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Expose digits argument for h_tbl_median_surv#1469

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Expose digits argument for h_tbl_median_surv#1469
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copilot/expose-digits-argument-h-tbl-median-surv

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Copilot AI commented Feb 12, 2026 •
edited by Melkiades
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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.

Copilot AI changed the title [WIP] Expose digits argument for h_tbl_median_surv function Expose digits argument for h_tbl_median_surv Feb 12, 2026
Copilot AI requested a review from shajoezhu February 12, 2026 17:35
@shajoezhu shajoezhu marked this pull request as ready for review February 12, 2026 18:37
@shajoezhu shajoezhu requested a review from donyunardi February 12, 2026 18:37
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github-actions Bot commented Feb 12, 2026 •
edited
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Unit Tests Summary

    1 files     85 suites   1m 38s ⏱️
  910 tests   901 ✅   9 💤 0 ❌
2 266 runs  1 553 ✅ 713 💤 0 ❌

Results for commit 77003a9.

♻️ This comment has been updated with latest results.

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github-actions Bot commented Feb 12, 2026 •
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Unit Test Performance Difference

Additional test case details
Test Suite $Status$ Time on main $±Time$ Test Case
h_km 👶 $+0.02$ control_surv_med_annot_works_with_custom_digits
h_km 👶 $+0.04$ h_tbl_median_surv_respects_digits_parameter

Results for commit 8eedcbd

♻️ This comment has been updated with latest results.

donyunardi
donyunardi requested changes Feb 13, 2026
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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.

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Melkiades commented May 12, 2026

is this still active @shajoezhu @donyunardi?

@Melkiades
Add digits argument to h_tbl_median_surv and control_surv_med_annot
1b7a759 
Expose digits parameter (default 4) to control signif() precision
for median survival time and CI values in the annotation table.
The parameter is passed through from g_km() via control_annot_surv_med.
@Melkiades Melkiades force-pushed the copilot/expose-digits-argument-h-tbl-median-surv branch from 344bf69 to 1b7a759 Compare May 20, 2026 15:12
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Melkiades commented May 20, 2026

@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)

Melkiades added 3 commits May 20, 2026 20:43
@Melkiades
lintr
5f0efdf
@Melkiades
snap
483ecd4
@Melkiades
Replace deprecated case_match() with recode_values() in test
2bc03d1 
dplyr 1.2.0 deprecated case_match() in favor of recode_values().
@Melkiades Melkiades requested a review from donyunardi May 20, 2026 18:49
@Melkiades Melkiades enabled auto-merge (squash) May 20, 2026 18:50
@Melkiades Melkiades disabled auto-merge May 21, 2026 08:36
Melkiades added 2 commits May 21, 2026 10:39
@Melkiades
Merge branch 'main' into copilot/expose-digits-argument-h-tbl-median-…
0d1d5c6 
…surv

Signed-off-by: Davide Garolini <davide.garolini@roche.com>
@Melkiades
snaps
77003a9
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github-actions Bot commented May 21, 2026

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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                                    167       7  95.81%   191-195, 238, 253, 261, 267-268
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                            30       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                     183       2  98.91%   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                                     509      39  92.34%   146, 198-203, 296, 387, 389-390, 401-403, 422, 429-430, 432-434, 442-444, 469, 474-477, 660-663, 1117-1126
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                                   180       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                           174       2  98.85%   264, 266
R/prop_diff.R                                495      21  95.76%   95-99, 136, 332, 334, 420-427, 576, 849, 1021, 1025, 1028
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                         150       2  98.67%   327-328
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                            120       1  99.17%   251
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                                      12255     594  95.15%

Diff against main

Filename      Stmts    Miss  Cover
----------  -------  ------  -------
R/g_km.R         +4       0  +0.18%
R/h_km.R          0      -2  +0.39%
TOTAL            +4      -2  +0.02%

Results for commit: 77003a9

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

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[Feature Request]: Expose the digits argument in tern::h_tbl_median_surv()

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@Melkiades @donyunardi @shajoezhu