Package 'reportRmd'

Title: Tidy Presentation of Clinical Reporting
Description: 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.
Authors: Lisa Avery [cre, aut] , Ryan Del Bel [aut], Osvaldo Espin-Garcia [aut], Katherine Lajkosz [aut] , Tyler Pittman [aut] , Anna Santiago [aut] , Yanning Wang [ctr], Jessica Weiss [aut], Wei Xu [aut]
Maintainer: Lisa Avery <[email protected]>
License: MIT + file LICENSE
Version: 0.1.0
Built: 2024-12-19 06:58:22 UTC
Source: CRAN

Help Index

Add spaces to strings in LaTeX

Description

Add spaces to strings in LaTeX. Returns appends ~~~ before the string

Usage

addspace(x)

Arguments

x

string

fit box cox transformed linear model

Description

Wrapper function to fit fine and gray competing risk model using function crr from package cmprsk

Usage

boxcoxfitRx(f, data, lambda = FALSE)

Arguments

f

formula for the model. Currently the formula only works by using the name of the column in a dataframe. It does not work by using $ or [] notation.
data

dataframe containing data
lambda

boolean indicating if you want to output the lamda used in the boxcox transformation. If so the function will return a list of length 2 with the model as the first element and a vector of length 2 as the second.

Value

a list containing the linear model (lm) object and, if requested, lambda

Capitalize a string

Description

Capitalize a string

Usage

cap(x)

Arguments

x

string

Clear variable labels from a data frame

Description

This function will remove all label attributes from variables in the data.

Usage

clear_labels(data)

Arguments

data

the data frame to remove labels from

Details

To change or remove individual labels use set_labels or set_var_labels

Examples

# Set a few variable labels for ctDNA
ctDNA <- ctDNA |> set_var_labels(
   ctdna_status="detectable ctDNA",
  cohort="A cohort label")
# Clear all variable data frames and check
clear_labels(ctDNA)

Get covariate summary dataframe

Description

Returns a dataframe corresponding to a descriptive table.

Usage

covsum(
  data,
  covs,
  maincov = NULL,
  digits = 1,
  numobs = NULL,
  markup = TRUE,
  sanitize = TRUE,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  show.tests = FALSE,
  dropLevels = TRUE,
  excludeLevels = NULL,
  full = TRUE,
  digits.cat = 0,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  include_missing = FALSE,
  percentage = c("column", "row")
)

Arguments

data

dataframe containing data
covs

character vector with the names of columns to include in table
maincov

covariate to stratify table by
digits

number of digits for summarizing mean data, does not affect p-values
numobs

named list overriding the number of people you expect to have the covariate
markup

boolean indicating if you want latex markup
sanitize

boolean indicating if you want to sanitize all strings to not break LaTeX
nicenames

boolean indicating if you want to replace . and _ in strings with a space
IQR

boolean indicating if you want to display the inter quantile range (Q1,Q3) as opposed to (min,max) in the summary for continuous variables
all.stats

boolean indicating if all summary statistics (Q1,Q3 + min,max on a separate line) should be displayed. Overrides IQR.
pvalue

boolean indicating if you want p-values included in the table
effSize

boolean indicating if you want effect sizes included in the table. Can only be obtained if pvalue is also requested. Effect sizes calculated include Cramer's V for categorical variables, Cohen's d, Wilcoxon r, or Eta-squared for numeric/continuous variables.
show.tests

boolean indicating if the type of statistical test and effect size used should be shown in a column beside the pvalues. Ignored if pvalue=FALSE.
dropLevels

logical, indicating if empty factor levels be dropped from the output, default is TRUE.
excludeLevels

a named list of covariate levels to exclude from statistical tests in the form list(varname =c('level1','level2')). These levels will be excluded from association tests, but not the table. This can be useful for levels where there is a logical skip (ie not missing, but not presented). Ignored if pvalue=FALSE.
full

boolean indicating if you want the full sample included in the table, ignored if maincov is NULL
digits.cat

number of digits for the proportions when summarizing categorical data (default: 0)
testcont

test of choice for continuous variables,one of rank-sum (default) or ANOVA
testcat

test of choice for categorical variables,one of Chi-squared (default) or Fisher
include_missing

Option to include NA values of maincov. NAs will not be included in statistical tests
percentage

choice of how percentages are presented ,one of column (default) or row

Details

Comparisons for categorical variables default to chi-square tests, but if there are counts of <5 then the Fisher Exact test will be used and if this is unsuccessful then a second attempt will be made computing p-values using MC simulation. If testcont='ANOVA' then the t-test with unequal variance will be used for two groups and an ANOVA will be used for three or more. The statistical test used can be displayed by specifying show.tests=TRUE.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

References

Ellis, P.D. (2010) The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.doi:10.1017/CBO9780511761676

Lakens, D. (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4; 863:1-12. doi:10.3389/fpsyg.2013.00863

See Also

fisher.test,chisq.test, wilcox.test,kruskal.test,and anova

fit crr model

Description

Wrapper function to fit fine and gray competing risk model using function crr from package cmprsk

Usage

crrRx(f, data)

Arguments

f

formula for the model. Currently the formula only works by using the name of the column in a dataframe. It does not work by using $ or [] notation.
data

dataframe containing data

Value

a competing risk model with the call appended to the list

See Also

crr

Examples

# From the crr help file:
set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2,200,replace=TRUE)
cov <- matrix(runif(600),nrow=200)
dimnames(cov)[[2]] <- c('x1','x2','x3')
df <- data.frame(ftime,fstatus,cov)
m1 <- crrRx(as.formula('ftime+fstatus~x1+x2+x3'),df)
# Nicely output to report:
rm_mvsum(m1,data=df,showN = TRUE,vif=TRUE)

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

Description

Tumour size change over time

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

Usage

ctDNA

Format

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

Source

https://www.nature.com/articles/s43018-020-0096-5

Retrieve columns number from spreadsheet columns specified as unquoted letters

Description

Retrieve columns number from spreadsheet columns specified as unquoted letters

Usage

excelCol(...)

Arguments

...

unquoted excel column headers (i.e. excelCol(A,CG,AA)) separated by commas

Value

a numeric vector corresponding to columns in a spreadsheet

Examples

## Find the column numbers for excel columns AB, CE and BB
excelCol(AB,CE,bb)
## Get the columns between A and K and Z
excelCol(A-K,Z)

Retrieve spreadsheet column letter-names from columns indices

Description

Creates a vector of spreadsheet-style letter-names corresponding to column numbers

Usage

excelColLetters(columnIndices)

Arguments

columnIndices

vector of integer column indices

Details

This is the inverse function of excelCol

Value

a character vector corresponding to the spreadsheet column headings

Examples

## Find the column numbers for excel columns AB, CE and BB
colIndices <- excelCol(AB,CE,bb)
## Go back to the column names
excelColLetters(colIndices)

Extract variable labels from labelled data frame

Description

Extract variable labels from data and return a data frame with labels

Usage

extract_labels(data, sep = "_")

Arguments

data

the data frame to extract labels from
sep

character used to separate multiple labels, defaults to "_"

Details

All variable names will be returned, even those with no labels. If the label attribute has length greater than one the values will be concatenated and returned as a single string separated by sep

Examples

# Set a few variable labels for ctDNA
ctDNA <- ctDNA |> set_var_labels(
   ctdna_status="detectable ctDNA",
  cohort="A cohort label")
# Extract labels
extract_labels(ctDNA)

Create a forest plot using ggplot2

Description

This function will accept a log or logistic regression fit from glm or geeglm, and display the OR or RR for each variable on the appropriate log scale.

Usage

forestplot2(
  model,
  conf.level = 0.95,
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  logScale = getOption("reportRmd.logScale", TRUE),
  nxTicks = 5
)

Arguments

model

an object output from the glm or geeglm function, must be from a logistic regression
conf.level

controls the width of the confidence interval
orderByRisk

logical, should the plot be ordered by risk
colours

can specify colours for risks less than, 1 and greater than 1.0. Default is red, black, green
showEst

logical, should the risks be displayed on the plot in text
rmRef

logical, should the reference levels be removed for the plot?
logScale

logical, should OR/RR be shown on log scale, defaults to TRUE, or reportRmd.logScale if set. See https://doi.org/10.1093/aje/kwr156 for why you may prefer a linear scale.
nxTicks

Number of tick marks supplied to the log_breaks function to produce

Value

a plot object

Examples

data("pembrolizumab")
glm_fit = glm(orr~change_ctdna_group+sex+age+l_size,
data=pembrolizumab,family = 'binomial')
forestplot2(glm_fit)

Create a multivariable forest plot using ggplot2

Description

This function will send and take log or logistic regression fit from glm or geeglm from mvsum function, and display the OR or RR for each variable on the appropriate log scale.

Usage

forestplotMV(
  model,
  data,
  conf.level = 0.95,
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  digits = getOption("reportRmd.digits", 2),
  logScale = getOption("reportRmd.logScale", TRUE),
  nxTicks = 5,
  showN = TRUE,
  showEvent = TRUE
)

Arguments

model

an object output from the glm or geeglm function, must be from a logistic regression
data

dataframe containing your data
conf.level

controls the width of the confidence interval
orderByRisk

logical, should the plot be ordered by risk
colours

can specify colours for risks less than, 1 and greater than 1.0. Default is red, black, green
showEst

logical, should the risks be displayed on the plot in text
rmRef

logical, should the reference levels be removed for the plot?
digits

number of digits to use displaying estimates
logScale

logical, should OR/RR be shown on log scale, defaults to TRUE, or reportRmd.logScale if set. See https://doi.org/10.1093/aje/kwr156 for why you may prefer a linear scale.
nxTicks

Number of tick marks supplied to the log_breaks function to produce
showN

Show number of observations per variable and category
showEvent

Show number of events per variable and category

Value

a plot object

Examples

data("pembrolizumab")
glm_fit = glm(orr~change_ctdna_group+sex+age+l_size,
data=pembrolizumab,family = 'binomial')
forestplotMV(glm_fit)

Create an univariable forest plot using ggplot2

Description

This function will send and take log or logistic regression fit from glm or geeglm from uvsum function, and display the OR or RR for each variable on the appropriate log scale.

Usage

forestplotUV(
  response,
  covs,
  data,
  id = NULL,
  corstr = NULL,
  model = "glm",
  family = NULL,
  digits = getOption("reportRmd.digits", 2),
  conf.level = 0.95,
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  logScale = getOption("reportRmd.logScale", TRUE),
  nxTicks = 5,
  showN = TRUE,
  showEvent = TRUE
)

Arguments

response

character vector with names of columns to use for response
covs

character vector with names of columns to use for covariates
data

dataframe containing your data
id

character vector which identifies clusters. Only used for geeglm
corstr

character string specifying the correlation structure. Only used for geeglm. The following are permitted: '"independence"', '"exchangeable"', '"ar1"', '"unstructured"' and '"userdefined"'
model

fitted model object
family

description of the error distribution and link function to be used in the model. Only used for geeglm
digits

number of digits to round to
conf.level

controls the width of the confidence interval
orderByRisk

logical, should the plot be ordered by risk
colours

can specify colours for risks less than, 1 and greater than 1.0. Default is red, black, green
showEst

logical, should the risks be displayed on the plot in text
rmRef

logical, should the reference levels be removed for the plot?
logScale

logical, should OR/RR be shown on log scale, defaults to TRUE, or reportRmd.logScale if set. See https://doi.org/10.1093/aje/kwr156 for why you may prefer a linear scale.
nxTicks

Number of tick marks supplied to the log_breaks function to produce
showN

Show number of observations per variable and category
showEvent

Show number of events per variable and category

Value

a plot object

Examples

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

Combine an univariable and multivariable forest plot using ggplot2

Description

This function will take log or logistic regression fit forest plot output from forestplotUV and forestplotMV functions and display the combined adjusted and unadjusted OR or RR for each variable on the appropriate log scale. Please note that total N and reference-level N is taken from unadjusted model.

Usage

forestplotUVMV(
  UVmodel,
  MVmodel,
  model = "glm",
  family = NULL,
  digits = getOption("reportRmd.digits", 2),
  orderByRisk = TRUE,
  colours = "default",
  showEst = TRUE,
  rmRef = FALSE,
  logScale = FALSE,
  nxTicks = 5,
  showN = TRUE,
  showEvent = TRUE
)

Arguments

UVmodel

an UV model object output from the forestplotUV function
MVmodel

a MV model object output from the forestplotMV function
model

fitted model object
family

description of the error distribution and link function to be used in the model. Only used for geeglm
digits

number of digits to round to
orderByRisk

logical, should the plot be ordered by risk
colours

can specify colours for risks less than, 1 and greater than 1.0. Default is red, black, green
showEst

logical, should the risks be displayed on the plot in text
rmRef

logical, should the reference levels be removed for the plot?
logScale

logical, should OR/RR be shown on log scale, defaults to TRUE. See https://doi.org/10.1093/aje/kwr156 for why you may prefer a linear scale.
nxTicks

Number of tick marks supplied to the log_breaks function to produce
showN

Show number of observations per variable and category
showEvent

Show number of events per variable and category

Value

a plot object

Examples

data("pembrolizumab")
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)

Specific p-value formatting

Description

If p < 0.001 returns "<0.001", if p < 0.01 returns p to 3 decimal places otherwise returns p to 2 decimal places

Usage

formatp(pvalues)

Arguments

pvalues

a vector of p values

Parameter Estimation for the Box-Cox Transformation

Description

This function is copied from the geoR package which has been removed from the CRAN repository.

Usage

geoR_boxcoxfit(object, xmat, lambda, lambda2 = NULL, add.to.data = 0)

Arguments

object

a vector with the data
xmat

a matrix with covariates values. Defaults to rep(1, length(y)).
lambda

numerical value(s) for the transformation parameter lambda. Used as the initial value in the function for parameter estimation. If not provided default values are assumed. If multiple values are passed the one with highest likelihood is used as initial value.
lambda2

ogical or numerical value(s) of the additional transformation (see DETAILS below). Defaults to NULL. If TRUE this parameter is also estimated and the initial value is set to the absolute value of the minimum data. A numerical value is provided it is used as the initial value. Multiple values are allowed as for lambda.
add.to.data

a constant value to be added to the data.

Details

For more information see: https://cran.r-project.org/web/packages/geoR/index.html

Plot KM and CIF curves with ggplot

Description

This function will plot a KM or CIF curve with option to add the number at risk. You can specify if you want confidence bands, the hazard ratio, and pvalues, as well as the units of time used.

Usage

ggkmcif(
  response,
  cov = NULL,
  data,
  type = NULL,
  pval = TRUE,
  HR = FALSE,
  HR_pval = FALSE,
  conf.curves = FALSE,
  conf.type = "log",
  table = TRUE,
  times = NULL,
  xlab = "Time",
  ylab = NULL,
  main = NULL,
  stratalabs = NULL,
  strataname = nicename(cov),
  stratalabs.table = NULL,
  strataname.table = strataname,
  median.text = FALSE,
  median.lines = FALSE,
  median.CI = FALSE,
  set.time.text = NULL,
  set.time.line = FALSE,
  set.time = 5,
  set.time.CI = FALSE,
  censor.marks = TRUE,
  censor.size = 3,
  censor.stroke = 1.5,
  fsize = 10,
  nsize = 3,
  lsize = 1,
  psize = 3.5,
  median.size = 3,
  median.pos = NULL,
  median.lsize = 1,
  set.size = 3,
  set.pos = NULL,
  set.lsize = 1,
  ylim = c(0, 1),
  col = NULL,
  linetype = NULL,
  xlim = NULL,
  legend.pos = NULL,
  pval.pos = NULL,
  plot.event = 1,
  event = c("col", "linetype"),
  flip.CIF = FALSE,
  cut = NULL,
  eventlabs = NULL,
  event.name = NULL,
  Numbers_at_risk_text = "Numbers at risk",
  HR.digits = 2,
  HR.pval.digits = 3,
  pval.digits = 3,
  median.digits = 3,
  set.time.digits = 3,
  returns = FALSE,
  print.n.missing = TRUE
)

Arguments

response

character vector with names of columns to use for response
cov

String specifying the column name of stratification variable
data

dataframe containing your data
type

string indicating he type of univariate model to fit. The function will try and guess what type you want based on your response. If you want to override this you can manually specify the type. Options include "KM", and ,"CIF"
pval

boolean to specify if you want p-values in the plot (Log Rank test for KM and Gray's test for CIF)
HR

boolean to specify if you want hazard ratios included in the plot
HR_pval

boolean to specify if you want HR p-values in the plot
conf.curves

boolean to specify if you want confidence interval bands
conf.type

One of "none"(the default), "plain", "log" , "log-log" or "logit". Only enough of the string to uniquely identify it is necessary. The first option causes confidence intervals not to be generated. The second causes the standard intervals curve +- k *se(curve), where k is determined from conf.int. The log option calculates intervals based on the cumulative hazard or log(survival). The log-log option bases the intervals on the log hazard or log(-log(survival)), and the logit option on log(survival/(1-survival)).
table

Logical value. If TRUE, includes the number at risk table
times

Numeric vector of times for the x-axis
xlab

String corresponding to xlabel. By default is "Time"
ylab

String corresponding to ylabel. When NULL uses "Survival probability" for KM cuves, and "Probability of an event" for CIF
main

String corresponding to main title. When NULL uses Kaplan-Meier Plot s, and "Cumulative Incidence Plot for CIF"
stratalabs

string corresponding to the labels of the covariate, when NULL will use the levels of the covariate
strataname

String of the covariate name default is nicename(cov)
stratalabs.table

String corresponding to the levels of the covariate for the number at risk table, when NULL will use the levels of the covariate. Can use a string of "-" when the labels are long
strataname.table

String of the covariate name for the number at risk table default is nicename(cov
median.text

boolean to specify if you want the median values added to the legend (or as added text if there are no covariates), for KM only
median.lines

boolean to specify if you want the median values added as lines to the plot, for KM only
median.CI

boolean to specify if you want the 95\ with the median text (Only for KM)
set.time.text

string for the text to add survival at a specified time (eg. year OS)
set.time.line

boolean to specify if you want the survival added as lines to the plot at a specified point
set.time

Numeric values of the specific time of interest, default is 5 (Multiple values can be entered)
set.time.CI

boolean to specify if you want the 95\ interval with the set time text
censor.marks

logical value. If TRUE, includes censor marks (only for KM curves)
censor.size

size of censor marks, default is 3
censor.stroke

stroke of censor marks, default is 1.5
fsize

font size
nsize

font size for numbers in the numbers at risk table
lsize

line size
psize

size of the pvalue
median.size

size of the median text (Only when there are no covariates)
median.pos

vector of length 2 corresponding to the median position (Only when there are no covariates)
median.lsize

line size of the median lines
set.size

size of the survival at a set time text (Only when there are no covariates)
set.pos

vector of length 2 corresponding to the survival at a set point position (Only when there are no covariates)
set.lsize

line size of the survival at set points
ylim

vector of length 2 corresponding to limits of y-axis. Default to NULL
col

vector of colours
linetype

vector of line types
xlim

vector of length 2 corresponding to limits of x-axis. Default to NULL
legend.pos

Can be either a string corresponding to the legend position ("left","top", "right", "bottom", "none") or a vector of length 2 corresponding to the legend position (uses normalized units (ie the c(0.5,0.5) is the middle of the plot))
pval.pos

vector of length 2 corresponding to the p-value position
plot.event

Which event(s) to plot (1,2, or c(1,2))
event

String specifying if the event should be mapped to the colour, or linetype when plotting both events to colour = "col", line type
flip.CIF

boolean to flip the CIF curve to start at 1
cut

numeric value indicating where to divide a continuous covariate (default is the median)
eventlabs

String corresponding to the event type names
event.name

String corresponding to the label of the event types
Numbers_at_risk_text

String for the label of the number at risk
HR.digits

Number of digits printed of the hazard ratio
HR.pval.digits

Number of digits printed of the hazard ratio pvalue
pval.digits

Number of digits printed of the Gray's/log rank pvalue
median.digits

Number of digits printed of the median pvalue
set.time.digits

Number of digits printed of the probability at a specified time
returns

Logical value returns a list with all ggplot objects in a list
print.n.missing

Logical, should the number of missing be shown !Needs to be checked

Details

Note that for proper pdf output of special characters the following code needs to be included in the first chunk of the rmd knitr::opts_chunk$set(dev="cairo_pdf")

Value

Nothing is returned unless returns = TRUE is used. With returns = TRUE, if table=TRUE (the default) a table style graphic with survival plot and number at risk table is returned. Otherwise a plot with the survival curves is returned.

Examples

data("pembrolizumab")
# Simple plot without confidence intervals
ggkmcif(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)

# Plot with median survival time
ggkmcif(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = TRUE,median.lines=TRUE,conf.curves=TRUE)

# Plot with specified survival times and log-log CI
ggkmcif(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = FALSE,set.time.text = 'mo OS',
set.time = c(12,24),conf.type = 'log-log',conf.curves=TRUE)

# KM plot with 95% CI and censor marks
ggkmcif(c('os_time','os_status'),'sex',data = pembrolizumab, type = 'KM',
HR=TRUE, HR_pval = TRUE, conf.curves = TRUE,conf.type='log-log',
set.time.CI = TRUE, censor.marks=TRUE)

Plot KM and CIF curves with ggplot

Description

This function puts together a survival curve, and a number at risk table

Usage

ggkmcif_paste(list_gg)

Arguments

list_gg

list containing the results of ggkmcif

Value

a gtable with three elements, the survival curve, a spacer and the number at risk table

Examples

data("pembrolizumab")
plot <- ggkmcif(response=c('pfs_time','pfs_status'),
data=pembrolizumab,returns = TRUE)

# Highlighting a section of the curve
plot[[1]] <- plot[[1]] +
ggplot2::geom_rect(xmin=4,xmax=8,ymin=0.15,ymax=0.4,alpha=0.01,fill='yellow')

# Putting the curve back together
ggkmcif_paste(plot)

Plot KM and CIF curves with ggplot

Description

This function will plot a KM or CIF curve with option to add the number at risk. You can specify if you want confidence bands, the hazard ratio, and pvalues, as well as the units of time used.

Usage

ggkmcif2(
  response,
  cov = NULL,
  data,
  pval = TRUE,
  conf.curves = FALSE,
  table = TRUE,
  xlab = "Time",
  ylab = NULL,
  col = NULL,
  times = NULL,
  type = NULL,
  plot.event = 1,
  ...
)

Arguments

response

character vector with names of columns to use for response
cov

String specifying the column name of stratification variable
data

dataframe containing your data
pval

boolean to specify if you want p-values in the plot (Log Rank test for KM and Gray's test for CIF)
conf.curves

boolean to specify if you want confidence interval bands
table

Logical value. If TRUE, includes the number at risk table
xlab

String corresponding to xlabel. By default is "Time"
ylab

String corresponding to ylabel. When NULL uses "Survival
col

vector of colours
times

Numeric vector of times for the x-axis probability" for KM cuves, and "Probability of an event" for CIF
type

string indicating he type of univariate model to fit. The function will try and guess what type you want based on your response. If you want to override this you can manually specify the type. Options include "KM", and ,"CIF"
plot.event

Which event(s) to plot (1,2, or c(1,2))
...

additional plotting arguments see ggkmcif2Parameters

Details

Note that for proper pdf output of special characters the following code needs to be included in the first chunk of the rmd knitr::opts_chunk$set(dev="cairo_pdf")

Value

ggplot object; if table = F then only curves are output; if table = T then curves and risk table are output together

Examples

# Simple plot without confidence intervals
data("pembrolizumab")
ggkmcif2(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)

# Plot with median survival time
ggkmcif2(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = TRUE,median.lines=TRUE,conf.curves=TRUE)

# Plot with specified survival times and log-log CI
ggkmcif2(response = c('os_time','os_status'),
cov='sex',
data=pembrolizumab,
median.text = FALSE,set.time.text = 'mo OS',
set.time = c(12,24),conf.type = 'log-log',conf.curves=TRUE)

# KM plot with 95% CI and censor marks
ggkmcif2(c('os_time','os_status'),'sex',data = pembrolizumab, type = 'KM',
HR=TRUE, HR_pval = TRUE, conf.curves = TRUE,conf.type='log-log',
set.time.CI = TRUE, censor.marks=TRUE)

Additional parameters passed to ggkmcif2

Description

Additional parameters passed to ggkmcif2

Usage

ggkmcif2Parameters(
  table.height = NULL,
  HR = FALSE,
  HR_pval = FALSE,
  conf.type = "log",
  main = NULL,
  stratalabs = NULL,
  strataname,
  stratalabs.table = NULL,
  strataname.table = strataname,
  median.text = FALSE,
  median.lines = FALSE,
  median.CI = FALSE,
  set.time.text = NULL,
  set.time.line = FALSE,
  set.time = 5,
  set.time.CI = FALSE,
  censor.marks = TRUE,
  censor.size = 3,
  censor.stroke = 1.5,
  fsize = 11,
  nsize = 3,
  lsize = 1,
  psize = 3.5,
  median.size = 3,
  median.pos = NULL,
  median.lsize = 1,
  set.size = 3,
  set.pos = NULL,
  set.lsize = 1,
  ylim = c(0, 1),
  linetype = NULL,
  xlim = NULL,
  legend.pos = NULL,
  pval.pos = NULL,
  event = c("col", "linetype"),
  flip.CIF = FALSE,
  cut = NULL,
  eventlabs = NULL,
  event.name = NULL,
  Numbers_at_risk_text = "Number at risk",
  HR.digits = 2,
  HR.pval.digits = 3,
  pval.digits = 3,
  median.digits = 3,
  set.time.digits = 3,
  print.n.missing = TRUE
)

Arguments

table.height

Relative height of risk table (0-1)
HR

boolean to specify if you want hazard ratios included in the plot
HR_pval

boolean to specify if you want HR p-values in the plot
conf.type

One of "none"(the default), "plain", "log" , "log-log" or "logit". Only enough of the string to uniquely identify it is necessary. The first option causes confidence intervals not to be generated. The second causes the standard intervals curve +- k *se(curve), where k is determined from conf.int. The log option calculates intervals based on the cumulative hazard or log(survival). The log-log option bases the intervals on the log hazard or log(-log(survival)), and the logit option on log(survival/(1-survival)).
main

String corresponding to main title. When NULL uses Kaplan-Meier Plot s, and "Cumulative Incidence Plot for CIF"
stratalabs

string corresponding to the labels of the covariate, when NULL will use the levels of the covariate
strataname

String of the covariate name default is nicename(cov)
stratalabs.table

String corresponding to the levels of the covariate for the number at risk table, when NULL will use the levels of the covariate. Can use a string of "-" when the labels are long
strataname.table

String of the covariate name for the number at risk table default is nicename(cov
median.text

boolean to specify if you want the median values added to the legend (or as added text if there are no covariates), for KM only
median.lines

boolean to specify if you want the median values added as lines to the plot, for KM only
median.CI

boolean to specify if you want the 95\ with the median text (Only for KM)
set.time.text

string for the text to add survival at a specified time (eg. year OS)
set.time.line

boolean to specify if you want the survival added as lines to the plot at a specified point
set.time

Numeric values of the specific time of interest, default is 5 (Multiple values can be entered)
set.time.CI

boolean to specify if you want the 95\ interval with the set time text
censor.marks

logical value. If TRUE, includes censor marks (only for KM curves)
censor.size

size of censor marks, default is 3
censor.stroke

stroke of censor marks, default is 1.5
fsize

font size
nsize

font size for numbers in the numbers at risk table
lsize

line size
psize

size of the pvalue
median.size

size of the median text (Only when there are no covariates)
median.pos

vector of length 2 corresponding to the median position (Only when there are no covariates)
median.lsize

line size of the median lines
set.size

size of the survival at a set time text (Only when there are no covariates)
set.pos

vector of length 2 corresponding to the survival at a set point position (Only when there are no covariates)
set.lsize

line size of the survival at set points
ylim

vector of length 2 corresponding to limits of y-axis. Default to NULL
linetype

vector of line types; default is solid for all lines
xlim

vector of length 2 corresponding to limits of x-axis. Default to NULL
legend.pos

Can be either a string corresponding to the legend position ("left","top", "right", "bottom", "none") or a vector of length 2 corresponding to the legend position (uses normalized units (ie the c(0.5,0.5) is the middle of the plot))
pval.pos

vector of length 2 corresponding to the p-value position
event

String specifying if the event should be mapped to the colour, or linetype when plotting both events to colour = "col", line type
flip.CIF

boolean to flip the CIF curve to start at 1
cut

numeric value indicating where to divide a continuous covariate (default is the median)
eventlabs

String corresponding to the event type names
event.name

String corresponding to the label of the event types
Numbers_at_risk_text

String for the label of the number at risk
HR.digits

Number of digits printed of the hazard ratio
HR.pval.digits

Number of digits printed of the hazard ratio pvalue
pval.digits

Number of digits printed of the Gray's/log rank pvalue
median.digits

Number of digits printed of the median pvalue
set.time.digits

Number of digits printed of the probability at a specified time
print.n.missing

Logical, should the number of missing be shown !Needs to be checked

Bold strings in HTML

Description

Bold strings in HTML

Usage

hbld(strings)

Arguments

strings

A vector of strings to bold.

Bold strings in LaTeX

Description

Bold strings in LaTeX

Usage

lbld(strings)

Arguments

strings

A vector of strings to bold.

Formats p-values for LaTeX

Description

Returns <0.001 if pvalue is <0.001. Else rounds the pvalue to specified significant digits. Will bold the p-value if it is <= 0.05

Usage

lpvalue(x, sigdigits = 2)

Arguments

x

an integer
sigdigits

number of significant digit to report

Get multivariate summary dataframe

Description

Returns a dataframe with the model summary and global p-value for multi-level variables.

Usage

mvsum(
  model,
  data,
  digits = getOption("reportRmd.digits", 2),
  showN = TRUE,
  showEvent = TRUE,
  markup = TRUE,
  sanitize = TRUE,
  nicenames = TRUE,
  CIwidth = 0.95,
  vif = TRUE
)

Arguments

model

fitted model object
data

dataframe containing data
digits

number of digits to round to
showN

boolean indicating sample sizes should be shown for each comparison, can be useful for interactions
showEvent

boolean indicating if number of events should be shown. Only available for logistic.
markup

boolean indicating if you want latex markup
sanitize

boolean indicating if you want to sanitize all strings to not break LaTeX
nicenames

boolean indicating if you want to replace . and _ in strings with a space.
CIwidth

width for confidence intervals, defaults to 0.95
vif

boolean indicating if the variance inflation factor should be included. See details

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned.

If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.

VIF for competing risk models is computed by fitting a linear model with a dependent variable comprised of the sum of the model independent variables and then calculating VIF from this linear model.

References

John Fox & Georges Monette (1992) Generalized Collinearity Diagnostics, Journal of the American Statistical Association, 87:417, 178-183, DOI: 10.1080/01621459.1992.10475190

John Fox and Sanford Weisberg (2019). An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage.

Combine two table columns into a single column with levels of one nested within levels of the other.

Description

This function accepts a data frame (via the data argument) and combines two columns into a single column with values from the head_col serving as headers and values of the to_col displayed underneath each header. The resulting table is then passed to outTable for printing and output, to use the grouped table as a data frame specify tableOnly=TRUE. By default the headers will be bolded and the remaining values indented.

Usage

nestTable(
  data,
  head_col,
  to_col,
  colHeader = "",
  caption = NULL,
  indent = TRUE,
  boldheaders = TRUE,
  hdr_prefix = "",
  hdr_suffix = "",
  digits = getOption("reportRmd.digits", 2),
  tableOnly = FALSE,
  fontsize
)

Arguments

data

dataframe
head_col

character value specifying the column name with the headers
to_col

character value specifying the column name to add the headers into
colHeader

character with the desired name of the first column. The default is to leave this empty for output or, for table only output to use the column name 'col1'.
caption

table caption
indent

Boolean should the original values in the to_col be indented
boldheaders

Boolean should the header column values be bolded
hdr_prefix

character value that will prefix headers
hdr_suffix

character value that will suffix headers
digits

number of digits to round numeric columns to, wither a single number or a vector corresponding to the number of numeric columns
tableOnly

boolean indicating if the table should be formatted for printing or returned as a data frame
fontsize

PDF/HTML output only, manually set the table fontsize

Details

Note that it is possible to combine multiple tables (more than two) with this function.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

## Investigate models to predict baseline ctDNA and tumour size and display together
## (not clinically useful!)
data(pembrolizumab)
fit1 <- lm(baseline_ctdna~age+l_size+pdl1,data=pembrolizumab)
m1 <- rm_mvsum(fit1,tableOnly=TRUE)
m1$Response = 'ctDNA'
fit2 <- lm(l_size~age+baseline_ctdna+pdl1,data=pembrolizumab)
m2 <- rm_mvsum(fit2,tableOnly=TRUE)
m2$Response = 'Tumour Size'
nestTable(rbind(m1,m2),head_col='Response',to_col='Covariate')

Lean strings for printing

Description

Returns strings with . and _ replaced by a space. This is nice when printing column names of your dataframe in a report

Usage

nicename(strings, check_numbers = TRUE)

Arguments

strings

vector of strings to give a nice name
check_numbers

boolean indicating if numbers with decimals should be checked for and retained.

Round retaining digits

Description

Round retaining digits

Usage

niceNum(x, digits = 2)

Arguments

x

a vector
digits

numeric

Print tables to PDF/Latex HTML or Word

Description

Output the table nicely to whatever format is appropriate. This is the output function used by the rm_* printing functions.

Usage

outTable(
  tab,
  row.names = NULL,
  to_indent = numeric(0),
  bold_headers = TRUE,
  rows_bold = numeric(0),
  bold_cells = NULL,
  caption = NULL,
  digits = getOption("reportRmd.digits", 2),
  align,
  applyAttributes = TRUE,
  keep.rownames = FALSE,
  nicenames = TRUE,
  fontsize,
  chunk_label,
  format = NULL
)

Arguments

tab

a table to format
row.names

a string specifying the column name to assign to the rownames. If NULL (the default) then rownames are removed.
to_indent

numeric vector indicating which rows to indent in the first column.
bold_headers

boolean indicating if the column headers should be bolded
rows_bold

numeric vector indicating which rows to bold
bold_cells

array indices indicating which cells to bold. These will be in addition to rows bolded by rows_bold.
caption

table caption
digits

number of digits to round numeric columns to, wither a single number or a vector corresponding to the number of numeric columns in tab
align

string specifying column alignment, defaults to left alignment of the first column and right alignment of all other columns. The align argument accepts a single string with 'l' for left, 'c' for centre and 'r' for right, with no separations. For example, to set the left column to be centred, the middle column right-aligned and the right column left aligned use: align='crl'
applyAttributes

boolean indicating if the function should use to_indent and bold_cells formatting attributes. This will only work properly if the dimensions of the table output from rm_covsum, rm_uvsum etc haven't changed.
keep.rownames

should the row names be included in the output
nicenames

boolean indicating if you want to replace . and _ in strings with a space
fontsize

PDF/HTML output only, manually set the table fontsize
chunk_label

only used knitting to Word docs to allow cross-referencing
format

if specified ('html','latex') will override the global pandoc setting

Details

Entire rows can be bolded, or specific cells. Currently indentation refers to the first column only. By default, underscores in column names are converted to spaces. To disable this set rm_ to FALSE

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

# To make custom changes or change the fontsize in PDF/HTML
data("pembrolizumab")
tab <- rm_covsum(data=pembrolizumab,maincov = 'change_ctdna_group',
covs=c('age','sex','pdl1','tmb','l_size'),show.tests=TRUE,tableOnly = TRUE)
outTable(tab, fontsize=7)

# To bold columns with the variable names
rows_bold <- c(1,4,7,10,13)
outTable(tab,rows_bold = rows_bold)

# To bold the estimates for male/female
bold_cells <- as.matrix(expand.grid(5:6,1:ncol(tab)))
outTable(tab,bold_cells= bold_cells)

# Output the above table to HTML or LaTeX
#cat(outTable(tab=tab)) #Knits to specified global setting
#cat(outTable(tab, format="html"), file = "tab.html") #HTML output
#cat(outTable(tab, format="latex"), file = "tab.tex") #LaTeX output

Survival data Survival status and ctDNA levels for patients receiving pembrolizumab

Description

Survival data

Survival status and ctDNA levels for patients receiving pembrolizumab

Usage

pembrolizumab

Format

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

Source

https://www.nature.com/articles/s43018-020-0096-5

Plot multiple bivariate relationships in a single plot

Description

This function is designed to accompany uvsum as a means of visualising the results, and uses similar syntax.

Usage

plotuv(
  response,
  covs,
  data,
  showN = FALSE,
  showPoints = TRUE,
  na.rm = TRUE,
  response_title = NULL,
  return_plotlist = FALSE,
  ncol = 2,
  p_margins = c(0, 0.2, 1, 0.2),
  bpThreshold = 20,
  mixed = TRUE
)

Arguments

response

character vector with names of columns to use for response
covs

character vector with names of columns to use for covariates
data

dataframe containing your data
showN

boolean indicating whether sample sizes should be shown on the plots
showPoints

boolean indicating whether individual data points should be shown when n>20 in a category
na.rm

boolean indicating whether na values should be shown or removed
response_title

character value with title of the plot
return_plotlist

boolean indicating that the list of plots should be returned instead of a plot, useful for applying changes to the plot, see details
ncol

the number of columns of plots to be display in the ggarrange call, defaults to 2
p_margins

sets the TRBL margins of the individual plots, defaults to c(0,0.2,1,.2)
bpThreshold

Default is 20, if there are fewer than 20 observations in a category then dotplots, as opposed to boxplots are shown.
mixed

should a mix of dotplots and boxplots be shown based on sample size? If false then all categories will be shown as either dotplots, or boxplots according the bpThreshold and the smallest category size

Details

Plots are displayed as follows: If response is continuous For a numeric predictor scatterplot For a categorical predictor: If 20+ observations available boxplot, otherwise dotplot with median line If response is a factor For a numeric predictor: If 20+ observations available boxplot, otherwise dotplot with median line For a categorical predictor barplot Response variables are shown on the ordinate (y-axis) and covariates on the abscissa (x-axis)

Value

a list containing plots for each variable in covs

a plot object

See Also

ggplot and ggarrange

Examples

## Run multiple univariate analyses on the pembrolizumab dataset to predict cbr and
## then visualise the relationships.
data("pembrolizumab")
rm_uvsum(data=pembrolizumab,
response='cbr',covs=c('age','sex','l_size','baseline_ctdna'))
plotuv(data=pembrolizumab,  response='cbr',
covs=c('age','sex','l_size','baseline_ctdna'),showN=TRUE)

Round and paste with parentheses

Description

Round and paste with parentheses

Usage

psthr(x, y = 2)

Arguments

x

a numeric vector
y

integer corresponding to the number of digits to round by

Paste with parentheses

Description

Paste with parentheses

Usage

pstprn(x)

Arguments

x

a vector

Formats p-values

Description

Returns <0.001 if pvalue is <0.001. Else rounds the pvalue to specified significant digits

Usage

pvalue(x, digits)

Arguments

x

an integer
digits

the number of significant digits to return

Summarize cumulative incidence by group

Description

Displays event counts and event rates at specified time points for the entire cohort and by group. Gray's test of differences in cumulative incidence is displayed.

Usage

rm_cifsum(
  data,
  time,
  status,
  group = NULL,
  eventcode = 1,
  cencode = 0,
  eventtimes,
  eventtimeunit,
  eventtimeLbls = NULL,
  CIwidth = 0.95,
  unformattedp = FALSE,
  na.action = "na.omit",
  showCounts = TRUE,
  showGraystest = TRUE,
  digits = 2,
  caption = NULL,
  tableOnly = FALSE
)

Arguments

data

data frame containing survival data
time

string indicating survival time variable
status

string indicating event status variable; must have at least 3 levels, e.g. 0 = censor, 1 = event, 2 = competing risk
group

string or character vector indicating the variable to group observations by
eventcode

numerical variable indicating event, default is 1
cencode

numerical variable indicating censored observation, default is 0
eventtimes

numeric vector specifying when event probabilities should be calculated
eventtimeunit

unit of time to suffix to the time column label if event probabilities are requested, should be plural
eventtimeLbls

if supplied, a vector the same length as eventtimes with descriptions (useful for displaying years with data provided in months)
CIwidth

width of the event probabilities, default is 95%
unformattedp

boolean indicating if you would like the p-value to be returned unformatted (ie not rounded or prefixed with '<'). Should be used in conjunction with the digits argument.
na.action

default is to omit missing values, but can be set to throw and error using na.action='na.fail'
showCounts

boolean indicating if the at risk, events and censored columns should be output, default is TRUE
showGraystest

boolean indicating Gray's test should be included in the final table, default is TRUE
digits

the number of digits to report in the event probabilities, default is 2.
caption

table caption for markdown output
tableOnly

should a dataframe or a formatted object be returned

Value

A character vector of the event table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

library(survival)
data(pbc)

# Event probabilities at various time points with replacement time labels
rm_cifsum(data=pbc,time='time',status='status',
eventtimes=c(1825,3650),eventtimeLbls=c(5,10),eventtimeunit='yr')

# Event probabilities by one group
rm_cifsum(data=pbc,time='time',status='status',group='trt',
eventtimes=c(1825,3650),eventtimeunit='day')


# Event probabilities by multiple groups
rm_cifsum(data=pbc,time='time',status='status',group=c('trt','sex'),
eventtimes=c(1825,3650),eventtimeunit='day')

Outputs a descriptive covariate table

Description

Returns a data frame corresponding to a descriptive table.

Usage

rm_covsum(
  data,
  covs,
  maincov = NULL,
  caption = NULL,
  tableOnly = FALSE,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  show.tests = FALSE,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  full = TRUE,
  include_missing = FALSE,
  percentage = c("column", "row"),
  dropLevels = TRUE,
  excludeLevels = NULL,
  numobs = NULL,
  fontsize,
  chunk_label
)

Arguments

data

dataframe containing data
covs

character vector with the names of columns to include in table
maincov

covariate to stratify table by
caption

character containing table caption (default is no caption)
tableOnly

Logical, if TRUE then a dataframe is returned, otherwise a formatted printed object is returned (default).
covTitle

character with the names of the covariate (predictor) column. The default is to leave this empty for output or, for table only output to use the column name 'Covariate'.
digits

number of digits for summarizing mean data
digits.cat

number of digits for the proportions when summarizing categorical data (default: 0)
nicenames

boolean indicating if you want to replace . and _ in strings with a space
IQR

boolean indicating if you want to display the inter quantile range (Q1,Q3) as opposed to (min,max) in the summary for continuous variables
all.stats

boolean indicating if all summary statistics (Q1,Q3 + min,max on a separate line) should be displayed. Overrides IQR.
pvalue

boolean indicating if you want p-values included in the table
effSize

boolean indicating if you want effect sizes included in the table. Can only be obtained if pvalue is also requested. Effect sizes calculated include Cramer's V for categorical variables, Cohen's d, Wilcoxon r, or Eta-squared for numeric/continuous variables.
p.adjust

p-adjustments to be performed
unformattedp

boolean indicating if you would like the p-value to be returned unformatted (ie not rounded or prefixed with '<'). Best used with tableOnly = T and outTable function. See examples.
show.tests

boolean indicating if the type of statistical test and effect size used should be shown in a column beside the pvalues. Ignored if pvalue=FALSE.
testcont

test of choice for continuous variables,one of rank-sum (default) or ANOVA
testcat

test of choice for categorical variables,one of Chi-squared (default) or Fisher
full

boolean indicating if you want the full sample included in the table, ignored if maincov is NULL
include_missing

Option to include NA values of maincov. NAs will not be included in statistical tests
percentage

choice of how percentages are presented, one of column (default) or row
dropLevels

logical, indicating if empty factor levels be dropped from the output, default is TRUE.
excludeLevels

a named list of covariate levels to exclude from statistical tests in the form list(varname =c('level1','level2')). These levels will be excluded from association tests, but not the table. This can be useful for levels where there is a logical skip (ie not missing, but not presented). Ignored if pvalue=FALSE.
numobs

named list overriding the number of people you expect to have the covariate
fontsize

PDF/HTML output only, manually set the table fontsize
chunk_label

only used if output is to Word to allow cross-referencing

Details

Comparisons for categorical variables default to chi-square tests, but if there are counts of <5 then the Fisher Exact test will be used and if this is unsuccessful then a second attempt will be made computing p-values using MC simulation. If testcont='ANOVA' then the t-test with unequal variance will be used for two groups and an ANOVA will be used for three or more. The statistical test used can be displayed by specifying show.tests=TRUE.

Effect size can be obtained when p-value is requested.

Further formatting options are available using tableOnly=TRUE and outputting the table with a call to outTable.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

References

Ellis, P.D. (2010) The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.doi:10.1017/CBO9780511761676

Lakens, D. (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4; 863:1-12. doi:10.3389/fpsyg.2013.00863

See Also

covsum,fisher.test, chisq.test, wilcox.test, kruskal.test, anova, cramer_v, eta_squared, and outTable

Examples

data("pembrolizumab")
rm_covsum(data=pembrolizumab, maincov = 'orr',
covs=c('age','sex','pdl1','tmb','l_size','change_ctdna_group'),
show.tests=TRUE)

# To Show Effect Sizes
rm_covsum(data=pembrolizumab, maincov = 'orr',
covs=c('age','sex'),
effSize=TRUE)

# To make custom changes or change the fontsize in PDF/HTML
tab <- rm_covsum(data=pembrolizumab,maincov = 'change_ctdna_group',
covs=c('age','sex','pdl1','tmb','l_size'),show.tests=TRUE,tableOnly = TRUE)
outTable(tab, fontsize=7)

# To return unformatted p-values
tab <- rm_covsum(data=pembrolizumab, maincov = 'orr',
covs=c('age','sex','pdl1','tmb','l_size','change_ctdna_group'),
show.tests=TRUE,unformattedp=TRUE,tableOnly=TRUE)
outTable(tab,digits=5)
outTable(tab,digits=5, applyAttributes=FALSE) # remove bold/indent

Format a regression model nicely for 'Rmarkdown'

Description

Multivariable (or univariate) regression models are re-formatted for reporting and a global p-value is added for the evaluation of factor variables.

Usage

rm_mvsum(
  model,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  vif = TRUE,
  caption = NULL,
  tableOnly = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  nicenames = TRUE,
  chunk_label,
  fontsize
)

Arguments

model

model fit
data

data that model was fit on (an attempt will be made to extract this from the model)
digits

number of digits to round estimates to, does not affect p-values
covTitle

character with the names of the covariate (predictor) column. The default is to leave this empty for output or, for table only output to use the column name 'Covariate'.
showN

boolean indicating sample sizes should be shown for each comparison, can be useful for interactions
showEvent

boolean indicating if number of events should be shown. Only available for logistic.
CIwidth

width for confidence intervals, defaults to 0.95
vif

boolean indicating if the variance inflation factor should be included. See details
caption

table caption
tableOnly

boolean indicating if unformatted table should be returned
p.adjust

p-adjustments to be performed (Global p-values only)
unformattedp

boolean indicating if you would like the p-value to be returned unformatted (ie not rounded or prefixed with '<'). Should be used in conjuction with the digits argument.
nicenames

boolean indicating if you want to replace . and _ in strings with a space
chunk_label

only used if output is to Word to allow cross-referencing
fontsize

PDF/HTML output only, manually set the table fontsize

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned. For negative binomial models a deviance test is used.

If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

References

John Fox & Georges Monette (1992) Generalized Collinearity Diagnostics, Journal of the American Statistical Association, 87:417, 178-183, doi:10.1080/01621459.1992.10475190

John Fox and Sanford Weisberg (2019). An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage.

Examples

data("pembrolizumab")
glm_fit = glm(change_ctdna_group~sex:age+baseline_ctdna+l_size,
data=pembrolizumab,family = 'binomial')
rm_mvsum(glm_fit)

#linear model with p-value adjustment
lm_fit=lm(baseline_ctdna~age+sex+l_size+tmb,data=pembrolizumab)
rm_mvsum(lm_fit,p.adjust = "bonferroni")
#Coxph
require(survival)
res.cox <- coxph(Surv(os_time, os_status) ~ sex+age+l_size+tmb, data = pembrolizumab)
rm_mvsum(res.cox, vif=TRUE)

Display event counts, expected event counts and logrank test of differences

Description

This is a wrapper function around the survdiff function to display overall event rates and group-specific rates along with the log-rank test of a difference in survival between groups in a single table suitable for markdown output. Median survival times are included by default but can be removed setting median=FALSE

Usage

rm_survdiff(
  data,
  time,
  status,
  covs,
  strata,
  includeVarNames = FALSE,
  digits = 1,
  showCols = c("N", "Observed", "Expected"),
  CIwidth = 0.95,
  conf.type = "log",
  caption = NULL,
  tableOnly = FALSE,
  fontsize
)

Arguments

data

data frame containing survival data
time

string indicating survival time variable
status

string indicating event status variable
covs

character vector indicating variables to group observations by
strata

string indicating the variable to stratify observations by
includeVarNames

boolean indicating if the variable names should be included in the output table, default is FALSE
digits

the number of digits in the survival rate
showCols

character vector indicating which of the optional columns to display, defaults to c('N','Observed','Expected')
CIwidth

width of the median survival estimates, default is 95%
conf.type

type of confidence interval see survfit for details. Default is 'log'.
caption

table caption
tableOnly

should a dataframe or a formatted object be returned
fontsize

PDF/HTML output only, manually set the table fontsize

Value

A character vector of the survival table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

survdiff

Examples

#' # Differences between sex
data("pembrolizumab")
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs='sex',digits=1)

# Differences between sex, stratified by cohort
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs='sex',strata='cohort',digits=1)
# Differences between sex/cohort groups
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs=c('sex','cohort'),digits=1)

Summarise survival data by group

Description

Displays event counts, median survival time and survival rates at specified times points for the entire cohort and by group. The logrank test of differences in survival curves is displayed.

Usage

rm_survsum(
  data,
  time,
  status,
  group = NULL,
  survtimes = NULL,
  survtimeunit,
  survtimesLbls = NULL,
  CIwidth = 0.95,
  unformattedp = FALSE,
  conf.type = "log",
  na.action = "na.omit",
  showCounts = TRUE,
  showLogrank = TRUE,
  eventProb = FALSE,
  digits = getOption("reportRmd.digits", 2),
  caption = NULL,
  tableOnly = FALSE,
  fontsize
)

Arguments

data

data frame containing survival data
time

string indicating survival time variable
status

string indicating event status variable
group

string or character vector indicating the variable(s) to group observations by. If this is left as NULL (the default) then summaries are provided for the entire cohort.
survtimes

numeric vector specifying when survival probabilities should be calculated.
survtimeunit

unit of time to suffix to the time column label if survival probabilities are requested, should be plural
survtimesLbls

if supplied, a vector the same length as survtimes with descriptions (useful for displaying years with data provided in months)
CIwidth

width of the survival probabilities, default is 95%
unformattedp

boolean indicating if you would like the p-value to be returned unformatted (ie not rounded or prefixed with '<'). Should be used in conjunction with the digits argument.
conf.type

type of confidence interval see survfit for details. Default is 'log'.
na.action

default is to omit missing values, but can be set to throw and error using na.action='na.fail'
showCounts

boolean indicating if the at risk, events and censored columns should be output; default is TRUE
showLogrank

boolean indicating if the log-rank test statistic and p-value should be output; default is TRUE
eventProb

boolean indicating if event probabilities, rather than survival probabilities, should be displayed; default is FALSE
digits

the number of digits in the survival rate, default is 2, unless the reportRmd.digits option is set
caption

table caption for markdown output
tableOnly

should a dataframe or a formatted object be returned
fontsize

PDF/HTML output only, manually set the table fontsize

Details

This summary table is supplied for simple group comparisons only. To examine differences in groups with stratification see rm_survdiff. To summarise differences in survival rates controlling for covariates see rm_survtime.

Value

A character vector of the survival table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

survfit

Examples

# Simple median survival table
data("pembrolizumab")
rm_survsum(data=pembrolizumab,time='os_time',status='os_status')

# Survival table with yearly survival rates
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
survtimes=c(12,24),survtimesLbls=1:2, survtimeunit='yr')

#Median survival by group
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',group='sex')

# Survival Summary by cohort, displayed in years
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
group="cohort",survtimes=seq(12,72,12),
survtimesLbls=seq(1,6,1),
survtimeunit='years')

# Survival Summary by Sex and ctDNA group
rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
group=c('sex','change_ctdna_group'),survtimes=c(12,24),survtimeunit='mo')

Display survival rates and events for specified times

Description

This is a wrapper for the survfit function to output a tidy display for reporting. Either Kaplan Meier or Cox Proportional Hazards models may be used to estimate the survival probabilities.

Usage

rm_survtime(
  data,
  time,
  status,
  covs = NULL,
  strata = NULL,
  type = "KM",
  survtimes,
  survtimeunit,
  strata.prefix = NULL,
  survtimesLbls = NULL,
  showCols = c("At Risk", "Events", "Censored"),
  CIwidth = 0.95,
  conf.type = "log",
  na.action = "na.omit",
  showCounts = TRUE,
  digits = getOption("reportRmd.digits", 2),
  caption = NULL,
  tableOnly = FALSE,
  fontsize
)

Arguments

data

data frame containing survival data
time

string indicating survival time variable
status

string indicating event status variable
covs

character vector with the names of variables to adjust for in coxph fit
strata

string indicating the variable to group observations by. If this is left as NULL (the default) then event counts and survival rates are provided for the entire cohort.
type

survival function, if no covs are specified defaults to Kaplan-Meier, otherwise the Cox PH model is fit. Use type='PH' to fit a Cox PH model with no covariates.
survtimes

numeric vector specifying when survival probabilities should be calculated.
survtimeunit

unit of time to suffix to the time column label if survival probabilities are requested, should be plural
strata.prefix

character value describing the grouping variable
survtimesLbls

if supplied, a vector the same length as survtimes with descriptions (useful for displaying years with data provided in months)
showCols

character vector specifying which of the optional columns to display, defaults to c('At Risk','Events','Censored')
CIwidth

width of the survival probabilities, default is 95%
conf.type

type of confidence interval see survfit for details. Default is 'log'.
na.action

default is to omit missing values, but can be set to throw and error using na.action='na.fail'
showCounts

boolean indicating if the at risk, events and censored columns should be output, default is TRUE
digits

the number of digits in the survival rate, default is 2.
caption

table caption for markdown output
tableOnly

should a dataframe or a formatted object be returned
fontsize

PDF/HTML output only, manually set the table fontsize

Details

If covariates are supplied then a Cox proportional hazards model is fit for the entire cohort and each strata. Otherwise the default is for Kaplan-Meier estimates. Setting type = 'PH' will force a proportional hazards model.

Value

A character vector of the survival table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

survfit

Examples

# Kaplan-Mieir survival probabilities with time displayed in years
data("pembrolizumab")
rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
strata="cohort",type='KM',survtimes=seq(12,72,12),
survtimesLbls=seq(1,6,1),
survtimeunit='years')

# Cox Proportional Hazards survivial probabilities
rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
strata="cohort",type='PH',survtimes=seq(12,72,12),survtimeunit='months')

# Cox Proportional Hazards survivial probabilities controlling for age
rm_survtime(data=pembrolizumab,time='os_time',status='os_status',
covs='age',strata="cohort",survtimes=seq(12,72,12),survtimeunit='months')

Combine univariate and multivariable regression tables

Description

This function will combine rm_uvsum and rm_mvsum outputs into a single table. The tableOnly argument must be set to TRUE when tables to be combined are created. The resulting table will be in the same order as the uvsum table and will contain the same columns as the uvsum and mvsum tables, but the p-values will be combined into a single column. There must be a variable overlapping between the uvsum and mvsum tables and all variables in the mvsum table must also appear in the uvsum table.

Usage

rm_uv_mv(
  uvsumTable,
  mvsumTable,
  covTitle = "",
  vif = FALSE,
  showN = FALSE,
  showEvent = FALSE,
  caption = NULL,
  tableOnly = FALSE,
  chunk_label,
  fontsize
)

Arguments

uvsumTable

Output from rm_uvsum, with tableOnly=TRUE
mvsumTable

Output from rm_mvsum, with tableOnly=TRUE
covTitle

character with the names of the covariate (predictor) column. The default is to leave this empty for output or, for table only output to use the column name 'Covariate'.
vif

boolean indicating if the variance inflation factor should be shown if present in the mvsumTable. Default is FALSE.
showN

boolean indicating if sample sizes should be displayed.
showEvent

boolean indicating if number of events (dichotomous outcomes) should be displayed.
caption

table caption
tableOnly

boolean indicating if unformatted table should be returned
chunk_label

only used if output is to Word to allow cross-referencing
fontsize

PDF/HTML output only, manually set the table fontsize

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

rm_uvsum,rm_mvsum

Examples

require(survival)
data("pembrolizumab")
uvTab <- rm_uvsum(response = c('os_time','os_status'),
covs=c('age','sex','baseline_ctdna','l_size','change_ctdna_group'),
data=pembrolizumab,tableOnly=TRUE)
mv_surv_fit <- coxph(Surv(os_time,os_status)~age+sex+
baseline_ctdna+l_size+change_ctdna_group, data=pembrolizumab)
uvTab <- rm_mvsum(mv_surv_fit)

#linear model
uvtab<-rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,tableOnly=TRUE)
lm_fit=lm(baseline_ctdna~age+sex+l_size+tmb,data=pembrolizumab)
mvtab<-rm_mvsum(lm_fit,tableOnly = TRUE)
rm_uv_mv(uvtab,mvtab,tableOnly=TRUE)

#logistic model
uvtab<-rm_uvsum(response = 'os_status',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,family = binomial,tableOnly=TRUE)
logis_fit<-glm(os_status~age+sex+l_size+pdl1+tmb,data = pembrolizumab,family = 'binomial')
mvtab<-rm_mvsum(logis_fit,tableOnly = TRUE)
rm_uv_mv(uvtab,mvtab,tableOnly=TRUE)

Output several univariate models nicely in a single table

Description

A table with the model parameters from running separate univariate models on each covariate. For factors with more than two levels a Global p-value is returned.

Usage

rm_uvsum(
  response,
  covs,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  caption = NULL,
  tableOnly = FALSE,
  removeInf = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  chunk_label,
  gee = FALSE,
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset,
  strata = 1,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  fontsize,
  forceWald
)

Arguments

response

string vector with name of response
covs

character vector with the names of columns to fit univariate models to
data

dataframe containing data
digits

number of digits to round estimates and CI to. Does not affect p-values.
covTitle

character with the names of the covariate (predictor) column. The default is to leave this empty for output or, for table only output to use the column name 'Covariate'.
caption

character containing table caption (default is no caption)
tableOnly

boolean indicating if unformatted table should be returned
removeInf

boolean indicating if infinite estimates should be removed from the table
p.adjust

p-adjustments to be performed (Global p-values only)
unformattedp

boolean indicating if you would like the p-value to be returned unformatted (ie not rounded or prefixed with '<'). Should be used in conjunction with the digits argument.
chunk_label

only used if output is to Word to allow cross-referencing
gee

boolean indicating if gee models should be fit to account for correlated observations. If TRUE then the id argument must specify the column in the data which indicates the correlated clusters.
id

character vector which identifies clusters. Only used for geeglm
corstr

character string specifying the correlation structure. Only used for geeglm. The following are permitted: '"independence"', '"exchangeable"', '"ar1"', '"unstructured"' and '"userdefined"'
family

description of the error distribution and link function to be used in the model. Only used for geeglm
type

string indicating the type of univariate model to fit. The function will try and guess what type you want based on your response. If you want to override this you can manually specify the type. Options include "linear", "logistic", "poisson",coxph", "crr", "boxcox", "ordinal", "geeglm"
offset

string specifying the offset term to be used for Poisson or negative binomial regression. Example: offset="log(follow_up)"
strata

character vector of covariates to stratify by. Only used for coxph and crr
nicenames

boolean indicating if you want to replace . and _ in strings with a space
showN

boolean indicating if you want to show sample sizes
showEvent

boolean indicating if you want to show number of events. Only available for logistic.
CIwidth

width of confidence interval, default is 0.95
reflevel

manual specification of the reference level. Only used for ordinal regression This will allow you to see which model is not fitting if the function throws an error
returnModels

boolean indicating if a list of fitted models should be returned. If this is TRUE then the models will be returned, but the output will be suppressed. In addition to the model elements a data element will be appended to each model so that the fitted data can be examined, if necessary. See Details
fontsize

PDF/HTML output only, manually set the table fontsize
forceWald

boolean indicating if Wald confidence intervals should be used instead of profile likelihood. This is not recommended, but can speed up computations. To use throughout a document use options(reportRmd.forceWald=TRUE)

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

uvsum,lm,glm,crr, coxph, lme,geeglm,polr

Examples

# Examples are for demonstration and are not meaningful
# Coxph model with 90% CI
data("pembrolizumab")
rm_uvsum(response = c('os_time','os_status'),
covs=c('age','sex','baseline_ctdna','l_size','change_ctdna_group'),
data=pembrolizumab,CIwidth=.9)

# Linear model with default 95% CI
rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab)

# Logistic model with default 95% CI
rm_uvsum(response = 'os_status',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,family = binomial)
# Poisson models returned as model list
mList <- rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab, returnModels=TRUE)
#'
# GEE on correlated outcomes
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)

Replace dollar signs with html for proper HTML output

Description

Replace dollar signs with html for proper HTML output

Usage

rmds(s)

Arguments

s

a character vector

Sanitizes strings to not break LaTeX

Description

Strings with special charaters will break LaTeX if returned 'asis' by knitr. This happens every time we use one of the main reportRx functions. We first sanitize our strings with this function to stop LaTeX from breaking.

Usage

sanitizestr(str)

Arguments

str

a vector of strings to sanitize

Set variable labels

Description

Assign variable labels to a data.frame from a lookup table.

Usage

set_labels(data, names_labels)

Arguments

data

data frame to be labelled
names_labels

data frame with column 1 containing variable names from data and column 2 containing variable labels. Other columns will be ignored.

Details

Useful if variable labels have been imported from a data dictionary. The first column in names_labels must contain the variable name and the second column the variable label. The column names are not used.

If no label is provided then the existing label will not be changed. To remove a label set the label to NA.

Examples

# create data frame with labels
lbls <- data.frame(c1=c('cohort','size_change'),
c2=c('Cancer cohort','Change in tumour size'))
# set labels and return labelled data frame
set_labels(ctDNA,lbls)

Set variable labels

Description

Set variable labels for a data frame using name-label pairs.

Usage

set_var_labels(data, ...)

Arguments

data

data frame containing variables to be labelled
...

Name-label pairs the name gives the name of the column in the output and the label is a character vector of length one.

Details

If no label is provided for a variable then the existing label will not be changed. To remove a label set the label to NA.

Examples

# set labels using name-label pairs
# and return labelled data frame
ctDNA |> set_var_labels(
   ctdna_status="detectable ctDNA",
  cohort="A cohort label")

Funky ctDNA data There is a weird factor with all one level, the cohort variable contains a cohort level (and Cohort A) and for one cohort all the size changes are missing

Description

Funky ctDNA data

There is a weird factor with all one level, the cohort variable contains a cohort level (and Cohort A) and for one cohort all the size changes are missing

Usage

testData

Format

A data frame with 270 rows and 6 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, cohort -for testing only

badfactor

for testing

ctdna_status

Change in ctDNA since baseline

time

Number of weeks on treatment

size_change

Percentage change in tumour measurement

Source

https://www.nature.com/articles/s43018-020-0096-5

Get univariate summary dataframe

Description

Returns a dataframe corresponding to a univariate regression table

Usage

uvsum(
  response,
  covs,
  data,
  digits = getOption("reportRmd.digits", 2),
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset = NULL,
  gee = FALSE,
  strata = 1,
  markup = TRUE,
  sanitize = TRUE,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  forceWald
)

Arguments

response

string vector with name of response
covs

character vector with the names of columns to fit univariate models to
data

dataframe containing data
digits

number of digits to round to
id

character vector which identifies clusters. Used for GEE and coxph models.
corstr

character string specifying the correlation structure. Only used for geeglm. The following are permitted: '"independence"', '"exchangeable"', '"ar1"', '"unstructured"' and '"userdefined"'
family

specify details of the model used. This argument does not need to be specified and should be used with caution. By default, gaussian errors are used for linear models, the binomial family with logit link is used for logistic regression and poisson with log link is used for poisson regression. This can be specified with the type argument, or will be inferred from the data type. See family. Ignored for ordinal and survival regression and if the type argument is not explicitly specified.
type

string indicating he type of univariate model to fit. The function will try and guess what type you want based on your response. If you want to override this you can manually specify the type. Options include "linear", "logistic", "poisson", coxph", "crr", "boxcox", "ordinal" and "negbin"
offset

string specifying the offset term to be used for Poisson or negative binomial regression. Example: offset="log(follow_up)"
gee

boolean indicating if gee models should be fit to account for correlated observations. If TRUE then the id argument must specify the column in the data which indicates the correlated clusters.
strata

character vector of covariates to stratify by. Only used for coxph and crr
markup

boolean indicating if you want latex markup
sanitize

boolean indicating if you want to sanitize all strings to not break LaTeX
nicenames

boolean indicating if you want to replace . and _ in strings with a space
showN

boolean indicating if you want to show sample sizes
showEvent

boolean indicating if you want to show number of events. Only available for logistic.
CIwidth

width of confidence interval, default is 0.95
reflevel

manual specification of the reference level. Only used for ordinal. This may allow you to debug if the function throws an error.
returnModels

boolean indicating if a list of fitted models should be returned.
forceWald

boolean indicating if Wald confidence intervals should be used instead of profile likelihood. This is not recommended, but can speed up computations. To use throughout a document use options(reportRmd.forceWald=TRUE)

Details

Univariate summaries for a number of covariates, the type of model can be specified. If unspecified the function will guess the appropriate model based on the response variable.

Confidence intervals are extracted using confint where possible. Otherwise Student t distribution is used for linear models and the Normal distribution is used for proportions.

returnModels can be used to return a list of the univariate models, which will be the same length as covs. The data used to run each model will include all cases with observations on the response and covariate. For gee models the data are re-ordered so that the ids appear sequentially and proper estimates are given.

See Also

lm,glm,crr,coxph, lme,geeglm,polr,glm.nb