Package 'reportRmd'

Description

Add censor marks to KM plot

Usage

add_censor_marks(p, df, censor.size = 0.5, censor.stroke = 1.5, shape = "|")

Arguments

Description

Add CIF Hazard Ratios to Strata Labels

Usage

add_cif_hazard_ratios(
  stratalabs,
  data,
  response,
  cov,
  plot.event = 1,
  HR = FALSE,
  HR_pval = FALSE,
  HR.digits = 2,
  HR.pval.digits = 3
)

Arguments

Value

Updated strata labels

Description

Add confidence bands to plot

Usage

add_confidence_bands(p, df, type, event = "col", plot.event = 1)

Arguments

Description

Add hazard ratios to strata labels

Usage

add_km_hazard_ratios(
  stratalabs,
  data,
  response,
  cov,
  type,
  plot.event = 1,
  HR = FALSE,
  HR_pval = FALSE,
  HR.digits = 2,
  HR.pval.digits = 3
)

Arguments

Description

Add median survival lines to plot

Usage

add_median_lines(p, median_vals, median.lsize = 1)

Arguments

Description

Add median survival text to plot

Usage

add_median_text(
  p,
  type,
  multiple_lines,
  median_txt,
  median.pos = NULL,
  times,
  ylim,
  median.size = 3,
  plot.event = 1,
  eventlabs = NULL
)

Arguments

Description

Add survival at set time lines to plot

Usage

add_set_time_lines(p, set.surv, set.lsize = 1)

Arguments

Description

Add survival at set time text to plot

Usage

add_set_time_text(
  p,
  type,
  multiple_lines,
  set.surv.text,
  set.pos = NULL,
  times,
  ylim,
  set.size = 3,
  plot.event = 1,
  eventlabs = NULL
)

Arguments

Description

Add statistical test results to plot

Usage

add_statistical_tests(
  p,
  type,
  multiple_lines,
  pval_result,
  pval.pos = NULL,
  times,
  xlim,
  ylim,
  psize = 3.5,
  pval.digits = 3,
  plot.event = 1,
  eventlabs = NULL
)

Arguments

Description

Apply colour and linetype scales

Usage

apply_scales_and_guides(
  p,
  col,
  linetype = NULL,
  stratalabs,
  eventlabs = NULL,
  multiple_lines,
  plot.event = 1,
  event = "col"
)

Arguments

Description

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

Usage

boxcoxfitRx(f, data, lambda = FALSE)

Arguments

Value

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

Description

Calculate median survival times and add to labels

Usage

calculate_and_add_median_times(
  sfit = NULL,
  fit = NULL,
  stratalabs,
  type = "KM",
  plot.event = 1,
  median.text = FALSE,
  median.CI = FALSE,
  median.digits = 3
)

Arguments

Description

Calculate survival/CIF at specific time points and add to labels

Usage

calculate_and_add_time_specific_estimates(
  sfit = NULL,
  fit = NULL,
  stratalabs,
  type = "KM",
  plot.event = 1,
  set.time.text = NULL,
  set.time = NULL,
  set.time.line = FALSE,
  set.time.CI = FALSE,
  set.time.digits = 3
)

Arguments

Description

Calculate Median Time to Event for CIF

Usage

calculate_cif_median(fit, event_name)

Arguments

Value

Median time to event

Description

Calculate CIF Estimates at Specific Time Points

Usage

calculate_cif_timepoints(
  fit,
  plot.event,
  set.time,
  set.time.CI = FALSE,
  set.time.digits = 3,
  multiple_lines = FALSE
)

Arguments

Value

Data frame with time-specific estimates

Description

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

Usage

clear_labels(data)

Arguments

Details

To change or remove individual labels use set_labels or set_var_labels

Examples

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

Description

Returns a dataframe corresponding to a descriptive table.

Usage

covsum(
  data,
  covs,
  maincov = NULL,
  digits = 1,
  numobs = NULL,
  markup = FALSE,
  sanitize = FALSE,
  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"),
  B = 1000,
  seed = 1
)

Arguments

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

Description

Create base ggplot for survival curves

Usage

create_base_plot(
  df,
  type,
  xlab = "Time",
  ylab = "Survival Probability",
  multiple_lines,
  plot.event = 1,
  event = "col",
  lsize = 0.5,
  fsize,
  col,
  linetype = NULL,
  legend.pos = "bottom",
  legend.title = NULL,
  times,
  ylim = c(0, 1),
  xlim = NULL,
  main = NULL
)

Arguments

Description

Create CIF Plotting Data Frame

Usage

create_cif_dataframe(
  fit,
  gsep,
  plot.event,
  stratalabs,
  conf.type = "log",
  flip.CIF = FALSE,
  eventlabs = NULL,
  cov = NULL,
  data = NULL
)

Arguments

Value

Data frame for plotting

Description

Create Survival Fit for Risk Table in CIF

Usage

create_cif_risk_table_sfit(data, response, cov = NULL)

Arguments

Value

Survival fit object for risk table

Description

Create plotting dataframe for KM curves

Usage

create_km_dataframe(sfit, stratalabs, conf.curves = FALSE, conf.type = "log")

Arguments

Description

Create risk table for survival plot

Usage

create_risk_table(
  sfit,
  times,
  xlim,
  stratalabs,
  stratalabs.table = NULL,
  strataname.table = "",
  Numbers_at_risk_text = "At Risk",
  multiple_lines = TRUE,
  col = NULL,
  fsize = 12,
  nsize = 3
)

Arguments

Description

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

Usage

crrRx(f, data)

Arguments

Value

a competing risk model with the call appended to the list

See Also

cmprsk::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)

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

data('ctDNA')

Format

A data frame with 270 rows and 5 variables:

id

Patient ID

cohort

Study Cohort

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

Description

Retrieve columns number from spreadsheet columns specified as unquoted letters

Usage

excelCol(...)

Arguments

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)

Description

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

Usage

excelColLetters(columnIndices)

Arguments

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)

Description

Extract Gray's test results from CIF fit

Usage

extract_grays_test(fit, plot.event = 1)

Arguments

Description

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

Usage

extract_labels(data, sep = "_")

Arguments

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
data("ctDNA")
ctDNA <- ctDNA |> set_var_labels(
   ctdna_status="detectable ctDNA",
  cohort="A cohort label")
# Extract labels
extract_labels(ctDNA)

Description

The function automatically detects the current R script file (works best in RStudio), parses the code to identify function calls, determines which packages they belong to, and creates a summary of all non-base R packages used in the script. It handles both namespace-qualified function calls (e.g., dplyr::filter) and regular function calls, while filtering out base R functions and control structures.

Usage

extract_package_details(ignore_comments = TRUE)

Arguments

Details

This function analyses the current file (an R script, Rmd or qmd file) to extract information about all functions called within the code, identifies their associated packages, and returns a summary of packages used with version and citation information.

Value

A data frame with the following columns:

package_name

Character. Name of the package

functions_called

Character. Comma-separated list of functions called from this package

package_version

Character. Version number of the installed package

package_citation

Character. Formatted citation for the package

Note

• Works best when run from RStudio with an active source file

• Requires that referenced packages are already loaded/installed

• Will not detect functions called through indirect methods (e.g., do.call())

See Also

getAnywhere, packageVersion, citation

Examples

## Not run: 
# Run this function from within an R script to analyze its dependencies
package_info <- extract_package_details()

# Include functions from commented code
package_info_all <- extract_package_details(ignore_comments = FALSE)
print(package_info)

## End(Not run)

Description

Fit Competing Risks Model

Usage

fit_cif_model(data, response, cov = NULL)

Arguments

Value

List containing fit object and group separator

Description

Fit Kaplan-Meier survival curves

Usage

fit_km_model(data, response, cov = NULL, conf.type = "log")

Arguments

Description

#' @description Deprecated: Please use forestplotMV() instead.

Usage

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

Arguments

Details

This function will be removed in a future version.

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.

Value

a plot object

Description

This function creates forest plots from fitted regression models, with optional inclusion of unadjusted estimates. It uses m_summary for robust data extraction and properly handles factor level ordering and reference levels.

Usage

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

Arguments

Value

a ggplot object

Examples

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

# Adjusted only
forestplotMV(glm_fit, data = pembrolizumab)

# Both adjusted and unadjusted
forestplotMV(glm_fit, data = pembrolizumab, include_unadjusted = TRUE)

Description

This function creates forest plots from univariable regression models. For new code, consider using forestplotMV() which can handle both adjusted and unadjusted estimates.

Usage

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

Arguments

Value

a ggplot object

Examples

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

Description

This function is deprecated. Please use forestplotMV() with include_unadjusted = TRUE instead.

Usage

forestplotUVMV(UVmodel, MVmodel, ...)

Arguments

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

Details

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

Description

ggkmcif() was deprecated in version 0.1.2 and will be removed in a future version.

Usage

ggkmcif_paste(list_gg)

Arguments

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,
  returns = FALSE,
  ...
)

Arguments

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")

Description

This section documents the additional parameters for ggkmcif2.

Usage

ggkmcif2Parameters(
  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 = 2,
  censor.stroke = 1.5,
  censor.symbol = "|",
  fsize,
  nsize = 3,
  lsize = 0.7,
  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,
  legend.title = strataname,
  pval.pos = NULL,
  event = c("col", "linetype"),
  flip.CIF = FALSE,
  cut = NULL,
  eventlabs = NULL,
  event.name = NULL,
  Numbers_at_risk_text = "At risk",
  tbl.height = NULL,
  HR.digits = 2,
  HR.pval.digits = 3,
  pval.digits = 3,
  median.digits = 3,
  set.time.digits = 3,
  print.n.missing = TRUE,
  returns = FALSE
)

Arguments

Description

Wraps strings in HTML bold formatting using inline CSS.

Usage

hbld(strings)

Arguments

Value

Vector of strings wrapped in HTML bold span

Description

Formats p-values specifically for display in plots (e.g., survival curves). Returns formatted string with "p = " or "p < " prefix.

Usage

lpvalue2(x, digits)

Arguments

Details

Formatting rules:

• p < 10^-digits: returns "p < threshold" (e.g., "p < 0.001")

• p >= threshold: returns "p = value" rounded to specified digits

Used by: ggkmcif2() for survival curve annotations in main.R and ggkmcif3.R

Value

Character string with "p = " or "p < " prefix

Examples

## Not run: 
lpvalue2(0.0001, 3)  # Returns: "p < 0.001"
lpvalue2(0.0456, 3)  # Returns: "p = 0.046"

## End(Not run)

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

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=TRUE) 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.

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

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')

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,
  header_above = NULL
)

Arguments

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 nicenames to FALSE

Value

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

Description

Survival data

Survival status and ctDNA levels for patients receiving pembrolizumab

Usage

data('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

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

os_time

Overall survival time in months

pfs_status

Progression free survival status

pfs_time

Progression free survival time in months

Source

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

Description

This function is designed to accompany rm_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,
  violin = FALSE,
  position = c("dodge", "stack", "fill"),
  use_labels = TRUE
)

Arguments

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)

Variable names are replaced by their labels if available, or by tidy versions if not. Set use_labels=FALSE to use the variable names.

Value

a list containing plots for each variable in covs

See Also

ggplot2::ggplot and ggpubr::ggarrange replace_plot_labels

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)

Description

Standardised reporting of binary prediction model performance. Returns a data frame of metrics (point estimate + confidence interval) and optionally renders it via reportRmd::outTable().

Usage

predsum(
  true,
  predicted,
  cutoff = NULL,
  positive = NULL,
  ci = c("clopper-pearson", "bootstrap"),
  ci_width = 0.95,
  ci_separate = FALSE,
  nboot = 2000,
  seed = 1,
  digits = 2,
  output = "epi",
  tableOnly = FALSE
)

Arguments

Details

Clopper-Pearson intervals are used for proportion-based metrics (prevalence, sensitivity, specificity, PPV, NPV, accuracy). AUC uses the Delong variance estimator on the logit scale. Brier score, F1 and AUC-PR have no closed-form interval and are obtained by percentile bootstrap; when ci = "clopper-pearson" this is done automatically and a message is emitted.

When predicted is dichotomous, AUC, AUC-PR, Brier and the cut-off row are omitted.

Value

A data frame of performance statistics.

References

Delong ER, Delong DM, Clarke-Pearson DL (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837-845.

Clopper CJ, Pearson ES (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26(4):404-413.

Examples

# Simulate true labels and model scores
set.seed(1)
n <- 300
true  <- rbinom(n, 1, 0.3)
score <- plogis(rnorm(n, mean = ifelse(true == 1, 0.9, -0.9)))

# Epidemiology-style summary from probability scores
predsum(true, score, cutoff = 0.5, tableOnly = TRUE)

# Data-science style (Recall, Precision, F1, Brier, AUC-PR)
predsum(true, score, cutoff = 0.5,
        output = "data-science", tableOnly = TRUE)

# Custom selection with separate CI columns
predsum(true, score, cutoff = 0.5,
        output = c("sensitivity", "specificity", "auc"),
        ci_separate = TRUE, tableOnly = TRUE)

# Dichotomous predictions: AUC, AUC-PR, Brier and cut-off are omitted
pred_class <- as.integer(score >= 0.5)
predsum(true, pred_class, tableOnly = TRUE)

Description

Process CIF Median Values

Usage

process_cif_medians(
  fit,
  plot.event,
  stratalabs,
  median.lines = FALSE,
  median.text = FALSE,
  median.digits = 3,
  multiple_lines = FALSE
)

Arguments

Value

List with updated stratalabs and median values

Description

Process CIF Time-Specific Estimates

Usage

process_cif_timepoints(
  fit,
  plot.event,
  stratalabs,
  set.time.text = NULL,
  set.time = NULL,
  set.time.line = FALSE,
  set.time.CI = FALSE,
  set.time.digits = 3,
  multiple_lines = FALSE
)

Arguments

Value

List with updated stratalabs and time-specific estimates

Description

Process covariate variable (factor conversion, numeric cutoffs)

Usage

process_covariate(data, cov, cut = NULL, stratalabs = NULL)

Arguments

Description

If the data stored in a ggplot object has variable labels then this will replace the variable names with the variable labels. If no labels are set then the variable names will be tidied and a nicer version used.

Usage

replace_plot_labels(plot)

Arguments

See Also

set_var_labels() for setting individual variable labels, set_labels() for setting variable labels using a data frame, extract_labels() for creating a data frame of all variable labels, clear_labels() for removing variable labels

Examples

## Not run: 
data("pembrolizumab")
p <- ggplot(pembrolizumab,aes(x=change_ctdna_group,y=baseline_ctdna)) +
geom_boxplot()
replace_plot_labels(p)
pembrolizumab <- set_var_labels(pembrolizumab,
change_ctdna_group="Change in ctDNA group")
p <- ggplot(pembrolizumab,aes(x=change_ctdna_group,y=baseline_ctdna)) +
geom_boxplot()
replace_plot_labels(p)
# Can also be used with a pipe, but expression needs to be wrapped in a brace
(ggplot(pembrolizumab,aes(x=change_ctdna_group,y=baseline_ctdna)) +
geom_boxplot()) |> replace_plot_labels()

## End(Not run)

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,
  nocensored = FALSE
)

Arguments

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')

#'
# Character event status
pbc$status_chr <- factor(pbc$status, levels = c(0, 1, 2),
  labels = c("Censored", "Transplant", "Death"))

rm_cifsum(data = pbc, time = "time", status = "status_chr", group = "trt",
  eventcode = "Death", cencode = "Censored", eventtimes = c(1825, 3650),
  eventtimeLbls = c(5, 10), eventtimeunit = "yr")

Description

Outputs a table formatted for pdf, word or html output with summary statistics

Usage

rm_compactsum(
  data,
  xvars,
  grp,
  use_mean,
  caption = NULL,
  tableOnly = FALSE,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  nicenames = TRUE,
  iqr = TRUE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  show.sumstats = FALSE,
  show.tests = FALSE,
  full = TRUE,
  percentage = "col",
  B = 1000,
  seed = 1
)

Arguments

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. For grouping variables with two levels, either t-tests (mean) or wilcoxon tests (median) will be used for numerical variables. Otherwise, ANOVA (mean) or Kruskal- Wallis tests will be used. The statistical test used can be displayed by specifying show.tests = TRUE. Statistical tests and effect sizes for grp and/ or xvars with less than 2 counts in any level will not be shown.

Effect sizes are calculated as Cohen d for between group differences if the variable is summarised with the mean, otherwise Wilcoxon R if summarised with a median. Cramer's V is used for categorical variables, omega is used for differences in means among more than two groups and epsilon for differences in medians among more than two groups. Confidence intervals are calculated using bootstrapping.

tidyselect can only be used for xvars and grp arguments. Additional arguments (digits, use_mean) must be passed in using characters if variable names are used.

Value

A character vector of the table source code, unless tableOnly = TRUE in which case a data frame is returned. The output has the following attribute:

• "description", which describes what is included in the output table and the type of statistical summary for each covariate. When applicable, the types of statistical tests used will be included. If effSize = TRUE, the effect sizes for each covariate will also be mentioned.

References

Smithson, M. (2002). Noncentral Confidence Intervals for Standardized Effect Sizes. (07/140 ed., Vol. 140). SAGE Publications. doi:10.4135/9781412983761.n4

Steiger, J. H. (2004). Beyond the F Test: Effect Size Confidence Intervals and Tests of Close Fit in the Analysis of Variance and Contrast Analysis. Psychological Methods, 9(2), 164–182. doi:10.1037/1082-989X.9.2.164

Kelley, T. L. (1935). An Unbiased Correlation Ratio Measure. Proceedings of the National Academy of Sciences - PNAS, 21(9), 554–559. doi:10.1073/pnas.21.9.554

Okada, K. (2013). Is Omega Squared Less Biased? A Comparison of Three Major Effect Size Indices in One-Way ANOVA. Behavior Research Methods, 40(2), 129-147.

Breslow, N. (1970). A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika, 57(3), 579-594.

FRITZ, C. O., MORRIS, P. E., & RICHLER, J. J. (2012). Effect Size Estimates: Current Use, Calculations, and Interpretation. Journal of Experimental Psychology. General, 141(1), 2–18. doi:10.1037/a0024338

Examples

data("pembrolizumab")
rm_compactsum(data = pembrolizumab, xvars = c("age",
"change_ctdna_group", "l_size", "pdl1"), grp = "sex", use_mean = "age",
digits = c("age" = 2, "l_size" = 3), digits.cat = 1, iqr = TRUE,
show.tests = TRUE)

# Other Examples (not run)
## Include the summary statistic in the variable column
#rm_compactsum(data = pembrolizumab, xvars = c("age",
#"change_ctdna_group"), grp = "sex", use_mean = "age", show.sumstats=TRUE)

## To show effect sizes
#rm_compactsum(data = pembrolizumab, xvars = c("age",
#"change_ctdna_group"), grp = "sex", use_mean = "age", digits = 2,
#effSize = TRUE, show.tests = TRUE)

## To return unformatted p-values
#rm_compactsum(data = pembrolizumab, xvars = c("l_size",
#"change_ctdna_group"), grp = "cohort", effSize = TRUE, unformattedp = TRUE)

## Using tidyselect
#pembrolizumab |> rm_compactsum(xvars = c(age, sex, pdl1), grp = cohort,
#effSize = TRUE)

Description

Returns a data frame corresponding to a descriptive table.

Usage

rm_covsum(
  data,
  covs = NULL,
  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,
  xvars = NULL,
  grp = NULL,
  B = 1000,
  seed = 1
)

Arguments

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.

A newer version of this function is rm_compactsum which is more flexible and displays fewer rows of output.

Tidyselect can be used for covs, maincov, xvars, and grp arguments, allowing bare column names (e.g., c(age, sex)) in addition to character strings (e.g., c("age", "sex")).

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, 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

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,
  whichp = c("levels", "global", "both"),
  caption = NULL,
  tableOnly = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  nicenames = TRUE,
  include_unadjusted = FALSE,
  chunk_label,
  fontsize
)

Arguments

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 lmer models (lme4), if the lmerTest package is installed, Satterthwaite-based p-values and F-test global p-values are used; otherwise Wald z-based p-values and chi-squared LRT global p-values are returned. For glmer models (lme4), Wald z-based p-values are used with chi-squared LRT global p-values. Estimates are exponentiated for binomial (OR) and poisson/negative binomial (RR) families. 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=TRUE, default) then a generalised VIF will be calculated in the same manner as the car package.

As of version 0.1.1 if global p-values are requested they will be included in the p-value column.

As of R 4.4.0 profile likelihood confidence intervals will be calculated automatically and there is no longer an option to force Wald tests.

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)

# lmer (lme4 mixed effects model) - single random intercept

if (require(lme4)){
lmer_fit <- lme4::lmer(age ~ sex + pdl1 + (1|cohort), data = pembrolizumab)
rm_mvsum(lmer_fit)
}


# lmer with multiple random effects and global p-values

if (require(lme4) && require(geepack)){
data(dietox, package = "geepack")
dietox$Cu <- as.factor(dietox$Cu)
lmer_fit2 <- lme4::lmer(Weight ~ Cu + Time + (1|Pig) + (1|Litter), data = dietox)
rm_mvsum(lmer_fit2, whichp = "both")
}


# glmer (binomial mixed effects model) - odds ratios

if (require(lme4)){
data(cbpp, package = "lme4")
glmer_fit <- lme4::glmer(cbind(incidence, size - incidence) ~ period + (1|herd),
  data = cbpp, family = binomial)
rm_mvsum(glmer_fit)
}


# glmer.nb (negative binomial mixed effects model) - rate ratios

if (require(lme4) && require(geepack)){
data(dietox, package = "geepack")
dietox$Cu <- as.factor(dietox$Cu)
nb_fit <- lme4::glmer.nb(Weight ~ Cu + Time + (1|Pig), data = dietox)
rm_mvsum(nb_fit, whichp = "both")
}

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,
  unformattedp = FALSE
)

Arguments

Value

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

See Also

survival::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)

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 = NULL,
  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,
  add.reverse.KM = FALSE,
  fontsize = NULL,
  cencode = 0,
  eventcode = 1,
  digits.reverse.KM = 1
)

Arguments

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

survival::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')


# Character event status and reverse Kaplan-Meier median follow-up
pembrolizumab$status_chr <- ifelse(pembrolizumab$os_status == 1, "Death",
  "Censored")
rm_survsum(data = pembrolizumab, time = "os_time", status = "status_chr",
  group = "sex", cencode = "Censored", eventcode = "Death",
  survtimes = c(12, 24), survtimeunit = "mo", add.reverse.KM = TRUE)

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

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

survival::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')

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

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)

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,
  whichp = c("levels", "global", "both"),
  chunk_label,
  gee = FALSE,
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset = NULL,
  strata = 1,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  fontsize,
  forceWald = FALSE
)

Arguments

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. For coxph models fit with clustering (id specified) a robust Wald global p-value is returned; otherwise a LRT is used. For GEE and CRR models Wald global p-values are returned.

As of version 0.1.1 if global p-values are requested they will be included in the p-value column.

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.

tidyselect can only be used for response and covs variables. Additional arguments must be passed in using characters

Value

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

See Also

lm,glm, cmprsk::crr, survival::coxph, nlme::lme, geepack::geeglm, MASS::glm.nb

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)

# Using tidyselect
pembrolizumab |> rm_uvsum(response = sex,
covs = c(age, cohort))

Description

This function accepts the output of a aa call to knitr::kable or reportRmd::outTable and, if the output format is html, will produce a scrollable table. Otherwise a regular table will be output for pandoc/latex

Usage

scrolling_table(knitrTable, pixelHeight = 500)

Arguments

Examples

data("pembrolizumab")
tab <- rm_covsum(data=pembrolizumab,maincov = 'change_ctdna_group',
covs=c('age','cohort','sex','pdl1','tmb','l_size'),full=FALSE)
scrolling_table(tab,pixelHeight=300)

Description

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

Usage

set_labels(data, names_labels)

Arguments

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.

See Also

set_var_labels() for setting individual variable labels, extract_labels() for creating a data frame of all variable labels, clear_labels() for removing variable labels

Examples

data("ctDNA")
# 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)

Description

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

Usage

set_var_labels(data, ...)

Arguments

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.

See Also

set_labels() for setting variable labels using a data frame, extract_labels() for creating a data frame of all variable labels, clear_labels() for removing variable labels

Examples

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

Description

Validate and prepare input data

Usage

validate_and_prepare_data(data, response, cov = NULL, print.n.missing = TRUE)

Arguments