--- title: "reportRmd Package" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{reportRmd Package} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} library(reportRmd) # knitr::opts_chunk$set(message = FALSE, warning = FALSE,dev="cairo_pdf") knitr::opts_chunk$set(message = FALSE, warning = FALSE) ``` # Overview reportRmd is a package designed to facilitate the reporting of common statistical outputs easily in RMarkdown documents. The package supports pdf, html and word output without any changes to the body of the report. The main features are Table 1 style summaries, combining multiple univariate regression models into a single table, tidy multivariable model output and combining univariate and multivariable regressions into a single table. Single table summaries of median survival times and survival probabilities are also provided. A highly customisable survival curve function, based on ggplot2 can be used to create publication-quality plots. Visualisation plots are also available for bivariate relationships and logistic regression models. A word of caution: The reportRmd package is designed to facilitate statistical reporting and does not provide any checks regarding the suitability of the models fit. \newpage # Summary statistics Basic summary statistics ```{r} data("pembrolizumab") rm_covsum(data=pembrolizumab, covs=c('age','sex')) ``` Set `IQR = T` for interquartile range instead of Min/Max ```{r} rm_covsum(data=pembrolizumab, covs=c('age','sex'),IQR=TRUE) ``` Or `all.stats=T` for both ```{r} rm_covsum(data=pembrolizumab, covs=c('age','sex'),all.stats = TRUE) ``` ## Summaries By Group This will produce summary statistics by Sex ```{r} rm_covsum(data=pembrolizumab, maincov = 'sex', covs=c('age','pdl1','change_ctdna_group')) ``` ## Showing Statistical Tests To indicate which statistical test was used use `show.tests=TRUE` ```{r} rm_covsum(data=pembrolizumab, maincov = 'sex', covs=c('age','pdl1','change_ctdna_group'), show.tests=TRUE) ``` ## Including Effect Sizes Effect sizes can be added with `effSize = TRUE`. Effect size measures include the Wilcoxon r for Wilcoxon rank-sum test, Cohen's d for t-test, Omega for ANOVA, Epsilon for Kruskal Wallis test, and Cramer's V for categorical variables. ```{r} rm_covsum(data=pembrolizumab, maincov = 'sex', covs=c('age','change_ctdna_group'), effSize=TRUE) ``` ## Parametric vs Non-parametric Comparisons Group comparisons are non-parametric by default, specify `testcont='ANOVA'` for t-tests/ANOVA ```{r} rm_covsum(data=pembrolizumab, maincov = 'sex', covs=c('age','pdl1'), testcont='ANOVA', show.tests=TRUE, effSize=TRUE) ``` ## Row vs Column Summaries The default is to indicate percentages by columns (ie. percentages within columns add to 100) ```{r} rm_covsum(data=pembrolizumab, maincov = 'sex', covs=c('cohort'), pvalue = FALSE) ``` But you can also specify to show by row instead ```{r} rm_covsum(data=pembrolizumab, maincov = 'sex', covs=c('cohort'), pvalue = FALSE, percentage='row') ``` \newpage # Compact summary statistics The `rm_compactsum` function is similar to the `rm_covsum` function, but condenses the table by excluding the reference rows and missing data rows and creating instead a missing data column. The arguments largely function the same way with the exception that displaying the iqr is the default and the function provides more flexibility. Future versions will align the two functions. ```{r} rm_compactsum(data = pembrolizumab, xvars = c("change_ctdna_group", "l_size")) ``` ## Using Mean Summary Statistic Instead of Median The default summary statistic for numerical variables is median. To specify which numerical variables should have mean displayed instead, change the `use_mean` argument. Unspecified xvars will use the default median ```{r} rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', use_mean = c("pdl1"), effSize = T, show.tests = T) ``` ## Custom Digits Argument The digits and digits.cat arguments can be changed to a custom numerical value. The default digits is 1, the default digits.cat is 0 ```{r} rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','change_ctdna_group'), grp = 'sex', use_mean = c("pdl1"), digits = 2, digits.cat = 1, effSize = T, show.tests = T) ``` To specify custom digit arguments for different numerical variables, change the `digits` argument. Unspecified variables will use the default value. ```{r} rm_compactsum(data=pembrolizumab, xvars=c('age','pdl1','l_size'), grp = 'sex', digits = c("age" = 3, "l_size" = 2), effSize = T, show.tests = T) ``` ## Viewing the Self-Generated Description To view the self-generated description for the summary table: ```{r, results = 'asis'} summary_tab <- rm_compactsum(data=pembrolizumab, xvars=c('change_ctdna_group','orr', 'age'), grp = 'cohort', effSize = T, show.tests = T) cat(attr(summary_tab, "description")) ``` ## Using Tidyselect Both `rm_covsum` and `rm_compactsum` support tidyselect syntax, allowing you to use bare column names instead of character strings. This also enables piping with `|>`: ```{r} pembrolizumab |> rm_compactsum(xvars = c(age, pdl1, change_ctdna_group), grp = sex) ``` The `xvars`/`grp` aliases can also be used with `rm_covsum` for a consistent interface: ```{r} pembrolizumab |> rm_covsum(xvars = c(age, pdl1, change_ctdna_group), grp = sex) ``` ## Adding Header Rows Header rows can be added to any table using the `header_above` argument in `outTable()`. This works for HTML, PDF and Word output. The argument takes a named vector where the names are the header labels and the values are the number of columns each label spans. ```{r} tab <- rm_covsum(data = pembrolizumab, maincov = cohort, covs = c(age, pdl1, sex), full = FALSE, pvalue = FALSE, tableOnly = TRUE) outTable(tab, header_above = c(" " = 1, "Cohorts A-C" = 3, "Cohorts D,E" = 2)) ``` \newpage # Univariate regression Combining multiple univariate regression analyses into a single table. The function will try to determine the most appropriate model from the data. ```{r } rm_uvsum(data=pembrolizumab, response='orr', covs=c('age','pdl1','change_ctdna_group')) ``` ## Simple Linear Regression If the response is continuous linear regression is the default. Using `type = 'linear'` will ensure linear regression. ```{r } rm_uvsum(data=pembrolizumab, response='l_size', covs=c('age','cohort')) ``` ## Logistic Regression If the response is binomial, logistic regression will be run (or specified with `type = 'logistic'`). ```{r } rm_uvsum(data=pembrolizumab, response='orr', covs=c('age','cohort')) ``` ## Poisson Regression If the response is integer, poisson regression will be run (or specified with `type = 'poisson'`). ```{r } pembrolizumab$Counts <- rpois(nrow(pembrolizumab),lambda = 3) rm_uvsum(data=pembrolizumab, response='Counts',covs=c('age','cohort')) ``` offset terms can be specified as well, but must correspond to a variable in the data set ```{r} pembrolizumab$length_followup <- rnorm(nrow(pembrolizumab),mean = 72,sd=3) pembrolizumab$log_length_followup <- log(pembrolizumab$length_followup) rm_uvsum(data=pembrolizumab, response='Counts',covs=c('age','cohort'), offset = "log_length_followup") ``` ## Negative Binomial Regression To run negative binomial regression instead specify `type = 'negbin'` ```{r } rm_uvsum(data=pembrolizumab, response='Counts', type='negbin', covs=c('age','cohort'), offset = "log_length_followup") ``` ## Survival Analysis If two response variables are specified and then survival analysis is run (specified with `type='coxph'`). ```{r } rm_uvsum(data=pembrolizumab, response=c('os_time','os_status'), covs=c('age','pdl1','change_ctdna_group'),whichp = "levels") ``` Clustered analyses can also be run ```{r } data("diabetic", package = "survival") rm_uvsum(data=diabetic, response=c('time','status'), covs=c('trt','laser','age','eye'),id='id') ``` ```{r, echo=F,eval=F} # # just a check that this works # cox_cl <- survival::coxph(survival::Surv(time,status)~trt+laser+age, # cluster = id,data = diabetic) # rm_mvsum(cox_cl) ``` ## Competing Risk Competing risk models need to be explicitly specified using `type='crr'`. ```{r } rm_uvsum(data=pembrolizumab, response=c('os_time','os_status'), covs=c('age','pdl1','change_ctdna_group'), type='crr') ``` ## GEE Models Correlated observations can be handled using GEE ```{r} 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) ``` ## MICE output Outputs from multiple imputations can be summarised as follows (not run): ```{r, echo=T,eval=F} miss_data <- pembrolizumab miss_data$age[sample(1:nrow(pembrolizumab),50,replace=F)] <- NA miss_data$cohort[sample(1:nrow(pembrolizumab),50,replace=F)] <- NA require(mice) ini <- mice::mice(miss_data, maxit = 0) pred_matrix <- ini$predictorMatrix pred_matrix[1, ] <- 0 pred_matrix[, 1] <- 0 imp <- mice::mice(miss_data, m = 5, predictorMatrix = pred_matrix, printFlag = FALSE) imputed_datasets <- purrr::map(1:5, \(m) mice::complete(imp, m)) fits <- lapply(imputed_datasets, function(dat) { glm(orr ~ age+cohort, data = dat,family='binomial') }) model <- mice::as.mira(fits) rm_mvsum(model) ``` ## Returning Model Objects If you want to check the underlying models, set `returnModels = TRUE` ```{r} rm_uvsum(response = 'orr', covs=c('age'), data=pembrolizumab,returnModels = TRUE) ``` The data analysed can be examined by interrogating the data object appended to each model ```{r} mList <- rm_uvsum(response = 'orr', covs=c('age'), data=pembrolizumab,returnModels = TRUE) head(mList$age$data) ``` ## Adjusting p-values Multiple comparisons can be controlled for with the p.adjust argument, which accepts any of the options from the `p.adjust` function. ```{r} rm_uvsum(response = 'orr', covs=c('age','sex','pdl1'), data=pembrolizumab,p.adjust = 'fdr') ``` **Note:** The raw p-value column is suppressed when there are categorical variables with >2 levels, to prevent three columns of p-values from appearing. \newpage # Multivariable analysis To create a nice display for multivariable models the multivariable model first needs to be fit. By default, the variance inflation factor will be shown to check for multicollinearity. To suppress this column set `vif=FALSE`. Note: variance inflation factors are not computed (yet) for multilevel or GEE models. ```{r} glm_fit <- glm(orr~change_ctdna_group+pdl1+age, family='binomial', data = pembrolizumab) rm_mvsum(glm_fit, showN = TRUE, vif=TRUE) ``` p-values can be adjusted for multiple comparisons using any of the options available in the `p.adjust` function. This argument is also available for univariate models run with rm_uvsum. ```{r} rm_mvsum(glm_fit, showN = TRUE, vif=TRUE,p.adjust = 'holm') ``` ## Including Unadjusted Estimates As of version 0.1.3, you can include univariate (unadjusted) estimates alongside the multivariable estimates in a single table by setting `include_unadjusted = TRUE`. This provides a convenient way to compare unadjusted and adjusted estimates side-by-side. ```{r} glm_fit <- glm(orr~change_ctdna_group+pdl1+age, family='binomial', data = pembrolizumab) rm_mvsum(glm_fit, include_unadjusted = TRUE, showN = TRUE, vif = TRUE) ``` The data are taken from the model itself, so the `data` argument is not needed. This includes Cox proportional hazards models, where the response is a `Surv()` object: ```{r} require(survival) cox_fit <- coxph(Surv(os_time, os_status) ~ age + sex + baseline_ctdna, data = pembrolizumab) rm_mvsum(cox_fit, include_unadjusted = TRUE) ``` Transformed terms are summarised on the scale they were fit on, so the unadjusted estimate for `log(l_size)` below is the slope from `glm(orr ~ log(l_size))`, not from `glm(orr ~ l_size)`: ```{r} rm_mvsum(glm(orr ~ log(l_size) + sex, family = 'binomial', data = pembrolizumab), include_unadjusted = TRUE) ``` Two points are worth knowing when reading these tables. The univariate models are fit on the same complete-case sample as the multivariable model, so the two sets of estimates are directly comparable and the `N` column applies to both. This differs from calling `rm_uvsum()` yourself, which uses all rows available for each covariate. A covariate that appears only inside an interaction term has no adjusted estimate of its own. Such covariates are appended to the end of the table with an unadjusted estimate and a blank adjusted column, rather than being dropped: ```{r} rm_mvsum(glm(orr ~ pdl1 + age:sex, family = 'binomial', data = pembrolizumab), include_unadjusted = TRUE) ``` Terms that expand to a matrix, such as `poly(age, 2)`, can not be fit univariately; these are skipped with a warning and their unadjusted cells are left blank. # Combining univariate and multivariable models To display both models in a single table run the rm_uvsum and rm_mvsum functions with `tableOnly=TRUE` and combine. ```{r} uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr', covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE) glm_fit <- glm(orr~change_ctdna_group+pdl1, family='binomial', data = pembrolizumab) mvsumTable <- rm_mvsum(glm_fit, showN = TRUE,tableOnly = TRUE) rm_uv_mv(uvsumTable,mvsumTable) ``` **Note:** This can also be done with adjusted p-values, but when combined the raw p-values are dropped. ```{r} uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr', covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE,p.adjust='holm') glm_fit <- glm(orr~change_ctdna_group+pdl1, family='binomial', data = pembrolizumab) mvsumTable <- rm_mvsum(glm_fit,tableOnly = TRUE,p.adjust='holm') rm_uv_mv(uvsumTable,mvsumTable) ``` # Changing the output If you need to make changes to the tables, setting `tableOnly=TRUE` will return a data frame for any of the `rm_` functions. Changes can be made, and the table output using `outTable()` ```{r} mvsumTable <- rm_mvsum(glm_fit, showN = TRUE,tableOnly = TRUE) names(mvsumTable)[1] <-'Predictor' outTable(mvsumTable) ``` # Combining tables Tables can be nested with the `nestTable()` function ```{r} cohortA <- rm_uvsum(data=subset(pembrolizumab,cohort=='A'), response = 'pdl1', covs=c('age','sex'), tableOnly = T) cohortA$Cohort <- 'Cohort A' cohortE <- rm_uvsum(data=subset(pembrolizumab,cohort=='E'), response = 'pdl1', covs=c('age','sex'), tableOnly = T) cohortE$Cohort <- 'Cohort E' nestTable(rbind(cohortA,cohortE),head_col = 'Cohort',to_col = 'Covariate') ``` \newpage ## Scrollable Tables If you are rendering to html format you can use the `scrollTable` function to create a scrollable table. This is useful for tables with many rows. If the output format is not html then a regular table will be displayed. ```{r} long_table <- rm_compactsum(data=pembrolizumab,xvars = c(age,sex,cohort,pdl1,tmb,baseline_ctdna,change_ctdna_group,orr,cbr)) scrolling_table(long_table,pixelHeight = 300) ``` \newpage # Simple Survival Summaries Displaying survival probabilities at different times by sex using Kaplan Meier estimates ```{r} rm_survsum(data=pembrolizumab,time='os_time',status='os_status', group="sex",survtimes=seq(12,36,12),survtimeunit='months') ``` ## Including Median Follow-up The reverse Kaplan-Meier follow-up time can also be displayed. ```{r} rm_survsum(data=pembrolizumab,time='os_time',status='os_status', group="sex",survtimes=seq(12,24,12),survtimeunit='months', add.reverse.KM = T) ``` \newpage # Survival Times in Long Format Displaying survival probabilities at different times by sex using Cox PH estimates ```{r} rm_survtime(data=pembrolizumab,time='os_time',status='os_status', strata="sex",survtimes=c(12,24),survtimeunit='mo',type='PH') ``` ## Survival Times With Covariate Adjustments Displaying survival probabilities at different times by sex, adjusting for age using Cox PH estimates ```{r} rm_survtime(data=pembrolizumab,time='os_time',status='os_status', covs='age', strata="sex",survtimes=c(12,24),survtimeunit='mo',type='PH') ``` # Stratified Survival Summary To combine estimates across strata ```{r} rm_survdiff(data=pembrolizumab,time='os_time',status='os_status', covs='sex',strata='cohort',digits=1) ``` # Cumulative Incidence Function Summary For competing risk analysis, use `rm_cifsum()` to display cumulative incidence probabilities at specific time points. This requires the cmprsk package and a status variable coded with different event types. ```{r} # Using pbc data from survival package as example data(pbc, package = "survival") # Create competing risk status: 0=censored, 1=transplant, 2=death pbc$status_cr <- ifelse(pbc$status == 0, 0, pbc$status) rm_cifsum(data=pbc, time='time', status='status_cr', group='trt', eventcode=2, cencode=0, eventtimes=c(1000, 2000, 3000), eventtimeunit='days') ``` \newpage # Working with Labels New in version 0.1.0 is the ability to incorporate variable labels into summary tables (but not yet all plots). If variables contain a `label` attribute this will be displayed automatically, to disable this set `nicenames=F` Variable labels will be shown in the `nicenames` argument is set to `TRUE` (the default). Variable labels are set using the `label` attribute of individual variables (assigned using `reportRmd` or another package like `haven`). `reportRmd` supports the addition, removal and export of labels using the following functions: - `set_labels` will set labels for a data frame from a lookup table - `set_var_labels` allows you to set individual variable labels to a data frame - `clear_labels` removes all labels from a data frame - `export_labels` extracts variable labels from a data frame and returns a data frame of variable names and variable labels ## Worked Example Get some descriptive stats for the ctDNA data that comes with the package. The `nicenames` argument is TRUE by default so underscores are replaced by spaces ```{r} rm_covsum(data=ctDNA, covs=c('cohort','ctdna_status','size_change')) ``` ### set_labels {#sec-set_labels} If we have a lookup table of variable names and labels that we imported from a data dictionary we can set the variable labels for the data frame and these will be used in the `rm_` functions ```{r} ctDNA_names <- data.frame(var=names(ctDNA), label=c('Patient ID', 'Study Cohort', 'Change in ctDNA since baseline', 'Number of weeks on treatment', 'Percentage change in tumour measurement')) ctDNA <- set_labels(ctDNA,ctDNA_names) rm_covsum(data=ctDNA, covs=c('cohort','ctdna_status','size_change')) ``` ### set_var_labels {#sec-set_var_labels} Individual labels can be changed with with the `set_var_labels` command ```{r} ctDNA <- set_var_labels(ctDNA, cohort="A new cohort label") rm_covsum(data=ctDNA, covs=c('cohort','ctdna_status','size_change')) ``` ### extract_labels {#sec-extract_labels} Extract the variable labels to a data frame ```{r} var_labels <- extract_labels(ctDNA) var_labels ``` \newpage ### replace_plot_labels {#sec-replace_plot_labels} This function will accept a ggplot plot and replace the variable names (x-axis, y-axis and legend) with the variable labels. This is useful for more professional looking plots. ```{r} library(ggplot2) p <- ggplot(data=ctDNA,aes(x=ctdna_status,y=size_change,colour=cohort))+ geom_point() replace_plot_labels(p) ``` ### clear_labels {#sec-clear_labels} Or clear them all ```{r} ctDNA <- clear_labels(ctDNA) ``` \newpage # Prediction Metrics `predsum()` summarises how well a set of predicted probabilities recovers a binary outcome. Supply the observed outcome, the predicted probabilities and the cut-off used to turn those probabilities into predicted classes. ```{r} d <- pembrolizumab[complete.cases(pembrolizumab[, c('change_ctdna_group', 'baseline_ctdna', 'age', 'l_size')]), ] fit <- glm(change_ctdna_group ~ baseline_ctdna + age + l_size, family = 'binomial', data = d) d$predicted <- predict(fit, type = 'response') predsum(true = d$change_ctdna_group, predicted = d$predicted, cutoff = 0.5) ``` The default output is the set of statistics usually reported in a clinical setting: prevalence, sensitivity, specificity, positive and negative predictive value, accuracy and AUC. The table also records the sample size, the number of events, the cut-off used, and the level of the outcome being treated as the event. That last row is worth reading. For a factor outcome the second level is taken as the event, so above the model is predicting *Increase from baseline*. If that is not what you intended, set `positive` explicitly: ```{r eval=FALSE} predsum(true = d$change_ctdna_group, predicted = d$predicted, cutoff = 0.5, positive = 'Decrease from baseline') ``` ## Choosing Which Statistics to Report The `output` argument selects statistics by name, and accepts several machine-learning measures that are not shown by default: ```{r} predsum(true = d$change_ctdna_group, predicted = d$predicted, cutoff = 0.5, output = c('accuracy', 'brier', 'f1', 'auc')) ``` Brier score, F1 and AUC-PR have no closed-form confidence interval, so `predsum()` obtains theirs by bootstrap. This is slower than the other statistics; `nboot` controls the number of replicates (default 2000). \newpage # Plotting Functions ## Plotting bivariate relationships These plots are designed for quick inspection of many variables, not for publication. This is the plotting version of rm_uvsum. As of 0.1.1 the variable names will be replaced by variable labels if they exist. ```{r, eval=FALSE,echo=TRUE} plotuv(data=pembrolizumab, response='orr', covs=c('age','cohort','pdl1','change_ctdna_group')) ``` ```{r, eval=FALSE,echo=FALSE} plotuv(data=pembrolizumab, response='orr', covs=c('age','cohort','pdl1','change_ctdna_group')) ggsave('images/plotuv.png',scale = 0.5,dpi = 300) ``` ![](images/plotuv.png){width=100%} The plotuv function can also be used without a response variable to display summary of variables ```{r, eval=FALSE,echo=TRUE} plotuv(data = pembrolizumab, covs=c('age','cohort','pdl1','change_ctdna_group'), showN = T) ``` ```{r, eval=FALSE,echo=FALSE} plotuv(data = pembrolizumab, covs=c('age','cohort','pdl1','change_ctdna_group'), showN = T) ggsave('images/plotuv_nores.png',scale = 0.5,dpi = 300) ``` ![](images/plotuv_nores.png){width=100%} \newpage ## Plotting survival curves Survival curves are now ggplot2-based, the older version, ggkmcif is deprecated from version 0.1.0 ```{r, eval=F,echo=T} ggkmcif2(response = c('os_time','os_status'), cov='cohort', data=pembrolizumab) ``` ```{r, eval=F,echo=FALSE} ggkmcif2(response = c('os_time','os_status'), cov='cohort', data=pembrolizumab) ggsave('images/ggkmcif.png',dpi=300,width = 5,height = 5) p <- ggkmcif2(response = c('os_time','os_status'), cov='cohort', data=pembrolizumab,returns = T) plot(p[[1]]) ggsave('images/ggkmcif_sm.png',scale = 0.5) ``` ![](images/ggkmcif.png){width=100%} \newpage ## Forest Plots Similar to `rm_uvsum` and `rm_mvsum`, forest plots can be created from univariate or multivariable models. forestplot2 is deprecated from version 0.1.0. Variable labels are not yet incorporated into the forest plots. This will default to a log scale, but can be set to linear using `logScale=FALSE` ```{r, eval=F,echo=T} forestplotUV(response="orr", covs=c("change_ctdna_group", "sex", "age", "l_size"), data=pembrolizumab, family='binomial') ``` ```{r, eval=F,echo=FALSE} forestplotUV(response="orr", covs=c("change_ctdna_group", "sex", "age", "l_size"), data=pembrolizumab, family='binomial') ggsave('images/forestuv.png', scale = 0.5) ``` ![](images/forestuv.png){width=100%} ### Multivariable Model Forest Plot ```{r, eval=F,echo=T} glm_fit <- glm(orr~pdl1+age, family='binomial', data = pembrolizumab) forestplotMV(glm_fit) ``` ```{r, eval=F,echo=FALSE} glm_fit <- glm(orr~pdl1+age, family='binomial', data = pembrolizumab) forestplotMV(glm_fit) ggsave('images/forestmv.png', scale = 0.5) ``` ![](images/forestmv.png){width=100%} ### Multivariable Forest Plot with Unadjusted Estimates As of version 0.1.3, you can include both unadjusted and adjusted estimates in a multivariable forest plot by setting `include_unadjusted = TRUE`: ```{r, eval=F,echo=T} glm_fit <- glm(orr~pdl1+age, family='binomial', data = pembrolizumab) forestplotMV(glm_fit, include_unadjusted = TRUE) ``` ```{r, eval=F,echo=FALSE} glm_fit <- glm(orr~pdl1+age, family='binomial', data = pembrolizumab) forestplotMV(glm_fit, include_unadjusted = TRUE) ggsave('images/forestmv_unadj.png', scale = 0.5,height = 1,width =4) ``` ![](images/forestmv_unadj.png){width=100%} # Miscellaneous Functions ## excelCol To identify the number of a column given the Excel column header ```{r} excelCol(G,AB,Az) ``` ## excelColLetters To identify the Excel header given a columns number ```{r} excelColLetters(c(7,28,52)) ``` ## extract_package_details This function will search the current script or markdown file for R packages and record the package name, functions called, the package version and the citation(s) for the package. By default, function calls and packages that only appear in commented code will be ignored, but they can be included by setting `ignore_comments` = FALSE ```{r, echo=T,eval=F} extract_package_details() |> outTable() ``` ```{r, echo=T,eval=F} extract_package_details(ignore_comments = FALSE) |> outTable() ``` | Package name | Functions called | Package version | Package citation | |-------------|------------------|-----------------|------------------| | reportRmd | rm_covsum, rm_compactsum, rm_uvsum, glm, rm_mvsum, Surv, rm_uv_mv, outTable, nestTable, scrolling_table, rm_survsum, rm_survtime, rm_survdiff, rm_cifsum, set_labels, set_var_labels, extract_labels, ggplot, aes, geom_point, replace_plot_labels, clear_labels, plotuv, ggsave, ggkmcif2, forestplotUV, forestplotMV, forestplotUVMV, excelCol, excelColLetters, citation | 0.1.3 | Avery L, Del Bel R, Espin-Garcia O, Lajkosz K, Ong C, Pittman T, Santiago A, Weiss J, Xu W (2026). *reportRmd: Tidy Presentation of Clinical Reporting*. R package version 0.1.3, . | | rmarkdown | run | 2.29 | Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2024). *rmarkdown: Dynamic Documents for R*. R package version 2.29, . Xie Y, Allaire J, Grolemund G (2018). *R Markdown: The Definitive Guide*. Chapman and Hall/CRC. Xie Y, Dervieux C, Riederer E (2020). *R Markdown Cookbook*. Chapman and Hall/CRC. | | survival | coxph | 3.8.3 | Therneau T (2024). *A Package for Survival Analysis in R*. R package version 3.8-3, . Therneau TM, Grambsch PM (2000). *Modeling Survival Data: Extending the Cox Model*. Springer. | | R | | 4.4.2 | R Core Team (2024). *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing, Vienna, Austria. . | \newpage # Reproducibility Two parts of the package draw random numbers, and both are seeded by default so that a report re-knitted next month produces the same table as the one already circulated. ## Monte Carlo p-values `rm_covsum()` and `rm_compactsum()` compare categorical variables with a chi-squared test, switching to Fisher's exact test when any *expected* cell count falls below five. On a large or sparse table the exact test can exceed its workspace, in which case both functions fall back to a Monte Carlo approximation and say so: ```{r eval=FALSE} Fisher's exact test could not be computed for site; using Monte Carlo simulation with B = 1000 and seed = 1. ``` `B` sets the number of replicates and `seed` fixes the random state for the duration of the call; the session's own random state is restored afterwards, so seeding here does not disturb anything else in your document. Setting `seed = NULL` restores the older behaviour in which the p-value varies between calls. ```{r eval=FALSE} rm_covsum(data = pembrolizumab, maincov = 'cohort', covs = c('age', 'sex'), B = 5000, seed = 2024) ``` When a Monte Carlo p-value has been used, `show.tests = TRUE` records the replicate count alongside the test name, so the table itself documents how the p-value was obtained. Because both functions now use the same expected-count rule, `rm_covsum()` and `rm_compactsum()` select the same test and report the same p-value for the same data. ## Bootstrap confidence intervals `predsum()` bootstraps the intervals for Brier score, F1 and AUC-PR. It takes the same `seed` argument, with the same default and the same restoration behaviour: ```{r eval=FALSE} predsum(true = d$change_ctdna_group, predicted = d$predicted, cutoff = 0.5, output = c('brier', 'f1'), nboot = 5000, seed = 2024) ``` \newpage # Options The following options can be set: - reportRmd.digits, default is 2. Sets the default number of digits in output functions. Does not affect p-values. - reportRmd.logScale, default is TRUE. The scale of the forest plots will default to log unless otherwise specified. Example: ```{r} rm_uvsum(response = 'baseline_ctdna', covs=c('age','sex','l_size','pdl1','tmb'), data=pembrolizumab) options('reportRmd.digits'=1) rm_uvsum(response = 'baseline_ctdna', covs=c('age','sex','l_size','pdl1','tmb'), data=pembrolizumab) ``` \newpage # PDF Output For pdf to be correctly generate when using survival curves it is recommended that the cairo format be used. This can be specified with the following command in the setup code chunk: `knitr::opts_chunk$set(message = FALSE, warning = FALSE,dev="cairo_pdf")` # Data Sets ## pembrolizumab Survival status and ctDNA levels for patients receiving pembrolizumab A data frame with 94 rows and 15 variables: - **id** Patient ID - **age** Age at study entry - **sex** Patient Sex - **cohort** Study Cohort: A = Squamous cell carcinoma of soft pallate, B = Triple negative breast cancer, C = Ovarian, high grade serous, D = Melanoma, E = Other Solid Tumor - **l_size** Target lesion size at baseline - **pdl1** PD L1 percent - **tmb** log of TMB - **baseline_ctdna** Baseline ctDNA - **change_ctdna_group** Did ctDNA increase or decrease from baseline to cycle 3 - **orr** Objective Response - **cbr** Clinical Beneficial Response - **os_status** Overall survival status, 0 = alive, 1 = deceased - **os_time** Overall survival time in months - **pfs_status** Progression free survival status, 0 = progression free, 1 = progressed - **pfs_time** Progression free survival time in months ## ctDNA Longitudinal changes in tumour size since baseline for patients by changes in ctDNA status (clearance, decrease or increase) since baseline. A data frame with 270 rows and 5 variables: - **id** Patient ID - **cohort** Study Cohort: A = Squamous cell carcinoma of soft pallate, B = Triple negative breast cancer, C = Ovarian, high grade serous, D = Melanoma, E = Other Solid Tumor - **ctdna_status** Change in ctDNA since baseline - **time** Number of weeks on treatment - **size_change** Percentage change in tumour measurement