Merge branch 'main' into 973_incidence_ctrl_params@main

Signed-off-by: Emily de la Rua <59304861+edelarua@users.noreply.github.com>

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: tern
 Title: Create Common TLGs Used in Clinical Trials
 Version: 0.8.3.9000
-Date: 2023-06-15
+Date: 2023-06-20
 Authors@R: c(
     person("Joe", "Zhu", , "joe.zhu@roche.com", role = c("aut", "cre")),
     person("Daniel", "Sabanés Bové", , "daniel.sabanes_bove@roche.com", role = "aut"),

NEWS.md

@@ -8,20 +8,21 @@
 ### Enhancements
 * Added explicit zero counts to `g_km` plot "at risk" annotation tables.
 * Added a flag for total level split in `analyze_patients_exposure_in_cols`.
-* Implemented `.indent_mods` argument in functions `h_tab_one_biomarker`, `h_tab_rsp_one_biomarker`, `h_tab_surv_one_biomarker`, `summarize_logistic`, `logistic_summary_by_flag`, `tabulate_rsp_biomarkers`, a_coxreg, `summarize_coxreg`, `tabulate_survival_biomarkers`, `surv_time`, `surv_timepoint`, and `cfun_by_flag`.
+* Implemented `.indent_mods` argument in functions `h_tab_one_biomarker`, `h_tab_rsp_one_biomarker`, `h_tab_surv_one_biomarker`, `summarize_logistic`, `logistic_summary_by_flag`, `tabulate_rsp_biomarkers`, `a_coxreg`, `summarize_coxreg`, `tabulate_survival_biomarkers`, `surv_time`, `surv_timepoint`, and `cfun_by_flag`.
 * Updated `summarize_coxreg` to print covariates in data rows for univariate Cox regression with no interactions and content rows otherwise.
 * Removed "baseline status" text from `d_count_abnormal_by_baseline` labels.
 * Improved default sizing of annotation tables in `g_km` and added dynamic scaling of the `surv_med` and `coxph` annotation tables, with customization via the `width_annots` argument.
 
 ### Bug Fixes
 * Fixed bug in `split_text_grob` preventing titles and footnotes from being properly formatted and printed by `decorate_grob`.
-* Fixed bug in `g_lineplot` preventing lines from being added to the plot when midpoint statistic calculations result in `NA` value(s).
+* Fixed bug in `g_lineplot` preventing the addition of lines to the plot when midpoint statistic calculations result in `NA` value(s).
 * Fixed `tern:::tidy.glm` formals to respect `broom:::tidy.default` formals.
 
 ### Miscellaneous
+* Updated `README` to include installation instructions for CRAN.
 * Remove examples for unexported functions.
 * Export functions `has_count_in_cols`, `has_counts_difference`, `combine_counts`, `h_tab_rsp_one_biomarker`, `arrange_grobs`, `a_count_patients_sum_exposure`, `a_coxreg`, `groups_list_to_df`, `forest_viewport`.
-* Updated README to include installation instructions for CRAN.
+* Updated `README` to include installation instructions for CRAN.
 * Began deprecation of `indent_mod` argument and replaced it with the `.indent_mods` argument in `summarize_num_patients` and `analyze_num_patients`.
 
 # tern 0.8.2
@@ -81,13 +82,13 @@
 
 ### Documentation and Tests
 * Added more tests to increase code coverage.
-* Created separate documentation files for functions in different sections of pkgdown reference.
+* Created separate documentation files for functions in different sections of `pkgdown` reference.
 * Created separate `.R` files for logistic regression and cox regression helper functions.
 * Fixed table tests using `analyze_num_patients` to generate an initial summary so there is no 
   repetition when paginating.
 * Updated tests to use `testthat` 3rd edition and replaced applicable tests with snapshot testing.
 * Updated `summarize_ancova` examples to use `iris` dataset instead of `scda` data.
-* Created vignette which saves cached synthetic CDISC dataset files to the `data/` folder and 
+* Created vignette which saves cached synthetic `CDISC` dataset files to the `data/` folder and 
   generated cached synthetic datasets.
 * Updated all examples/tests to use datasets from the `data/` folder instead of `scda` datasets.
 * Removed all template tests from `tern`. These tests are in internal repo `scda.test`.
@@ -104,7 +105,7 @@
 # tern 0.7.10
 
 ### New Features
-* Added stratified Newcombe and stratified Wilson statistics to `estimate_proportion` and
+* Added stratified `Newcombe` and stratified Wilson statistics to `estimate_proportion` and
   `estimate_proportion_diff` with relative tests.
 * Added `stat_mean_pval`, a new summary statistic to calculate the p-value of 
   the mean.
@@ -122,7 +123,7 @@
   log-rank test instead of Cox Proportional-Hazards Model.
 * Implemented `nestcolor` in all examples by adapting `g_km`, `g_ipp`, 
   `g_waterfall`, `g_step`, `g_lineplot`, and `g_forest`.
-* Added parameters `interaction_y` and `interaction_item` in ANCOVA to make the 
+* Added parameters `interaction_y` and `interaction_item` in `ANCOVA` to make the 
   interaction calculations available.
 * Added new parameter `footnotes` to add footnotes to `g_km`.
 
@@ -265,7 +266,7 @@
 
 * Enhanced `g_lineplot` with table to automatically scale the table height and return a `ggplot` object.
 * Enhanced `g_ipp` with caption argument and adjust the position.
-* Enhanced `prop_diff`, `tern` function and related functions to be able to apply a continuity correction in the Newcombe method.
+* Enhanced `prop_diff`, `tern` function and related functions to be able to apply a continuity correction in the `Newcombe` method.
 * Enhanced `summarize_numeric_in_columns` and `summarize_variables` to allow factor/character summary and to be able to summarize the number of `BLQs` in `AVALC` from `ADPC` dataset.
 * Updated order of summarize variables stats in manual for order consistency.
 * Added a `sum` option to `summarize_variables`.
@@ -344,7 +345,7 @@
 * Fixed `prop_diff_cmh` to handle edge case of no FALSE (or TRUE) responses.
 * Enhanced `g_mmrm_diagnostic` to improve error handling when data is not amenable to the Locally Weighted Scatterplot Smoothing.
 * Fixes in `g_km`:
-  * Plot can now display any combination of the annotation tables for number of patients at risk, median survival time, and CoxPH summary.
+  * Plot can now display any combination of the annotation tables for number of patients at risk, median survival time, and `CoxPH` summary.
   * Function will return a warning instead of an error if the `arm` variable includes a single level and `annot_coxph = TRUE`.
   * Lines in the plot now start at time 0 and probability 1.
   * Category labels can include the equals sign.
@@ -428,7 +429,7 @@
 * New arguments `yval` and `ci_ribbon` added to `g_km`.
 * Add new individual patient plot function `g_ipp` along with helpers `h_g_ipp` and `h_set_nest_theme`.
 * Fixed bug in `count_patients_with_events`, now shows zero counts without percentage.
-* Fixed bug in `get_mmrm_lsmeans` which did not allow MMRM analysis of more than 3000 observations.
+* Fixed bug in `get_mmrm_lsmeans` which did not allow `MMRM` analysis of more than 3000 observations.
 * Updated `stat_mean_ci` and `stat_median_ci` to handle edge cases with number of elements in input series equal to 1. For such cases, `NA_real_` is now returned, instead of `NA` or `+/-Inf` for confidence interval (CI) estimates.
 * Rename `n_lim` argument of `stat_mean_ci` to `n_min` to better reflect its desired meaning.
 
@@ -449,7 +450,7 @@ This version of `tern` introduces a major rewriting of `tern` due to the change
 * Fitting and tabulating the results of Cox regressions with `fit_coxreg_univar`, `fit_coxreg_multivar` and `summarize_coxreg`, respectively.
 * Pruning occurrence tables (or tables with counts and fractions) with flexible rules, see `?prune_occurrences` for details.
 * Sorting occurrence tables using different options, see `?score_occurrences` for details.
-* Fitting and tabulating MMRM models with `fit_mmrm` and `as.rtable` and `summarize_lsmeans`, see `?tabulate_mmrm` for details.
+* Fitting and tabulating `MMRM` models with `fit_mmrm` and `as.rtable` and `summarize_lsmeans`, see `?tabulate_mmrm` for details.
 * Counting the number of unique and non-unique patients with `summarize_num_patients`.
 * Counting occurrences with `count_occurrences`.
 * Counting occurrences by grade with `summarize_occurrences_by_grade` and `count_occurrences_by_grade`.
@@ -466,16 +467,16 @@ This version of `tern` introduces a major rewriting of `tern` due to the change
 * Add new function `t_contingency` for contingency tables.
 * Renamed the class `splitText` to `dynamicSplitText` to resolve the name conflict with the package `ggpubr`.
 * Add `rreplace_format` for tabulation post-processing.
-* Add new tern function `t_ancova` to create ANCOVA tables, as well as corresponding elementary table function `t_el_ancova` and summary function `s_ancova`.
+* Add new tern function `t_ancova` to create `ANCOVA` tables, as well as corresponding elementary table function `t_el_ancova` and summary function `s_ancova`.
 * Add new tern function `s_odds_ratio` to estimate Odds Ratio of response between categories, as well as the corresponding elementary table function `t_el_odds_ratio`.
-* Added new CI methods (Agresti-Coull, Jeffreys) for `s_proportion`.
+* Added new CI methods (`Agresti-Coull`, `Jeffreys`) for `s_proportion`.
 * Added new CI methods `anderson-hauck` and `newcombe` to `s_proportion_diff`.
 * Added new p-value methods (Fisher's Exact, Chi-Squared Test with Schouten Correction) for `s_test_proportion_diff`.
 * The binary summary table function `t_binary_outcome` takes now lists (instead of character vectors) specified by the helper function `control_binary_comparison` as the arguments `strat_analysis` and `unstrat_analysis`. Odds Ratio estimates and CIs are now removable and included by default, similarly to the other subsections of the arm comparison analyses. Also added argument `rsp_multinomial`.
 * Add new table function `t_el_multinomial_proportion`.
 * Add new table function `t_abn_shift`.
-* Add new MMRM analysis function `s_mmrm`, as well as corresponding table functions `t_mmrm_lsmeans`, `t_mmrm_cov`, `t_mmrm_diagnostic`, `t_mmrm_fixed`, and plot functions `g_mmrm_lsmeans`, `g_mmrm_diagnostic`. The results of these match SAS results (up to numeric precision).
-* Deprecated old MMRM functions `a_mmrm` and `t_mmrm` (they give a deprecation warning but still work) to remove in the next release. The reason is that the results of these functions don't match SAS results.
+* Add new `MMRM` analysis function `s_mmrm`, as well as corresponding table functions `t_mmrm_lsmeans`, `t_mmrm_cov`, `t_mmrm_diagnostic`, `t_mmrm_fixed`, and plot functions `g_mmrm_lsmeans`, `g_mmrm_diagnostic`. The results of these match SAS results (up to numeric precision).
+* Deprecated old `MMRM` functions `a_mmrm` and `t_mmrm` (they give a deprecation warning but still work) to remove in the next release. The reason is that the results of these functions don't match SAS results.
 * Fix bug in `g_km` related to numbers in patients at risk table to correct numbers for integer time-to-event variable inputs.
 
 # tern 0.6.7
@@ -489,7 +490,7 @@ This version of `tern` introduces a major rewriting of `tern` due to the change
 * Removed `grade_levels` argument from `t_events_term_grade_id` functions. Post-processing by reordering the leaves of the table tree creates a different ordering of rows if required. Creating a helper function will occur at a later time.
 * Added `prune_zero_rows` argument to `t_events_per_term_grade_id` and `t_max_grade_per_id` to not show rows of all zeros as they can clutter the visualization in the Shiny app and make it slower.
 * Fixed position of (N=xx) in `t_summary_by` output when summarizing numeric columns in parallel with `compare_in_header`.
-* Rename t_coxph to t_coxph_pairwise to reflect the model process, add details in documentation.
+* Rename `t_coxph` to `t_coxph_pairwise` to reflect the model process, add details in documentation.
 * Remove `test.nest` dependency.
 * Keep column labels when splitting data into tree.
 
@@ -566,7 +567,7 @@ This version of `tern` introduces a major rewriting of `tern` due to the change
 
 # tern 0.6.1
 
-* Fixed colors in Kaplan-Meyer-Plot
+* Fixed colors in Kaplan-Meier Plot
 * Refactor of all functions to pass `test.nest` tests:
     * Changed `width_row.names` argument of `g_forest` function into `width_row_names`.
     * Changed `censor.show` argument of `g_km` function into `censor_show`.

R/abnormal_by_worst_grade_worsen.R

@@ -11,7 +11,7 @@
 #' @name abnormal_by_worst_grade_worsen
 NULL
 
-#' Helper Function to Prepare ADLB with Worst Labs
+#' Helper Function to Prepare `ADLB` with Worst Labs
 #'
 #' @description `r lifecycle::badge("stable")`
 #'

R/control_survival.R

@@ -1,9 +1,9 @@
-#' Control Function for CoxPH Model
+#' Control Function for `CoxPH` Model
 #'
 #' @description `r lifecycle::badge("stable")`
 #'
-#' This is an auxiliary function for controlling arguments for CoxPH model, typically used internally to specify
-#' details of CoxPH model for [s_coxph_pairwise()]. `conf_level` refers to Hazard Ratio estimation.
+#' This is an auxiliary function for controlling arguments for `CoxPH` model, typically used internally to specify
+#' details of `CoxPH` model for [s_coxph_pairwise()]. `conf_level` refers to Hazard Ratio estimation.
 #'
 #' @inheritParams argument_convention
 #' @param pval_method (`string`)\cr p-value method for testing hazard ratio = 1.

R/coxph.R

@@ -182,7 +182,7 @@ estimate_coef <- function(variable, given,
 #'
 #' @examples
 #' # `car::Anova` on cox regression model including strata and expected
-#' # a likelihood ratio test triggers a warning as only Wald method is
+#' # a likelihood ratio test triggers a warning as only `Wald` method is
 #' # accepted.
 #'
 #' library(survival)
@@ -224,7 +224,7 @@ try_car_anova <- function(mod,
   return(y)
 }
 
-#' Fit the Cox Regression Model and Anova
+#' Fit the Cox Regression Model and `Anova`
 #'
 #' The functions allows to derive from the [survival::coxph()] results the effect p.values using [car::Anova()].
 #' This last package introduces more flexibility to get the effect p.values.
@@ -328,7 +328,7 @@ check_increments <- function(increments, covariates) {
 #' @param data (`data.frame`)\cr A data frame which includes the variable in formula and covariates.
 #' @param conf_level (`proportion`)\cr The confidence level for the hazard ratio interval estimations. Default is 0.95.
 #' @param pval_method (`character`)\cr The method used for the estimation of p-values, should be one of
-#'   "wald" (default) or "likelihood".
+#'   `"wald"` (default) or `"likelihood"`.
 #' @param ... Optional parameters passed to [survival::coxph()]. Can include `ties`, a character string specifying the
 #'   method for tie handling, one of `exact` (default), `efron`, `breslow`.
 #'

R/data.R

@@ -1,30 +1,30 @@
-#' Simulated CDISC Data for Examples
+#' Simulated `CDISC` Data for Examples
 #'
-#' @format rds (data.frame)
+#' @format `rds` (data.frame)
 #'
 #' @name ex_data
 NULL
 
-#' @describeIn ex_data ADSL data
+#' @describeIn ex_data `ADSL` data
 #'
 "tern_ex_adsl"
 
-#' @describeIn ex_data ADAE data
+#' @describeIn ex_data `ADAE` data
 #'
 "tern_ex_adae"
 
-#' @describeIn ex_data ADLB data
+#' @describeIn ex_data `ADLB` data
 #'
 "tern_ex_adlb"
 
-#' @describeIn ex_data ADPP data
+#' @describeIn ex_data `ADPP` data
 #'
 "tern_ex_adpp"
 
-#' @describeIn ex_data ADRS data
+#' @describeIn ex_data `ADRS` data
 #'
 "tern_ex_adrs"
 
-#' @describeIn ex_data ADTTE data
+#' @describeIn ex_data `ADTTE` data
 #'
 "tern_ex_adtte"

R/decorate_grob.R

@@ -304,7 +304,7 @@ split_string <- function(text, width) {
 #'
 #' @return A text grob.
 #'
-#' @details This code is taken from R Graphics by Paul Murell, 2nd edition
+#' @details This code is taken from `R Graphics by Paul Murell, 2nd edition`
 #'
 #' @keywords internal
 split_text_grob <- function(text,

R/desctools_binom_diff.R

@@ -334,11 +334,11 @@ desctools_binom <- function(x1, n1, x2, n2, conf.level = 0.95, sides = c( # noli
 #' @param x (`count`)\cr number of successes
 #' @param n (`count`)\cr number of trials
 #' @param conf.level (`proportion`)\cr confidence level, defaults to 0.95.
-#' @param sides (`character`)\cr side of the confidence interval to compute. Must be one of "two-sided" (default),
-#'   "left", or "right".
-#' @param method (`character`)\cr method to use. Can be one out of: "wald", "wilson", "wilsoncc", "agresti-coull",
-#'   "jeffreys", "modified wilson", "modified jeffreys", "clopper-pearson", "arcsine", "logit", "witting", "pratt",
-#'   "midp", "lik", and "blaker".
+#' @param sides (`character`)\cr side of the confidence interval to compute. Must be one of `"two-sided"` (default),
+#'   `"left"`, or `"right"`.
+#' @param method (`character`)\cr method to use. Can be one out of: `"wald"`, `"wilson"`, `"wilsoncc"`,
+#' `"agresti-coull"`, `"jeffreys"`, `"modified wilson"`, `"modified jeffreys"`, `"clopper-pearson"`, `"arcsine"`,
+#' `"logit"`, `"witting"`, `"pratt"`, `"midp"`, `"lik"`, and `"blaker"`.
 #'
 #' @return A `matrix` with 3 columns containing:
 #'   * `est`: estimate of proportion difference.

R/estimate_proportion.R

@@ -386,7 +386,7 @@ prop_wald <- function(rsp, conf_level, correct = FALSE) {
   c(l_ci, u_ci)
 }
 
-#' @describeIn h_proportions Calculates the Agresti-Coull interval (created by Alan Agresti and Brent Coull) by
+#' @describeIn h_proportions Calculates the `Agresti-Coull` interval (created by `Alan Agresti` and `Brent Coull`) by
 #'   (for 95% CI) adding two successes and two failures to the data and then using the Wald formula to construct a CI.
 #'
 #' @examples

R/g_lineplot.R

@@ -11,7 +11,7 @@
 #'   * `y` (`character`)\cr name of y-axis variable.
 #'   * `strata` (`character`)\cr name of grouping variable, i.e. treatment arm. Can be `NA` to indicate lack of groups.
 #'   * `paramcd` (`character`)\cr name of the variable for parameter's code. Used for y-axis label and plot's subtitle.
-#'     Can be `NA` if paramcd is not to be added to the y-axis label or subtitle.
+#'     Can be `NA` if `paramcd` is not to be added to the y-axis label or subtitle.
 #'   * `y_unit` (`character`)\cr name of variable with units of `y`. Used for y-axis label and plot's subtitle.
 #'     Can be `NA` if y unit is not to be added to the y-axis label or subtitle.
 #' @param mid (`character` or `NULL`)\cr names of the statistics that will be plotted as midpoints.
@@ -39,13 +39,13 @@
 #'   or two-element numeric vector).
 #' @param ggtheme (`theme`)\cr a graphical theme as provided by `ggplot2` to control styling of the plot.
 #' @param y_lab (`character`)\cr y-axis label. If equal to `NULL`, then no label will be added.
-#' @param y_lab_add_paramcd (`logical`)\cr should paramcd, i.e. `unique(df[[variables["paramcd"]]])` be added to the
+#' @param y_lab_add_paramcd (`logical`)\cr should `paramcd`, i.e. `unique(df[[variables["paramcd"]]])` be added to the
 #'   y-axis label `y_lab`?
 #' @param y_lab_add_unit (`logical`)\cr should y unit, i.e. `unique(df[[variables["y_unit"]]])` be added to the y-axis
 #'   label `y_lab`?
 #' @param title (`character`)\cr plot title.
 #' @param subtitle (`character`)\cr plot subtitle.
-#' @param subtitle_add_paramcd (`logical`)\cr should paramcd, i.e. `unique(df[[variables["paramcd"]]])` be added to
+#' @param subtitle_add_paramcd (`logical`)\cr should `paramcd`, i.e. `unique(df[[variables["paramcd"]]])` be added to
 #'   the plot's subtitle `subtitle`?
 #' @param subtitle_add_unit (`logical`)\cr should y unit, i.e. `unique(df[[variables["y_unit"]]])` be added to the
 #'   plot's subtitle `subtitle`?
@@ -404,7 +404,7 @@ g_lineplot <- function(df,
   }
 }
 
-#' Helper function to get the right formatting in the optional table in g_lineplot.
+#' Helper function to get the right formatting in the optional table in `g_lineplot`.
 #'
 #' @description `r lifecycle::badge("stable")`
 #'
@@ -461,7 +461,7 @@ h_format_row <- function(x, format, labels = NULL) {
   row
 }
 
-#' Control Function for g_lineplot Function
+#' Control Function for `g_lineplot` Function
 #'
 #' @description `r lifecycle::badge("stable")`
 #'
@@ -471,8 +471,8 @@ h_format_row <- function(x, format, labels = NULL) {
 #' @param x (`character`)\cr x variable name.
 #' @param y (`character`)\cr y variable name.
 #' @param strata (`character` or `NA`)\cr strata variable name.
-#' @param paramcd (`character` or `NA`)\cr paramcd variable name.
-#' @param y_unit (`character` or `NA`)\cr y_unit variable name.
+#' @param paramcd (`character` or `NA`)\cr `paramcd` variable name.
+#' @param y_unit (`character` or `NA`)\cr `y_unit` variable name.
 #'
 #' @return A named character vector of variable names.
 #'

R/h_adsl_adlb_merge_using_worst_flag.R

@@ -1,4 +1,4 @@
-#' Helper Function for Deriving Analysis Datasets for LBT13 and LBT14
+#' Helper Function for Deriving Analysis Datasets for `LBT13` and `LBT14`
 #'
 #' @description `r lifecycle::badge("stable")`
 #'