CMHT01

Cochran-Mantel-Haenszel (CMH) Summary

Output

Preview
Try this using WebR

Code

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)

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.

Preview
Try this using WebR

Code

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)

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.

Code

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)

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)

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.

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://insightsengineering.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] "2024-11-20 09:22:00 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     2024-11-20
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
 backports               1.5.0    2024-05-23 [1] RSPM
 brio                    1.1.5    2024-04-24 [1] RSPM
 broom                   1.0.7    2024-09-26 [1] RSPM
 bslib                   0.8.0    2024-07-29 [1] RSPM
 cachem                  1.1.0    2024-05-16 [1] RSPM
 callr                   3.7.6    2024-03-25 [1] RSPM
 checkmate               2.3.2    2024-07-29 [1] RSPM
 chromote                0.3.1    2024-08-30 [1] RSPM
 cli                     3.6.3    2024-06-21 [1] RSPM
 coda                    0.19-4.1 2024-01-31 [1] CRAN (R 4.4.1)
 codetools               0.2-20   2024-03-31 [2] CRAN (R 4.4.1)
 colorspace              2.1-1    2024-07-26 [1] RSPM
 curl                    6.0.1    2024-11-14 [1] RSPM
 digest                  0.6.37   2024-08-19 [1] RSPM
 dplyr                 * 1.1.4    2023-11-17 [1] RSPM
 emmeans                 1.10.5   2024-10-14 [1] RSPM
 estimability            1.5.1    2024-05-12 [1] RSPM
 evaluate                1.0.1    2024-10-10 [1] RSPM
 fansi                   1.0.6    2023-12-08 [1] RSPM
 fastmap                 1.2.0    2024-05-15 [1] RSPM
 fontawesome             0.5.3    2024-11-16 [1] RSPM
 forcats                 1.0.0    2023-01-29 [1] RSPM
 formatR                 1.14     2023-01-17 [1] CRAN (R 4.4.1)
 formatters            * 0.5.9    2024-09-12 [1] RSPM
 geepack                 1.3.12   2024-09-23 [1] RSPM
 generics                0.1.3    2022-07-05 [1] RSPM
 ggplot2                 3.5.1    2024-04-23 [1] RSPM
 glue                    1.8.0    2024-09-30 [1] RSPM
 gtable                  0.3.6    2024-10-25 [1] RSPM
 htmltools               0.5.8.1  2024-04-04 [1] RSPM
 htmlwidgets             1.6.4    2023-12-06 [1] RSPM
 httpuv                  1.6.15   2024-03-26 [1] RSPM
 jquerylib               0.1.4    2021-04-26 [1] RSPM
 jsonlite                1.8.9    2024-09-20 [1] RSPM
 knitr                   1.49     2024-11-08 [1] RSPM
 later                   1.3.2    2023-12-06 [1] RSPM
 lattice                 0.22-6   2024-03-20 [2] CRAN (R 4.4.1)
 lifecycle               1.0.4    2023-11-07 [1] RSPM
 logger                  0.4.0    2024-10-22 [1] RSPM
 magrittr              * 2.0.3    2022-03-30 [1] RSPM
 MASS                    7.3-61   2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-1    2024-10-18 [1] RSPM
 memoise                 2.0.1    2021-11-26 [1] RSPM
 mime                    0.12     2021-09-28 [1] RSPM
 multcomp                1.4-26   2024-07-18 [1] CRAN (R 4.4.1)
 munsell                 0.5.1    2024-04-01 [1] RSPM
 mvtnorm                 1.3-2    2024-11-04 [1] RSPM
 nlme                    3.1-166  2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.9.0    2023-03-22 [1] RSPM
 pkgcache                2.2.3    2024-09-12 [1] RSPM
 pkgconfig               2.0.3    2019-09-22 [1] RSPM
 processx                3.8.4    2024-03-16 [1] RSPM
 promises                1.3.0    2024-04-05 [1] RSPM
 ps                      1.8.1    2024-10-28 [1] RSPM
 purrr                   1.0.2    2023-08-10 [1] RSPM
 R6                      2.5.1    2021-08-19 [1] RSPM
 random.cdisc.data       0.3.16   2024-10-10 [1] RSPM
 rbibutils               2.3      2024-10-04 [1] RSPM
 Rcpp                    1.0.13-1 2024-11-02 [1] RSPM
 Rdpack                  2.6.2    2024-11-15 [1] RSPM
 rlang                   1.1.4    2024-06-04 [1] RSPM
 rmarkdown               2.29     2024-11-04 [1] RSPM
 rtables               * 0.6.10   2024-09-20 [1] RSPM
 sandwich                3.1-1    2024-09-15 [1] CRAN (R 4.4.1)
 sass                    0.4.9    2024-03-15 [1] RSPM
 scales                  1.3.0    2023-11-28 [1] RSPM
 sessioninfo             1.2.2    2021-12-06 [1] any (@1.2.2)
 shiny                 * 1.9.1    2024-08-01 [1] RSPM
 shinycssloaders         1.1.0    2024-07-30 [1] RSPM
 shinyjs                 2.1.0    2021-12-23 [1] RSPM
 shinyvalidate           0.1.3    2023-10-04 [1] RSPM
 shinyWidgets            0.8.7    2024-09-23 [1] RSPM
 stringi                 1.8.4    2024-05-06 [1] RSPM
 stringr                 1.5.1    2023-11-14 [1] RSPM
 survival                3.7-0    2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2   2024-03-07 [1] RSPM
 teal.code             * 0.5.0    2024-01-11 [1] RSPM
 teal.data             * 0.6.0    2024-04-30 [1] RSPM
 teal.logger             0.3.0    2024-10-24 [1] RSPM
 teal.modules.clinical * 0.9.1    2024-04-27 [1] RSPM
 teal.reporter           0.3.1    2024-03-15 [1] RSPM
 teal.slice            * 0.5.1    2024-04-30 [1] RSPM
 teal.transform        * 0.5.0    2024-02-17 [1] RSPM
 teal.widgets            0.4.2    2023-12-14 [1] RSPM
 tern                  * 0.9.6    2024-09-24 [1] RSPM
 tern.gee                0.1.5    2024-08-23 [1] RSPM
 testthat                3.2.1.1  2024-04-14 [1] RSPM
 TH.data                 1.1-2    2023-04-17 [1] CRAN (R 4.4.1)
 tibble                  3.2.1    2023-03-20 [1] RSPM
 tidyr                   1.3.1    2024-01-24 [1] RSPM
 tidyselect              1.2.1    2024-03-11 [1] RSPM
 utf8                    1.2.4    2023-10-22 [1] RSPM
 vctrs                   0.6.5    2023-12-01 [1] RSPM
 webshot                 0.5.5    2023-06-26 [1] CRAN (R 4.4.1)
 webshot2                0.1.1    2023-08-11 [1] RSPM
 websocket               1.4.2    2024-07-22 [1] RSPM
 withr                   3.0.2    2024-10-28 [1] RSPM
 xfun                    0.49     2024-10-31 [1] RSPM
 xtable                  1.8-4    2019-04-21 [1] RSPM
 yaml                    2.3.10   2024-07-26 [1] RSPM
 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

──────────────────────────────────────────────────────────────────────────────

`.lock` file

Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

Download

Output

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file