusted Estimates

As of version 0.1.3, you can include univariate (unadjusted) estimates alongside the multivariable estimates in a single table by setting `include_unadjusted = TRUE`. This provides a convenient way to compare unadjusted and adjusted estimates side-by-side.

    glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
                   family='binomial',
                   data = pembrolizumab)
    rm_mvsum(glm_fit, data = pembrolizumab, include_unadjusted = TRUE, showN = TRUE, vif = TRUE)

| Unadjusted OR(95%CI) | Unadjusted p-value | Adjusted OR(95%CI) | Adjusted p-value | N | Event | VIF
--- | --- | --- | --- | --- | --- | --- | ---
Did ctDNA increase or decrease from baseline to cycle 3 |  |  |  |  | 73 | 58 | 1.03
Decrease from baseline | Reference |  | Reference |  | 33 | 19 |
Increase from baseline | 28.74 (5.20, 540.18) | 0.002 | 23.92 (3.69, 508.17) | 0.006 | 40 | 39 |
PD L1 percent | 0.97 (0.95, 0.98) | <0.001 | 0.97 (0.95, 0.99) | 0.011 | 73 | 58 | 1.24
Age at study entry | 0.96 (0.91, 1.00) | 0.089 | 0.94 (0.87, 1.00) | 0.078 | 73 | 58 | 1.23

This feature works with all supported model types including Cox proportional hazards models:

    require(survival)
    cox_fit <- coxph(Surv(os_time, os_status) ~ age + sex + baseline_ctdna,
                     data = pembrolizumab)
    rm_mvsum(cox_fit, data = pembrolizumab, include_unadjusted = TRUE, tableOnly = TRUE)

    ##            Covariate Unadjusted HR(95%CI) Unadjusted p-value Adjusted HR(95%CI)
    ## 1 Age at study entry    0.99 (0.97, 1.01)               0.16  0.98 (0.96, 1.00)
    ## 2        Patient Sex                 <NA>               <NA>               <NA>
    ## 3             Female            Reference               <NA>          Reference
    ## 4               Male    1.21 (0.73, 2.00)               0.46  1.19 (0.70, 2.02)
    ## 5     Baseline ctDNA    1.00 (1.00, 1.00)              0.019  1.00 (1.00, 1.00)
    ##   Adjusted p-value  N Event  VIF
    ## 1            0.088 94    64 1.06
    ## 2             <NA> 94    64 1.11
    ## 3             <NA> 58    39   NA
    ## 4             0.52 36    25   NA
    ## 5            0.021 94    64 1.09

# Combining univariate and multivariable models

To display both models in a single table run the rm_uvsum and rm_mvsum functions with `tableOnly=TRUE` and combine.

    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, showN = TRUE,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 |  |
Patient 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
Did ctDNA increase or decrease 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

Note: This can also be done with adjusted p-values, but when combined the raw p-values are dropped.

    uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr',
    covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE,p.adjust='holm')

    glm_fit <- glm(orr~change_ctdna_group+pdl1,
                   family='binomial',
                   data = pembrolizumab)
    mvsumTable <- rm_mvsum(glm_fit,tableOnly = TRUE,p.adjust='holm')

    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) | 0.18 |  |
Patient Sex |  |  |  |
Female | Reference |  |  |
Male | 0.41 (0.13, 1.22) | 0.18 |  |
PD L1 percent | 0.97 (0.95, 0.98) | <0.001 | 0.98 (0.95, 1.00) | 0.024
Did ctDNA increase or decrease from baseline to cycle 3 |  |  |  |
Decrease from baseline | Reference |  | Reference |
Increase from baseline | 28.74 (5.20, 540.18) | 0.005 | 24.71 (4.19, 479.13) | 0.007

# Changing the output

If you need to make changes to the tables, setting `tableOnly=TRUE` will return a data frame for any of the `rm_` functions. Changes can be made, and the table output using `outTable()`

    mvsumTable <- rm_mvsum(glm_fit, showN = TRUE,tableOnly = TRUE)
    names(mvsumTable)[1] <-'Predictor'
    outTable(mvsumTable)

Predictor | OR(95%CI) | p-value | N | Event | VIF
--- | --- | --- | --- | --- | ---
Did ctDNA increase or decrease from baseline to cycle 3 |  |  | 73 | 58 | 1.01
Decrease from baseline | Reference |  | 33 | 19 |
Increase from baseline | 24.71 (4.19, 479.13) | 0.004 | 40 | 39 |
PD L1 percent | 0.98 (0.95, 1.00) | 0.024 | 73 | 58 | 1.01

# Combining tables

#Tables can be nested with the `nestTable()` function

    cohortA <- rm_uvsum(data=subset(pembrolizumab,cohort=='A'),
                         response = 'pdl1',
                         covs=c('age','sex'),
                         tableOnly = T)
    cohortA$Cohort <- 'Cohort A'
    cohortE <- rm_uvsum(data=subset(pembrolizumab,cohort=='E'),
                         response = 'pdl1',
                         covs=c('age','sex'),
                         tableOnly = T)
    cohortE$Cohort <- 'Cohort E'
    nestTable(rbind(cohortA,cohortE),head_col = 'Cohort',to_col = 'Covariate')

| Estimate(95%CI) | p-value | N
--- | --- | --- | ---
Cohort A |  |  |
Age at study entry | 2.94 (-0.70, 6.58) | 0.10 | 15
Patient Sex |  |  | 15
Female | Reference |  | 3
Male | -40.25 (-96.25, 15.75) | 0.14 | 12
Cohort E |  |  |
Age at study entry | -0.44 (-1.02, 0.15) | 0.14 | 30
Patient Sex |  |  | 30
Female | Reference |  | 12
Male | -14.86 (-32.57, 2.85) | 0.097 | 18

## Scrollable Tables

If you are rendering to html format you can use the `scrollTable` function to create a scrollable table. This is useful for tables with many rows. If the output format is not html then a regular table will be displayed.

    long_table <- rm_compactsum(data=pembrolizumab,xvars = c(age,sex,cohort,pdl1,tmb,baseline_ctdna,change_ctdna_group,orr,cbr))
    scrolling_table(long_table,pixelHeight = 300)

| Full Sample (n=94) | Missing
--- | --- | ---
Age at study entry | 59.1 (49.5-68.7) | 0
Patient Sex - Male | 36 (38%) | 0
Study Cohort |  | 0
A | 16 (17%) |
B | 18 (19%) |
C | 18 (19%) |
D | 12 (13%) |
E | 30 (32%) |
PD L1 percent | 0.0 (0.0-10.0) | 1
log of TMB | 0.7 (0.4-1.3) | 0
Baseline ctDNA | 86.0 (7.5-416.6) | 0
Did ctDNA increase or decrease from baseline to cycle 3 - Increase from baseline | 40 (55%) | 21
Objective Response - SD/PD | 78 (83%) | 0
Clinical Beneficial Response - PD/SD<C6 | 70 (74%) | 0

# Simple Survival Summaries

Displaying survival probabilities at different times by sex using Kaplan Meier estimates

    rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
     group="sex",survtimes=seq(12,36,12),survtimeunit='months')

Group | Events/Total | Median (95%CI) | 12months (95% CI) | 24months (95% CI) | 36months (95% CI)
--- | --- | --- | --- | --- | ---
Female | 39/58 | 14.29 (9.69, 23.82) | 0.55 (0.44, 0.69) | 0.34 (0.24, 0.50) | 0.29 (0.18, 0.45)
Male | 25/36 | 11.24 (6.14, 25.33) | 0.50 (0.36, 0.69) | 0.31 (0.18, 0.52) | 0.27 (0.15, 0.48)
|  |  | Log Rank Test | ChiSq | 0.5 on 1 df
|  |  |  | p-value | 0.46

# Survival Times in Long Format

Displaying survival probabilities at different times by sex using Cox PH estimates

    rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
     strata="sex",survtimes=c(12,24),survtimeunit='mo',type='PH')

Time (mo) | At Risk | Events | Censored | Survival Rate (95% CI)
--- | --- | --- | --- | ---
Overall | 94 |  |  |
12 | 48 | 44 | 2 | 0.53 (0.44, 0.64)
24 | 24 | 17 | 7 | 0.33 (0.25, 0.45)
Female | 58 |  |  |
12 | 31 | 26 | 1 | 0.55 (0.44, 0.70)
24 | 16 | 11 | 4 | 0.35 (0.24, 0.50)
Male | 36 |  |  |
12 | 17 | 18 | 1 | 0.51 (0.37, 0.70)
24 | 8 | 6 | 3 | 0.32 (0.19, 0.52)

## Survival Times With Covariate Adjustments

Displaying survival probabilities at different times by sex, adjusting for age using Cox PH estimates

    rm_survtime(data=pembrolizumab,time='os_time',status='os_status', covs='age',
     strata="sex",survtimes=c(12,24),survtimeunit='mo',type='PH')

Time (mo) | At Risk | Events | Censored | Survival Rate (95% CI)
--- | --- | --- | --- | ---
Overall | 94 |  |  |
12 | 48 | 44 | 2 | 0.54 (0.44, 0.65)
24 | 24 | 17 | 7 | 0.33 (0.25, 0.45)
Female | 58 |  |  |
12 | 31 | 26 | 1 | 0.56 (0.44, 0.70)
24 | 16 | 11 | 4 | 0.35 (0.24, 0.50)
Male | 36 |  |  |
12 | 17 | 18 | 1 | 0.51 (0.37, 0.70)
24 | 8 | 6 | 3 | 0.31 (0.19, 0.53)

# Stratified Survival Summary

To combine estimates across strata

    rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
                covs='sex',strata='cohort',digits=1)

group | N | Observed | Expected | Median (95%CI)
--- | --- | --- | --- | ---
Overall | 94 | 64 |  | 14.0 (9.0, 20.1)
Female | 58 | 39 | 43.0 | 14.3 (9.7, 23.8)
Male | 36 | 25 | 21.0 | 11.2 (6.1, 25.3)
Log Rank Test |  |  |  | ChiSq = 1.9 on 1 df
stratified by cohort |  |  |  | p-value = 0.17

# Cumulative Incidence Function Summary

For competing risk analysis, use `rm_cifsum()` to display cumulative incidence probabilities at specific time points. This requires the cmprsk package and a status variable coded with different event types.

    # Using pbc data from survival package as example
    data(pbc, package = "survival")
    # Create competing risk status: 0=censored, 1=transplant, 2=death
    pbc$status_cr <- ifelse(pbc$status == 0, 0, pbc$status)

    rm_cifsum(data=pbc, time='time', status='status_cr',
              group='trt', eventcode=2, cencode=0,
              eventtimes=c(1000, 2000, 3000),
              eventtimeunit='days')

Strata | Event/Total | 1000days (95% CI) | 2000days (95% CI) | 3000days (95% CI)
--- | --- | --- | --- | ---
1 | 65/158 | 0.15 (0.10, 0.21) | 0.30 (0.23, 0.38) | 0.44 (0.35, 0.53)
2 | 60/154 | 0.20 (0.14, 0.27) | 0.29 (0.22, 0.37) | 0.38 (0.29, 0.47)
|  | Gray’s Test | ChiSq | 0.1 on 1 df
|  |  | p-value | 0.80

# Working with Labels

New in version 0.1.0 is the ability to incorporate variable labels into summary tables (but not yet all plots). If variables contain a `label` attribute this will be displayed automatically, to disable this set `nicenames=F`

Variable labels will be shown in the `nicenames` argument is set to `TRUE` (the default). Variable labels are set using the `label` attribute of individual variables (assigned using `reportRmd` or another package like `haven`).

`reportRmd` supports the addition, removal and export of labels using the following functions:

- `set_labels` will set labels for a data frame from a lookup table
- `set_var_labels` allows you to set individual variable labels to a data frame
- `clear_labels` removes all labels from a data frame
- `export_labels` extracts variable labels from a data frame and returns a data frame of variable names and variable labels

## Worked Example

Get some descriptive stats for the ctDNA data that comes with the package. The `nicenames` argument is TRUE by default so underscores are replaced by spaces

    rm_covsum(data=ctDNA,
              covs=c('cohort','ctdna_status','size_change'))

| n=270
--- | ---
Study Cohort |
A | 50 (19)
B | 14 (5)
C | 18 (7)
D | 88 (33)
E | 100 (37)
Change in ctDNA since baseline |
Clearance | 137 (51)
No clearance, decrease from baseline | 44 (16)
No clearance, increase from baseline | 89 (33)
Percentage change in tumour measurement |
Mean (sd) | -29.7 (52.8)
Median (Min,Max) | -32.5 (-100.0, 197.1)
Missing | 8

### set_labels

If we have a lookup table of variable names and labels that we imported from a data dictionary we can set the variable labels for the data frame and these will be used in the `rm_` functions

    ctDNA_names <- data.frame(var=names(ctDNA),
                              label=c('Patient ID',
                                      'Study Cohort',
                                      'Change in ctDNA since baseline',
                                      'Number of weeks on treatment',
                                      'Percentage change in tumour measurement'))
    ctDNA <- set_labels(ctDNA,ctDNA_names)

    rm_covsum(data=ctDNA,
              covs=c('cohort','ctdna_status','size_change'))

| n=270
--- | ---
Study Cohort |
A | 50 (19)
B | 14 (5)
C | 18 (7)
D | 88 (33)
E | 100 (37)
Change in ctDNA since baseline |
Clearance | 137 (51)
No clearance, decrease from baseline | 44 (16)
No clearance, increase from baseline | 89 (33)
Percentage change in tumour measurement |
Mean (sd) | -29.7 (52.8)
Median (Min,Max) | -32.5 (-100.0, 197.1)
Missing | 8

### set_var_labels

Individual labels can be changed with with the `set_var_labels` command

    ctDNA <- set_var_labels(ctDNA,
                            cohort="A new cohort label")
    rm_covsum(data=ctDNA,
              covs=c('cohort','ctdna_status','size_change'))

| n=270
--- | ---
A new cohort label |
A | 50 (19)
B | 14 (5)
C | 18 (7)
D | 88 (33)
E | 100 (37)
Change in ctDNA since baseline |
Clearance | 137 (51)
No clearance, decrease from baseline | 44 (16)
No clearance, increase from baseline | 89 (33)
Percentage change in tumour measurement |
Mean (sd) | -29.7 (52.8)
Median (Min,Max) | -32.5 (-100.0, 197.1)
Missing | 8

### extract_labels

Extract the variable labels to a data frame

    var_labels <- extract_labels(ctDNA)
    var_labels

    ##       variable                                   label
    ## 1           id                              Patient ID
    ## 2       cohort                      A new cohort label
    ## 3 ctdna_status          Change in ctDNA since baseline
    ## 4         time            Number of weeks on treatment
    ## 5  size_change Percentage change in tumour measurement

### replace_plot_labels

This function will accept a ggplot plot and replace the variable names (x-axis, y-axis and legend) with the variable labels. This is useful for more professional looking plots.

    library(ggplot2)
    p <- ggplot(data=ctDNA,aes(x=ctdna_status,y=size_change,colour=cohort))+
      geom_point()
    replace_plot_labels(p)

### clear_labels

Or clear them all

    ctDNA <- clear_labels(ctDNA)

# Plotting Functions

{width=100%}

## Plotting bivariate relationships

These plots are designed for quick inspection of many variables, not for publication. This is the plotting version of rm_uvsum. As of 0.1.1 the variable names will be replaced by variable labels if they exist.

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

The plotuv function can also be used without a response variable to display summary of variables

    plotuv(data = pembrolizumab, covs=c('age','cohort','pdl1','change_ctdna_group'), showN = T)

## Plotting survival curves

Survival curves are now ggplot2-based, the older version, ggkmcif is deprecated from version 0.1.0

    ggkmcif2(response = c('os_time','os_status'),
    cov='cohort',
    data=pembrolizumab)

## Forest Plots

Similar to `rm_uvsum` and `rm_mvsum`, forest plots can be created from univariate or multivariable models. forestplot2 is deprecated from version 0.1.0. Variable labels are not yet incorporated into the forest plots.

This will default to a log scale, but can be set to linear using `logScale=FALSE`

    forestplotUV(response="orr", covs=c("change_ctdna_group", "sex", "age", "l_size"),
    data=pembrolizumab, family='binomial')

### Multivariable Model Forest Plot

    glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
                   family='binomial',
                   data = pembrolizumab)
    forestplotMV(glm_fit)

### Multivariable Forest Plot with Unadjusted Estimates

As of version 0.1.3, you can include both unadjusted and adjusted estimates in a multivariable forest plot by setting `include_unadjusted = TRUE`:

    glm_fit <- glm(orr~change_ctdna_group+pdl1+age,
                   family='binomial',
                   data = pembrolizumab)
    forestplotMV(glm_fit, data = pembrolizumab, include_unadjusted = TRUE)

This provides a convenient alternative to the `forestplotUVMV` function for comparing unadjusted and adjusted estimates.

## Combining univariable and multivariable models into a single plot

    UVp = forestplotUV(response="orr", covs=c("change_ctdna_group", "sex", "age",
     "l_size"), data=pembrolizumab, family='binomial')
     MVp = forestplotMV(glm(orr~change_ctdna_group+sex+age+l_size,
     data=pembrolizumab,family = 'binomial'))
     forestplotUVMV(UVp, MVp)

This can also be done with linear scale odds ratios. Number of subjects and/or number of events can also be turned off, as well as different colours used.

    uvFP <- forestplotUV(data=pembrolizumab, response='orr',
    covs=c('age','sex','pdl1','change_ctdna_group'))

    glm_fit <- glm(orr~change_ctdna_group+pdl1,
                   family='binomial',
                   data = pembrolizumab)
    mvFP <- forestplotMV(glm_fit)

    forestplotUVMV(uvFP,mvFP,showN=F,showEvent=F,colours=c("orange","black","blue"),logScale=F)

# Miscellaneous Functions

## excelCol

To identify the number of a column given the Excel column header

    excelCol(G,AB,Az)

    ##  G AB AZ
    ##  7 28 52

## excelColLetters

To identify the Excel header given a columns number

    excelColLetters(c(7,28,52))

    ##    7   28   52
    ##  "G" "AB" "AZ"

## extract_package_details

This function will search the current script or markdown file for R packages and record the package name, functions called, the package version and the citation(s) for the package. By default, function calls and packages that only appear in commented code will be ignored, but they can be included by setting `ignore_comments` = FALSE

    extract_package_details() |>
      outTable()

    extract_package_details(ignore_comments = FALSE) |>
      outTable()

Package name | Functions called | Package version | Package citation
--- | --- | --- | ---
reportRmd | rm_covsum, rm_compactsum, rm_uvsum, glm, rm_mvsum, Surv, rm_uv_mv, outTable, nestTable, scrolling_table, rm_survsum, rm_survtime, rm_survdiff, rm_cifsum, set_labels, set_var_labels, extract_labels, ggplot, aes, geom_point, replace_plot_labels, clear_labels, plotuv, ggsave, ggkmcif2, forestplotUV, forestplotMV, forestplotUVMV, excelCol, excelColLetters, citation | 0.1.3 | Avery L, Del Bel R, Espin-Garcia O, Lajkosz K, Ong C, Pittman T, Santiago A, Weiss J, Xu W (2026). reportRmd: Tidy Presentation of Clinical Reporting. R package version 0.1.3, https://biostatspmh.github.io/reportRmd/.
rmarkdown | run | 2.29 | Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2024). rmarkdown: Dynamic Documents for R. R package version 2.29, https://github.com/rstudio/rmarkdown. Xie Y, Allaire J, Grolemund G (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC. Xie Y, Dervieux C, Riederer E (2020). R Markdown Cookbook. Chapman and Hall/CRC.
survival | coxph | 3.8.3 | Therneau T (2024). A Package for Survival Analysis in R. R package version 3.8-3, https://CRAN.R-project.org/package=survival. Therneau TM, Grambsch PM (2000). Modeling Survival Data: Extending the Cox Model. Springer.
R |  | 4.4.2 | R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

# Options

The following options can be set:

- reportRmd.digits, default is 2. Sets the default number of digits in output functions. Does not affect p-values.
- reportRmd.logScale, default is TRUE. The scale of the forest plots will default to log unless otherwise specified.

Example:

     rm_uvsum(response = 'baseline_ctdna',
     covs=c('age','sex','l_size','pdl1','tmb'),
     data=pembrolizumab)

| Estimate(95%CI) | p-value | N
--- | --- | --- | ---
Age at study entry | 0.82 (-10.13, 11.76) | 0.88 | 94
Patient Sex |  |  | 94
Female | Reference |  | 58
Male | 56.61 (-228.71, 341.93) | 0.69 | 36
Target lesion size at baseline | 1.21 (-1.12, 3.54) | 0.31 | 94
PD L1 percent | -3.50 (-8.27, 1.27) | 0.15 | 93
log of TMB | 18.78 (-125.18, 162.74) | 0.80 | 94

     options('reportRmd.digits'=1)

    rm_uvsum(response = 'baseline_ctdna',
     covs=c('age','sex','l_size','pdl1','tmb'),
     data=pembrolizumab)

| Estimate(95%CI) | p-value | N
--- | --- | --- | ---
Age at study entry | 0.8 (-10.1, 11.8) | 0.88 | 94
Patient Sex |  |  | 94
Female | Reference |  | 58
Male | 56.6 (-228.7, 341.9) | 0.69 | 36
Target lesion size at baseline | 1.2 (-1.1, 3.5) | 0.31 | 94
PD L1 percent | -3.5 (-8.3, 1.3) | 0.15 | 93
log of TMB | 18.8 (-125.2, 162.7) | 0.80 | 94

# PDF Output

For pdf to be correctly generate when using survival curves it is recommended that the cairo format be used. This can be specified with the following command in the setup code chunk:

`knitr::opts_chunk$set(message = FALSE, warning = FALSE,dev="cairo_pdf")`

# Data Sets

## pembrolizumab

Survival status and ctDNA levels for patients receiving pembrolizumab

A data frame with 94 rows and 15 variables:

- id Patient ID
- age Age at study entry
- sex Patient Sex
- cohort Study Cohort: A = Squamous cell carcinoma of soft pallate, B = Triple negative breast cancer, C = Ovarian, high grade serous, D = Melanoma, E = Other Solid Tumor
- l_size Target lesion size at baseline
- pdl1 PD L1 percent
- tmb log of TMB
- baseline_ctdna Baseline ctDNA
- change_ctdna_group Did ctDNA increase or decrease from baseline to cycle 3
- orr Objective Response
- cbr Clinical Beneficial Response
- os_status Overall survival status, 0 = alive, 1 = deceased
- os_time Overall survival time in months
- pfs_status Progression free survival status, 0 = progression free, 1 = progressed
- pfs_time Progression free survival time in months

## ctDNA

Longitudinal changes in tumour size since baseline for patients by changes in ctDNA status (clearance, decrease or increase) since baseline.

A data frame with 270 rows and 5 variables:

- id Patient ID
- cohort Study Cohort: A = Squamous cell carcinoma of soft pallate, B = Triple negative breast cancer, C = Ovarian, high grade serous, D = Melanoma, E = Other Solid Tumor
- ctdna_status Change in ctDNA since baseline
- time Number of weeks on treatment
- size_change Percentage change in tumour measurement