Function reference • tern

Overview Pages

tern tern-package: tern Package

analyze_functions: Analyze functions

analyze_colvars_functions: Analyze functions on columns

summarize_functions: Summarize functions

formatting_functions: Formatting functions

Control Functions

These functions capture options in lists and take care of defaults (and checks where applicable). They avoid cluttering of function signatures with long lists of single arguments.

control_analyze_vars(): Control function for descriptive statistics

control_surv_med_annot() control_coxph_annot(): Control functions for Kaplan-Meier plot annotation tables

control_coxph(): Control function for Cox-PH model

control_coxreg(): Control function for Cox regression

control_incidence_rate(): Control function for incidence rate

control_lineplot_vars(): Control function for g_lineplot()

control_logistic(): Control function for logistic regression model fitting

control_step(): Control function for subgroup treatment effect pattern (STEP) calculations

control_surv_time(): Control function for survfit models for survival time

control_surv_timepoint(): Control function for survfit models for patients' survival rate at time points

Analysis Functions

Analyze functions with their corresponding statistics functions and formatted analysis functions.

Analyze functions are used in combination with rtables layout functions in the pipeline which creates the table.
Statistics functions (denoted by s_ prefix) compute the numbers that are tabulated later. In order to separate computation from formatting, they do not take care of rcell type formatting themselves.
Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze().

analyze_vars() s_summary() a_summary(): Analyze variables

analyze_vars_in_cols(): Summarize numeric variables in columns

analyze_num_patients() summarize_num_patients() s_num_patients() s_num_patients_content(): Number of patients

analyze_patients_exposure_in_cols() summarize_patients_exposure_in_cols() s_count_patients_sum_exposure() a_count_patients_sum_exposure(): Count patients and sum exposure across all patients in columns

compare_vars() s_compare(): Compare variables between groups

count_abnormal() s_count_abnormal() a_count_abnormal(): Patient counts with abnormal range values

count_abnormal_by_baseline() s_count_abnormal_by_baseline() a_count_abnormal_by_baseline(): Patient counts with abnormal range values by baseline status

count_abnormal_by_marked() s_count_abnormal_by_marked() a_count_abnormal_by_marked(): Count patients with marked laboratory abnormalities

count_abnormal_by_worst_grade() s_count_abnormal_by_worst_grade() a_count_abnormal_by_worst_grade(): Patient counts with the most extreme post-baseline toxicity grade per direction of abnormality

count_abnormal_lab_worsen_by_baseline() s_count_abnormal_lab_worsen_by_baseline() a_count_abnormal_lab_worsen_by_baseline(): Patient counts for laboratory events (worsen from baseline) by highest grade post-baseline

count_cumulative() s_count_cumulative() a_count_cumulative(): Cumulative counts with thresholds

count_missed_doses() s_count_nonmissing() s_count_missed_doses() a_count_missed_doses(): Count missed doses

count_occurrences() summarize_occurrences() s_count_occurrences() a_count_occurrences(): Occurrence counts

count_occurrences_by_grade() summarize_occurrences_by_grade() s_count_occurrences_by_grade() a_count_occurrences_by_grade(): Occurrence counts by grade

summarize_patients_events_in_cols() s_count_patients_and_multiple_events(): Count patients and events in columns

count_patients_with_event() s_count_patients_with_event() a_count_patients_with_event(): Count the number of patients with a particular event

count_patients_with_flags() s_count_patients_with_flags() a_count_patients_with_flags(): Count the number of patients with particular flags

count_values() s_count_values() a_count_values(): Count specific values

estimate_multinomial_response() s_length_proportion() a_length_proportion(): Estimation of proportions per level of factor

estimate_proportion() s_proportion() a_proportion(): Estimation of proportions

estimate_incidence_rate() s_incidence_rate() a_incidence_rate(): Incidence rate

estimate_odds_ratio() s_odds_ratio() a_odds_ratio(): Odds ratio estimation

estimate_proportion_diff() s_proportion_diff() a_proportion_diff(): Proportion difference

summarize_coxreg() s_coxreg() a_coxreg(): Cox proportional hazards regression

summarize_ancova() s_ancova() a_ancova(): Summary for analysis of covariance (ANCOVA).

summarize_change() s_change_from_baseline() a_change_from_baseline(): Summarize the change from baseline or absolute baseline values

summarize_colvars(): Summarize variables in columns

summarize_glm_count() s_glm_count() a_glm_count(): Summarize Poisson negative binomial regression

summarize_logistic(): Multivariate logistic regression table

surv_time() s_surv_time() a_surv_time(): Survival time analysis

surv_timepoint() s_surv_timepoint() a_surv_timepoint() s_surv_timepoint_diff() a_surv_timepoint_diff(): Survival time point analysis

tabulate_rsp_biomarkers(): Tabulate biomarker effects on binary response by subgroup

tabulate_rsp_subgroups() a_response_subgroups(): Tabulate binary response by subgroup

tabulate_survival_biomarkers(): Tabulate biomarker effects on survival by subgroup

tabulate_survival_subgroups() a_survival_subgroups(): Tabulate survival duration by subgroup

test_proportion_diff() s_test_proportion_diff() a_test_proportion_diff(): Difference test for two proportions

Analysis Helper Functions

These functions are useful in defining an analysis.

get_stats() get_formats_from_stats() get_labels_from_stats() get_indents_from_stats() tern_default_stats tern_default_formats tern_default_labels summary_formats() summary_labels(): Get default statistical methods and their associated formats, labels, and indent modifiers

h_coxreg_inter_effect() h_coxreg_extract_interaction() h_coxreg_inter_estimations(): Cox regression helper function for interactions

h_get_format_threshold() h_format_threshold(): Format extreme values

h_adlb_abnormal_by_worst_grade(): Helper function to prepare ADLB for count_abnormal_by_worst_grade()

h_adlb_worsen(): Helper function to prepare ADLB with worst labs

h_adsl_adlb_merge_using_worst_flag(): Helper function for deriving analysis datasets for select laboratory tables

h_ancova(): Helper function to return results of a linear model

h_append_grade_groups(): Helper function for s_count_occurrences_by_grade()

h_count_cumulative(): Helper function for s_count_cumulative()

h_coxreg_univar_formulas() h_coxreg_multivar_formula() h_coxreg_univar_extract() h_coxreg_multivar_extract(): Helper functions for Cox proportional hazards regression

h_decompose_gg(): ggplot decomposition

h_format_row(): Helper function to format the optional g_lineplot table

h_ggkm(): Helper function to create a KM plot

h_km_layout(): Helper function to prepare a KM layout

h_get_interaction_vars() h_interaction_coef_name() h_or_cat_interaction() h_or_cont_interaction() h_or_interaction() h_simple_term_labels() h_interaction_term_labels() h_glm_simple_term_extract() h_glm_interaction_extract() h_glm_inter_term_extract() h_logistic_simple_terms() h_logistic_inter_terms(): Helper functions for multivariate logistic regression

h_map_for_count_abnormal(): Helper function to create a map data frame for trim_levels_to_map()

or_glm() or_clogit(): Helper functions for odds ratio estimation

h_pkparam_sort(): Sort pharmacokinetic data by PARAM variable

prop_diff_wald() prop_diff_ha() prop_diff_nc() prop_diff_cmh() prop_diff_strat_nc(): Helper functions to calculate proportion difference

prop_wilson() prop_strat_wilson() prop_clopper_pearson() prop_wald() prop_agresti_coull() prop_jeffreys(): Helper functions for calculating proportion confidence intervals

h_rsp_to_logistic_variables() h_logistic_mult_cont_df() h_tab_rsp_one_biomarker(): Helper functions for tabulating biomarker effects on binary response by subgroup

h_proportion_df() h_proportion_subgroups_df() h_odds_ratio_df() h_odds_ratio_subgroups_df(): Helper functions for tabulating binary response by subgroup

h_split_by_subgroups(): Split data frame by subgroups

h_split_param(): Split parameters

h_stack_by_baskets(): Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records.

h_step_window() h_step_trt_effect() h_step_survival_formula() h_step_survival_est() h_step_rsp_formula() h_step_rsp_est(): Helper functions for subgroup treatment effect pattern (STEP) calculations

h_surv_to_coxreg_variables() h_coxreg_mult_cont_df() h_tab_surv_one_biomarker(): Helper functions for tabulating biomarker effects on survival by subgroup

h_survtime_df() h_survtime_subgroups_df() h_coxph_df() h_coxph_subgroups_df(): Helper functions for tabulating survival duration by subgroup

h_tab_one_biomarker(): Helper function for tabulation of a single biomarker result

h_tbl_coxph_pairwise(): Helper function for generating a pairwise Cox-PH table

h_tbl_median_surv(): Helper function for survival estimations

h_worsen_counter(): Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline()

imputation_rule(): Apply 1/3 or 1/2 imputation rule to data

labels_use_control(): Update labels according to control specifications

Model-Specific Functions

These functions help with fitting or extracting results from specific models.

estimate_coef(): Hazard ratio estimation in interactions

extract_rsp_biomarkers(): Prepare response data estimates for multiple biomarkers in a single data frame

extract_rsp_subgroups(): Prepare response data for population subgroups in data frames

extract_survival_biomarkers(): Prepare survival data estimates for multiple biomarkers in a single data frame

extract_survival_subgroups(): Prepare survival data for population subgroups in data frames

fit_coxreg_univar() fit_coxreg_multivar(): Fitting functions for Cox proportional hazards regression

fit_logistic(): Fit for logistic regression

fit_rsp_step(): Subgroup treatment effect pattern (STEP) fit for binary (response) outcome

fit_survival_step(): Subgroup treatment effect pattern (STEP) fit for survival outcome

get_smooths(): Smooth function with optional grouping

logistic_regression_cols(): Logistic regression multivariate column layout function

logistic_summary_by_flag(): Logistic regression summary table

tidy(<glm>): Custom tidy method for binomial GLM results

tidy(<step>): Custom tidy method for STEP results

tidy(<summary.coxph>) tidy(<coxreg.univar>) tidy(<coxreg.multivar>): Custom tidy methods for Cox regression

univariate(): Univariate formula special term

Graphs

These function create graphical type output.

s_bland_altman() g_bland_altman(): Bland-Altman analysis

g_forest(): Create a forest plot from an rtable

g_ipp(): Individual patient plots

g_km(): Kaplan-Meier plot

g_lineplot(): Line plot with optional table

g_step(): Create a STEP graph

g_waterfall(): Horizontal waterfall plot

rtables Helper Functions

These functions help to work with the rtables package and may be moved there later.

add_riskdiff(): Split function to configure risk difference column

add_rowcounts(): Layout-creating function to add row total counts

append_varlabels(): Add variable labels to top left corner in table

default_na_str() set_default_na_str(): Default string replacement for NA values

as.rtable(): Convert to rtable

combine_counts(): Combine counts

combine_groups(): Reference and treatment group combination

combine_levels(): Combine factor levels

combine_vectors(): Element-wise combination of two vectors

h_col_indices(): Obtain column indices

h_row_first_values() h_row_counts() h_row_fractions() h_col_counts() h_content_first_row() is_leaf_table() check_names_indices(): Helper functions for accessing information from rtables

rtable2gg(): Convert rtable objects to ggplot objects

split_cols_by_groups(): Split columns by groups of levels

to_string_matrix(): Convert table into matrix of strings

groups_list_to_df(): Convert list of groups to a data frame

ref_group_position() level_order(): Custom split functions

rtables Formatting Functions

These functions provide customized formatting rules to work with the rtables package.

format_auto(): Format automatically using data significant digits

format_count_fraction(): Format count and fraction

format_count_fraction_fixed_dp(): Format count and percentage with fixed single decimal place

format_count_fraction_lt10(): Format count and fraction with special case for count < 10

format_extreme_values(): Format a single extreme value

format_extreme_values_ci(): Format extreme values part of a confidence interval

format_fraction(): Format fraction and percentage

format_fraction_fixed_dp(): Format fraction and percentage with fixed single decimal place

format_fraction_threshold(): Format fraction with lower threshold

format_sigfig(): Format numeric values by significant figures

format_xx(): Format XX as a formatting function

rtables Scoring Functions

These functions can help with table sorting.

score_occurrences() score_occurrences_cols() score_occurrences_subtable() score_occurrences_cont_cols(): Occurrence table sorting

rtables Pruning Functions

These functions and classes help with flexible pruning of tables.

`&`(<CombinationFunction>,<CombinationFunction>) `|`(<CombinationFunction>,<CombinationFunction>) `!`(<CombinationFunction>): Class for CombinationFunction

keep_rows() keep_content_rows() has_count_in_cols() has_count_in_any_col() has_fraction_in_cols() has_fraction_in_any_col() has_fractions_difference() has_counts_difference(): Occurrence table pruning

Graph Helper Functions

These functions are useful to modify graphs.

decorate_grob(): Add titles, footnotes, page Number, and a bounding box to a grid grob

split_text_grob(): Split text according to available text width

decorate_grob_factory(): Update page number

decorate_grob_set(): Decorate set of grobs and add page numbering

h_g_ipp(): Helper function to create simple line plot over time

h_xticks(): Helper function to calculate x-tick positions

Data Helper Functions

These functions are used by other functions to derive data.

aesi_label(): Labels for adverse event baskets

as_factor_keep_attributes(): Conversion of a vector to a factor

bins_percent_labels(): Labels for bins in percent

combine_levels(): Combine factor levels

cut_quantile_bins(): Cut numeric vector into empirical quantile bins

day2month(): Conversion of days to months

df_explicit_na(): Encode categorical missing values in a data frame

d_count_abnormal_by_baseline(): Description function for s_count_abnormal_by_baseline()

d_count_cumulative(): Description of cumulative count

d_count_missed_doses(): Description function that calculates labels for s_count_missed_doses()

d_onco_rsp_label(): Description of standard oncology response

d_pkparam(): Generate PK reference dataset

d_proportion(): Description of the proportion summary

d_proportion_diff(): Description of method used for proportion comparison

d_rsp_subgroups_colvars(): Labels for column variables in binary response by subgroup table

d_survival_subgroups_colvars(): Labels for column variables in survival duration by subgroup table

d_test_proportion_diff(): Description of the difference test between two proportions

explicit_na(): Missing data

fct_collapse_only(): Collapse factor levels and keep only those new group levels

fct_discard(): Discard specified levels of a factor

fct_explicit_na_if(): Insertion of explicit missing values in a factor

f_conf_level(): Utility function to create label for confidence interval

f_pval(): Utility function to create label for p-value

h_data_plot(): Helper function to tidy survival fit data

month2day(): Conversion of months to days

reapply_varlabels(): Reapply variable labels

sas_na(): Convert strings to NA

stat_mean_ci(): Confidence interval for mean

stat_mean_pval(): p-Value of the mean

stat_median_ci(): Confidence interval for median

stat_propdiff_ci(): Proportion difference and confidence interval

strata_normal_quantile(): Helper function for the estimation of stratified quantiles

to_n(): Replicate entries of a vector if required

update_weights_strat_wilson(): Helper function for the estimation of weights for prop_strat_wilson()

Assertion Functions

These functions supplement those in the checkmate package.

assert_list_of_variables() assert_df_with_variables() assert_valid_factor() assert_df_with_factors() assert_proportion_value(): Additional assertions to use with checkmate

Data

Data included in the package.

tern_ex_adsl tern_ex_adae tern_ex_adlb tern_ex_adpp tern_ex_adrs tern_ex_adtte: Simulated CDISC data for examples

Deprecated Functions

Functions that are currently deprecated within tern.

forest_viewport(): Create a viewport tree for the forest plot

h_decompose_gg(): ggplot decomposition

h_ggkm(): Helper function to create a KM plot

h_grob_coxph(): Helper function to create Cox-PH grobs

h_grob_median_surv(): Helper function to create survival estimation grobs

h_grob_tbl_at_risk(): Helper function to create patient-at-risk grobs

h_grob_y_annot(): Helper function to create grid object with y-axis annotation

h_km_layout(): Helper function to prepare a KM layout

stack_grobs(): Stack multiple grobs

arrange_grobs(): Arrange multiple grobs

draw_grob(): Draw grob