CMHT01

Cochran-Mantel-Haenszel (CMH) Summary

---

Table of Single Parameter

Preview

anl_01 <- adqs %>%
  filter(PARAMCD == "FKSI-FWB" & AVISIT == "WEEK 1 DAY 8") %>%
  mutate(is_rsp = PCHG > 15) %>%
  mutate(ARM = relevel(ARM, ref = "A: Drug X")) %>%
  var_relabel(ARM = "Description of Planned Arm") %>%
  mutate(strata = interaction(STRATA1, STRATA2, drop = TRUE))

lyt_01 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM", ref_group = "A: Drug X") %>%
  estimate_proportion(vars = "is_rsp", table_names = "est_prop") %>%
  estimate_proportion_diff(
    var_labels = "Unstratified Analysis",
    vars = "is_rsp",
    show_labels = "visible",
    table_names = "est_prop_diff"
  ) %>%
  test_proportion_diff(vars = "is_rsp", table_names = "test_prop") %>%
  estimate_odds_ratio(vars = "is_rsp", table_names = "est_or") %>%
  estimate_proportion_diff(
    var_labels = "Stratified Analysis",
    vars = "is_rsp",
    show_labels = "visible",
    method = "cmh",
    variables = list(strata = "strata"),
    table_names = "est_prop_diff_strat"
  ) %>%
  test_proportion_diff(
    vars = "is_rsp",
    method = "cmh",
    variables = list(strata = "strata"),
    table_names = "test_prop_strat"
  ) %>%
  estimate_odds_ratio(
    vars = "is_rsp",
    variables = list(strata = "strata", arm = "ARM"),
    table_names = "est_or_strat"
  )

result <- build_table(
  lyt = lyt_01,
  df = anl_01,
  alt_counts_df = adsl
)
result

                                            A: Drug X         B: Placebo         C: Combination  
                                             (N=134)           (N=134)              (N=132)      
—————————————————————————————————————————————————————————————————————————————————————————————————
Responders                                  49 (36.6%)        51 (38.1%)           47 (35.6%)    
95% CI (Wald, with correction)             (28.0, 45.1)      (29.5, 46.7)         (27.1, 44.2)   
Unstratified Analysis                                                                            
  Difference in Response rate (%)                                1.5                  -1.0       
    95% CI (Wald, with correction)                          (-10.8, 13.8)        (-13.3, 11.3)   
  p-value (Chi-Squared Test)                                    0.8006               0.8704      
Odds Ratio (95% CI)                                       1.07 (0.65 - 1.75)   0.96 (0.58 - 1.58)
Stratified Analysis                                                                              
  Difference in Response rate (%)                                2.8                  -1.5       
    95% CI (CMH, without correction)                         (-8.2, 13.8)         (-13.0, 9.9)   
  p-value (Cochran-Mantel-Haenszel Test)                        0.6397               0.7952      
Odds Ratio (95% CI)                                       1.12 (0.68 - 1.83)   0.99 (0.60 - 1.64)

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Table of Multiple Parameters

Preview

anl_02 <- adqs %>%
  filter(AVISIT == "WEEK 1 DAY 8") %>%
  mutate(is_rsp = PCHG > 15) %>%
  mutate(PARAM = droplevels(PARAM)) %>%
  mutate(ARM = relevel(ARM, ref = "A: Drug X")) %>%
  var_relabel(ARM = "Description of Planned Arm") %>%
  mutate(strata = interaction(STRATA1, STRATA2, drop = TRUE))

var_labels(anl_02)["PARAM"] <- "Parameter"

split_fun <- drop_split_levels

lyt_02 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM", ref_group = "A: Drug X") %>%
  split_rows_by(
    var = "PARAM",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(anl_02$PARAM)
  ) %>%
  estimate_proportion(vars = "is_rsp", table_names = "est_prop") %>%
  estimate_proportion_diff(
    var_labels = "Unstratified Analysis",
    vars = "is_rsp",
    show_labels = "visible",
    table_names = "est_prop_diff"
  ) %>%
  test_proportion_diff(vars = "is_rsp", table_names = "test_prop") %>%
  estimate_odds_ratio(vars = "is_rsp", table_names = "est_or") %>%
  estimate_proportion_diff(
    var_labels = "Stratified Analysis",
    vars = "is_rsp",
    show_labels = "visible",
    method = "cmh",
    variables = list(strata = "strata"),
    table_names = "est_prop_diff_strat"
  ) %>%
  test_proportion_diff(
    vars = "is_rsp",
    method = "cmh",
    variables = list(strata = "strata"),
    table_names = "test_prop_strat"
  ) %>%
  estimate_odds_ratio(
    vars = "is_rsp",
    variables = list(strata = "strata", arm = "ARM"),
    table_names = "est_or_strat"
  )

result <- build_table(
  lyt = lyt_02,
  df = anl_02,
  alt_counts_df = adsl
)
result

                                              A: Drug X         B: Placebo         C: Combination  
Parameter                                      (N=134)           (N=134)              (N=132)      
———————————————————————————————————————————————————————————————————————————————————————————————————
BFI All Questions                                                                                  
  Responders                                  57 (42.5%)        65 (48.5%)           50 (37.9%)    
  95% CI (Wald, with correction)             (33.8, 51.3)      (39.7, 57.3)         (29.2, 46.5)   
  Unstratified Analysis                                                                            
    Difference in Response rate (%)                                6.0                  -4.7       
      95% CI (Wald, with correction)                           (-6.7, 18.6)         (-17.2, 7.9)   
    p-value (Chi-Squared Test)                                    0.3264               0.4385      
  Odds Ratio (95% CI)                                       1.27 (0.79 - 2.06)   0.82 (0.50 - 1.35)
  Stratified Analysis                                                                              
    Difference in Response rate (%)                                6.7                  -5.1       
      95% CI (CMH, without correction)                         (-5.0, 18.4)         (-16.8, 6.6)   
    p-value (Cochran-Mantel-Haenszel Test)                        0.2781               0.4056      
  Odds Ratio (95% CI)                                       1.30 (0.80 - 2.11)   0.80 (0.49 - 1.32)
Fatigue Interference                                                                               
  Responders                                  66 (49.3%)        50 (37.3%)           48 (36.4%)    
  95% CI (Wald, with correction)             (40.4, 58.1)      (28.8, 45.9)         (27.8, 44.9)   
  Unstratified Analysis                                                                            
    Difference in Response rate (%)                               -11.9                -12.9       
      95% CI (Wald, with correction)                           (-24.5, 0.6)        (-25.4, -0.3)   
    p-value (Chi-Squared Test)                                    0.0485               0.0337      
  Odds Ratio (95% CI)                                       0.61 (0.38 - 1.00)   0.59 (0.36 - 0.96)
  Stratified Analysis                                                                              
    Difference in Response rate (%)                               -12.0                -13.3       
      95% CI (CMH, without correction)                        (-23.7, -0.2)        (-24.9, -1.6)   
    p-value (Cochran-Mantel-Haenszel Test)                        0.0518               0.0303      
  Odds Ratio (95% CI)                                       0.61 (0.37 - 0.99)   0.58 (0.36 - 0.96)
FKSI-19 All Questions                                                                              
  Responders                                  60 (44.8%)        57 (42.5%)           68 (51.5%)    
  95% CI (Wald, with correction)             (36.0, 53.6)      (33.8, 51.3)         (42.6, 60.4)   
  Unstratified Analysis                                                                            
    Difference in Response rate (%)                                -2.2                 6.7        
      95% CI (Wald, with correction)                          (-14.9, 10.4)         (-6.0, 19.5)   
    p-value (Chi-Squared Test)                                    0.7118               0.2714      
  Odds Ratio (95% CI)                                       0.91 (0.56 - 1.48)   1.31 (0.81 - 2.12)
  Stratified Analysis                                                                              
    Difference in Response rate (%)                                -2.8                 5.6        
      95% CI (CMH, without correction)                         (-14.3, 8.8)         (-6.2, 17.4)   
    p-value (Cochran-Mantel-Haenszel Test)                        0.6442               0.3683      
  Odds Ratio (95% CI)                                       0.90 (0.56 - 1.47)   1.26 (0.77 - 2.04)
Function/Well-Being (GF1,GF3,GF7)                                                                  
  Responders                                  49 (36.6%)        51 (38.1%)           47 (35.6%)    
  95% CI (Wald, with correction)             (28.0, 45.1)      (29.5, 46.7)         (27.1, 44.2)   
  Unstratified Analysis                                                                            
    Difference in Response rate (%)                                1.5                  -1.0       
      95% CI (Wald, with correction)                          (-10.8, 13.8)        (-13.3, 11.3)   
    p-value (Chi-Squared Test)                                    0.8006               0.8704      
  Odds Ratio (95% CI)                                       1.07 (0.65 - 1.75)   0.96 (0.58 - 1.58)
  Stratified Analysis                                                                              
    Difference in Response rate (%)                                2.8                  -1.5       
      95% CI (CMH, without correction)                         (-8.2, 13.8)         (-13.0, 9.9)   
    p-value (Cochran-Mantel-Haenszel Test)                        0.6397               0.7952      
  Odds Ratio (95% CI)                                       1.12 (0.68 - 1.83)   0.99 (0.60 - 1.64)
Treatment Side Effects (GP2,C5,GP5)                                                                
  Responders                                  62 (46.3%)        49 (36.6%)           51 (38.6%)    
  95% CI (Wald, with correction)             (37.5, 55.1)      (28.0, 45.1)         (30.0, 47.3)   
  Unstratified Analysis                                                                            
    Difference in Response rate (%)                                -9.7                 -7.6       
      95% CI (Wald, with correction)                           (-22.2, 2.8)         (-20.2, 5.0)   
    p-value (Chi-Squared Test)                                    0.1069               0.2080      
  Odds Ratio (95% CI)                                       0.67 (0.41 - 1.09)   0.73 (0.45 - 1.19)
  Stratified Analysis                                                                              
    Difference in Response rate (%)                                -9.6                 -7.1       
      95% CI (CMH, without correction)                         (-21.0, 1.8)         (-18.8, 4.6)   
    p-value (Cochran-Mantel-Haenszel Test)                        0.1168               0.2426      
  Odds Ratio (95% CI)                                       0.67 (0.41 - 1.10)   0.73 (0.45 - 1.19)

Try this using WebR

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Data Setup

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adqs <- random.cdisc.data::cadqs

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adqs <- df_explicit_na(adqs)

teal App

Preview

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADRS <- random.cdisc.data::cadrs
})
datanames <- c("ADSL", "ADRS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADRS <- data[["ADRS"]]
arm_ref_comp <- list(
  ARMCD = list(ref = "ARM B", comp = c("ARM A", "ARM C")),
  ARM = list(ref = "B: Placebo", comp = c("A: Drug X", "C: Combination"))
)

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_binary_outcome(
      label = "Responders",
      dataname = "ADRS",
      paramcd = choices_selected(
        choices = value_choices(ADRS, "PARAMCD", "PARAM"),
        selected = "BESRSPI"
      ),
      arm_var = choices_selected(
        choices = variable_choices(ADRS, c("ARM", "ARMCD", "ACTARMCD")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      strata_var = choices_selected(
        choices = variable_choices(ADRS, c("SEX", "BMRKR2")),
        select = NULL
      )
    )
  )
)

shinyApp(app$ui, app$server)

Try this using shinylive

Experimental use!

shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.

#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://pharmaverse.r-universe.dev", getOption("webr_pkg_repos")))

# -- APP CODE --
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADRS <- random.cdisc.data::cadrs
})
datanames <- c("ADSL", "ADRS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADRS <- data[["ADRS"]]
arm_ref_comp <- list(
  ARMCD = list(ref = "ARM B", comp = c("ARM A", "ARM C")),
  ARM = list(ref = "B: Placebo", comp = c("A: Drug X", "C: Combination"))
)

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_binary_outcome(
      label = "Responders",
      dataname = "ADRS",
      paramcd = choices_selected(
        choices = value_choices(ADRS, "PARAMCD", "PARAM"),
        selected = "BESRSPI"
      ),
      arm_var = choices_selected(
        choices = variable_choices(ADRS, c("ARM", "ARMCD", "ACTARMCD")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      strata_var = choices_selected(
        choices = variable_choices(ADRS, c("SEX", "BMRKR2")),
        select = NULL
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 17:43:43 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
 backports               1.5.0       2024-05-23 [1] CRAN (R 4.4.1)
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 MASS                    7.3-61      2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-0       2024-04-26 [1] CRAN (R 4.4.1)
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 mvtnorm                 1.3-1       2024-09-03 [1] CRAN (R 4.4.1)
 nestcolor               0.1.2.9017  2024-11-02 [1] git2r (https://github.com/insightsengineering/nestcolor.git@3d2ce62)
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 processx                3.8.4       2024-03-16 [1] CRAN (R 4.4.1)
 promises                1.3.0       2024-04-05 [1] CRAN (R 4.4.1)
 ps                      1.8.0       2024-09-12 [1] CRAN (R 4.4.1)
 purrr                   1.0.2       2023-08-10 [1] CRAN (R 4.4.1)
 R6                      2.5.1       2021-08-19 [1] CRAN (R 4.4.1)
 random.cdisc.data       0.3.16.9001 2024-11-02 [1] git2r (https://github.com/insightsengineering/random.cdisc.data.git@a65bfbe)
 rbibutils               2.3         2024-10-04 [1] CRAN (R 4.4.1)
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 rlang                   1.1.4       2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown               2.28        2024-08-17 [1] CRAN (R 4.4.1)
 rtables               * 0.6.10.9002 2024-11-02 [1] git2r (https://github.com/insightsengineering/rtables.git@d2cc7a0)
 sandwich                3.1-1       2024-09-15 [1] CRAN (R 4.4.1)
 sass                    0.4.9       2024-03-15 [1] CRAN (R 4.4.1)
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 shiny                 * 1.9.1       2024-08-01 [1] CRAN (R 4.4.1)
 shinycssloaders         1.1.0       2024-07-30 [1] CRAN (R 4.4.1)
 shinyjs                 2.1.0       2021-12-23 [1] CRAN (R 4.4.1)
 shinyvalidate           0.1.3       2023-10-04 [1] CRAN (R 4.4.1)
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 stringr                 1.5.1       2023-11-14 [1] CRAN (R 4.4.1)
 survival                3.7-0       2024-06-05 [2] CRAN (R 4.4.1)
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 teal.code             * 0.5.0.9012  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.code.git@86aa265)
 teal.data             * 0.6.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.data.git@eca9695)
 teal.logger             0.3.0.9000  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.logger.git@cd9cc7e)
 teal.modules.clinical * 0.9.1.9030  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.modules.clinical.git@5cf76ac)
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 websocket               1.4.2       2024-07-22 [1] CRAN (R 4.4.1)
 withr                   3.0.1       2024-07-31 [1] CRAN (R 4.4.1)
 xfun                    0.48        2024-10-03 [1] CRAN (R 4.4.1)
 xtable                  1.8-4       2019-04-21 [1] CRAN (R 4.4.1)
 yaml                    2.3.10      2024-07-26 [1] CRAN (R 4.4.1)
 zoo                     1.8-12      2023-04-13 [1] CRAN (R 4.4.1)

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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.lock file

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