Expose digits argument for h_tbl_median_surv

[Copilot]

Pull Request

Fixes #1467

Changes

• h_tbl_median_surv(): Added digits parameter (default: 4) to control signif() precision for median and CI values.
• control_surv_med_annot(): Added digits parameter to expose the setting via the control interface.
• g_km(): Passes digits from control_annot_surv_med with %||% 4 fallback for backward compatibility.

Usage

# Via control object in g_km()
g_km(
  df = df,
  variables = variables,
  control_annot_surv_med = control_surv_med_annot(digits = 2)
)

# Direct function call
h_tbl_median_surv(fit_km = fit, digits = 2)

Backward compatible: default value maintains existing behavior.

[github-actions]

Unit Tests Summary

    1 files     85 suites   1m 46s ⏱️
  932 tests   923 ✅   9 💤 0 ❌
2 319 runs  1 603 ✅ 716 💤 0 ❌

Results for commit 9028697.

♻️ This comment has been updated with latest results.

[github-actions]

Unit Test Performance Difference

Additional test case details

Test Suite	$Status$	Time on main	$±Time$	Test Case
h_km	👶		$+0.03$	h_tbl_median_surv_works_with_custom_digits_parameter

Results for commit fddfa7f

♻️ This comment has been updated with latest results.

[donyunardi]

Hi @shajoezhu

• Should the digit be exposed all the way to the top to g_km? This is the function that's ultimately being used for tm_g_km
• there's no request to address h_km. This seems to be out-of-scope from the intended issue. Or is there any relationship between g_km and h_km?
• CI/CD did not pass, usually we make sure it passed before review.

[Melkiades]

is this still active @shajoezhu @donyunardi?

[Melkiades]

@donyunardi all points here should have been addressed:

1. digits flows through control_surv_med_annot() → g_km(), consistent with how x, y, w, h, fill are already handled. No need for a top-level g_km() argument.
2. h_tbl_median_surv() is the function from issue [Feature Request]: Expose the digits argument in tern::h_tbl_median_surv() #1467 — it lives in h_km.R. control_surv_med_annot() (same file) is the control interface that connects it to g_km().
3. Branch rebased on main, snapshots added, CI should be green now (doing roxygen2 fix now)

[github-actions]

Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                     101       3  97.03%   242, 244-245
R/abnormal_by_marked.R                        88       8  90.91%   94-98, 281, 283-284
R/abnormal_by_worst_grade.R                   94       3  96.81%   215, 217-218
R/abnormal_lab_worsen_by_baseline.R          159      10  93.71%   205-208, 213, 215-216, 459-461
R/abnormal.R                                  78       2  97.44%   222, 224
R/analyze_variables.R                        320      11  96.56%   593-596, 818-823, 831
R/analyze_vars_in_cols.R                     178      14  92.13%   178, 221, 235-236, 238, 246-254
R/bland_altman.R                              92       1  98.91%   46
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                         35       0  100.00%
R/control_incidence_rate.R                    10       0  100.00%
R/control_logistic.R                           7       0  100.00%
R/control_step.R                              23       1  95.65%   58
R/control_survival.R                          16       0  100.00%
R/count_cumulative.R                         115       4  96.52%   74, 270-271, 273
R/count_missed_doses.R                        89       4  95.51%   206-209
R/count_occurrences_by_grade.R               169       8  95.27%   178, 386, 388, 465, 467, 469, 473-474
R/count_occurrences.R                        137      10  92.70%   119, 262-264, 330-332, 334, 338-339
R/count_patients_events_in_cols.R             67       1  98.51%   60
R/count_patients_with_event.R                 73       2  97.26%   220, 223
R/count_patients_with_flags.R                 93       2  97.85%   234, 236
R/count_values.R                              61       2  96.72%   193, 196
R/cox_regression_inter.R                     154       0  100.00%
R/cox_regression.R                           161       0  100.00%
R/coxph.R                                    165       7  95.76%   190-194, 236, 251, 259, 265-266
R/d_pkparam.R                                406       0  100.00%
R/decorate_grob.R                            116       0  100.00%
R/desctools_binom_diff.R                     621      64  89.69%   53, 88-89, 125-126, 129, 199, 223-232, 264, 266, 286, 290, 294, 298, 353, 356, 359, 362, 422, 430, 439, 444-447, 454, 457, 466, 469, 516-517, 519-520, 522-523, 525-526, 593, 604-616, 620, 663, 676, 680
R/df_explicit_na.R                            45       0  100.00%
R/estimate_multinomial_rsp.R                  86       4  95.35%   65, 212, 214-215
R/estimate_proportion.R                      240       7  97.08%   88, 99, 255, 257-258, 389, 553
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     190       2  98.95%   141, 276
R/g_forest.R                                 585      60  89.74%   240, 252-255, 260-261, 275, 277, 287-290, 335-338, 345, 414, 501, 514, 518-519, 524-525, 538, 554, 601, 630, 705, 714, 720, 739, 794-814, 817, 828, 847, 902, 905, 1040-1045
R/g_ipp.R                                    133       0  100.00%
R/g_km.R                                     354      57  83.90%   285-288, 307-309, 363-366, 400, 428, 432-475, 482-486
R/g_lineplot.R                               261      22  91.57%   222, 397-404, 443-453, 562, 570
R/g_step.R                                    68       1  98.53%   108
R/g_waterfall.R                               47       0  100.00%
R/h_adsl_adlb_merge_using_worst_flag.R        73       0  100.00%
R/h_biomarkers_subgroups.R                    91      23  74.73%   40-42, 84-103
R/h_cox_regression.R                         110       0  100.00%
R/h_incidence_rate.R                          45       0  100.00%
R/h_km.R                                     510      39  92.35%   147, 199-204, 297, 388, 390-391, 402-404, 423, 430-431, 433-435, 443-445, 470, 475-478, 661-664, 1118-1127
R/h_logistic_regression.R                    468       3  99.36%   203-204, 273
R/h_map_for_count_abnormal.R                  54       0  100.00%
R/h_pkparam_sort.R                            15       0  100.00%
R/h_response_biomarkers_subgroups.R           77      12  84.42%   50-55, 107-112
R/h_response_subgroups.R                     178      18  89.89%   257-270, 329-334
R/h_stack_by_baskets.R                        64       1  98.44%   89
R/h_step.R                                   178       0  100.00%
R/h_survival_biomarkers_subgroups.R           73       6  91.78%   111-116
R/h_survival_duration_subgroups.R            207      18  91.30%   259-271, 336-341
R/imputation_rule.R                           17       0  100.00%
R/incidence_rate.R                           103       7  93.20%   68-73, 242
R/logistic_regression.R                      102       0  100.00%
R/missing_data.R                              26       5  80.77%   39, 62-63, 96, 106
R/odds_ratio.R                               157       4  97.45%   270-273
R/prop_diff_test.R                           197       2  98.98%   267, 269
R/prop_diff.R                                526      21  96.01%   97-101, 138, 341, 343, 429-436, 585, 910, 1082, 1086, 1089
R/prune_occurrences.R                         57       0  100.00%
R/response_biomarkers_subgroups.R            124      10  91.94%   88-91, 270-275
R/response_subgroups.R                       252      16  93.65%   100-105, 271-275, 280, 282-283, 310-311
R/riskdiff.R                                  65       4  93.85%   94-97
R/rtables_access.R                            38       0  100.00%
R/score_occurrences.R                         20       1  95.00%   124
R/split_cols_by_groups.R                      49       0  100.00%
R/stat.R                                      59       0  100.00%
R/summarize_ancova.R                         174       2  98.85%   355-356
R/summarize_change.R                          72       3  95.83%   175, 177-178
R/summarize_colvars.R                         13       1  92.31%   75
R/summarize_coxreg.R                         172       0  100.00%
R/summarize_glm_count.R                      269      10  96.28%   129-130, 202-203, 459-463, 596
R/summarize_num_patients.R                   121      10  91.74%   122-124, 244, 248, 252-253, 337-338, 340
R/summarize_patients_exposure_in_cols.R      155       7  95.48%   58, 232-233, 237, 357-358, 362
R/survival_biomarkers_subgroups.R            136      10  92.65%   117-122, 228-231
R/survival_coxph_pairwise.R                  154       9  94.16%   55-56, 124, 138, 145, 149, 288, 290-291
R/survival_duration_subgroups.R              250      15  94.00%   124-129, 268-273, 286, 288-289
R/survival_time.R                            128       1  99.22%   261
R/survival_timepoint.R                       153       2  98.69%   320, 322
R/utils_checkmate.R                           68       0  100.00%
R/utils_default_stats_formats_labels.R       201       0  100.00%
R/utils_factor.R                              87       1  98.85%   99
R/utils_ggplot.R                             110       0  100.00%
R/utils_grid.R                               126       5  96.03%   164, 279-286
R/utils_rtables.R                            125       9  92.80%   39, 46, 414-415, 537-541
R/utils_split_funs.R                          52       2  96.15%   82, 94
R/utils.R                                    141       7  95.04%   131, 134, 137, 141, 150-151, 345
TOTAL                                      12360     594  95.19%

Diff against main

Filename      Stmts    Miss  Cover
----------  -------  ------  -------
R/g_km.R         +4       0  +0.18%
R/h_km.R         +3       0  +0.05%
TOTAL            +7       0  +0.00%

Results for commit: 9028697

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[Melkiades]

Any update on this? @donyunardi @shajoezhu?

[shajoezhu]

@copilot resolve the merge conflicts in this pull request

[Copilot]

@copilot resolve the merge conflicts in this pull request

✅ Merge conflicts resolved!

The branch has been successfully rebased onto the latest main. The changes are:

1. R/h_km.R: Added digits parameter (default: 4) to:

   • control_surv_med_annot() - exposes the setting via control interface
   • h_tbl_median_surv() - controls signif() precision for median and CI values

2. R/g_km.R: Updated to pass digits from control_annot_surv_med with fallback to 4

3. Tests: Added test coverage for the new digits parameter and updated snapshots

The roxygen documentation will be automatically generated and committed by CI. All changes maintain backward compatibility with the default value of 4.

Commits: c06ab3f, 54cf3da, 0dab2879

[shajoezhu]

see the following example to make it 3 digits

library(nestcolor)

data <- teal_data()
data <- within(data, {
  ADSL <- tmc_ex_adsl
  ADTTE <- tmc_ex_adtte
})
join_keys(data) <- default_cdisc_join_keys[names(data)]

ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]

arm_ref_comp <- list(
  ACTARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  ),
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  )
)

app <- init(
  data = data,
  modules = modules(
    tm_g_km(
      label = "Kaplan-Meier Plot",
      dataname = "ADTTE",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      paramcd = choices_selected(
        value_choices(ADTTE, "PARAMCD", "PARAM"),
        "OS"
      ),
      arm_ref_comp = arm_ref_comp,
      strata_var = choices_selected(
        variable_choices(ADSL, c("SEX", "BMRKR2")),
        "SEX"
      ),
      facet_var = choices_selected(
        variable_choices(ADSL, c("SEX", "BMRKR2")),
        NULL
      ),
      control_annot_surv_med = tern::control_surv_med_annot(digits = 3)
    )
  )
)
if (interactive()) {
  shinyApp(app$ui, app$server)
}

[shajoezhu]

the following example for 5 digits

default 4 digits

[donyunardi]

LGTM!