LGRT02

Multi-Variable Logistic Regression

---

Output

Multi-Variable Logistic Regression

Preview

model <- fit_logistic(
  adrs,
  variables = list(response = "Response", arm = "ARMCD", covariates = c("SEX", "AGE"))
)
conf_level <- 0.95
df <- broom::tidy(model, conf_level = conf_level)

# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")

result <- basic_table() %>%
  summarize_logistic(
    conf_level = conf_level,
    drop_and_remove_str = "_NA_"
  ) %>%
  append_topleft("Logistic regression") %>%
  build_table(df = df)
result

Logistic regression          Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio    Wald 95% CI    p-value
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Planned Arm Code                     2                                                                               0.0408 
  Reference ARM A, n = 134                                                                                                  
  ARM B, n = 134                     1                  -2.094             1.080           0.12      (0.01, 1.02)    0.0524 
  ARM C, n = 132                     1                  -0.074             1.423           0.93      (0.06, 15.09)   0.9584 
Sex                                                                                                                         
  Reference M, n = 169                                                                                                      
  F, n = 231                         1                  0.331              0.695           1.39      (0.36, 5.44)    0.6339 
Age                                                                                                                         
  Age                                1                  0.070              0.054           1.07      (0.97, 1.19)    0.1945 

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Experimental use!

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Multi-Variable Logistic Regression with Interaction Term

Preview

model <- fit_logistic(
  adrs,
  variables = list(
    response = "Response",
    arm = "ARMCD",
    covariates = c("SEX", "AGE"),
    interaction = "SEX"
  )
)
conf_level <- 0.95
df <- broom::tidy(model, conf_level = conf_level)

# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")

result <- basic_table() %>%
  summarize_logistic(
    conf_level = conf_level,
    drop_and_remove_str = "_NA_"
  ) %>%
  append_topleft("Logistic regression with interaction") %>%
  build_table(df = df)
result

Logistic regression with interaction    Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Age                                                                                                                                      
  Age                                           1                  0.067              0.054           1.07       (0.96, 1.19)     0.2084 
Planned Arm Code                                2                                                                                 0.4882 
  Reference ARM A, n = 134                                                                                                               
  ARM B, n = 134                                1                 -17.850            2362.767                                     0.9940 
    Sex                                                                                                                                  
      F                                                                                               0.23       (0.02, 2.11)            
      M                                                                                               0.00      (0.00, >999.99)          
  ARM C, n = 132                                1                 -16.442            2362.767                                     0.9944 
    Sex                                                                                                                                  
      F                                                                                             >999.99     (0.00, >999.99)          
      M                                                                                               0.00      (0.00, >999.99)          
Sex                                                                                                                                      
  Reference M, n = 169                                                                                                                   
  F, n = 231                                    1                 -16.044            2362.767                                     0.9946 
    Planned Arm Code                                                                                                                     
      ARM A                                                                                           0.00      (0.00, >999.99)          
      ARM B                                                                                           1.39       (0.29, 6.59)            
      ARM C                                                                                         >999.99     (0.00, >999.99)          
Interaction of Planned Arm Code * Sex           2                                                                                 0.9999 
  Reference ARM A or M, n = 248                                                                                                          
  ARM B * F, n = 82                             1                  16.373            2362.767                                     0.9945 
  ARM C * F, n = 70                             1                  32.492            3156.732                                     0.9918 

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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.

Multi-Variable Logistic Regression Specifying Covariates

Preview

model <- fit_logistic(
  adrs,
  variables = list(
    response = "Response",
    arm = "ARMCD",
    covariates = c("SEX", "AGE", "RACE")
  )
)
conf_level <- 0.95
df <- broom::tidy(model, conf_level = conf_level)

# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")

result <- basic_table() %>%
  summarize_logistic(
    conf_level = conf_level,
    drop_and_remove_str = "_NA_"
  ) %>%
  append_topleft("y ~ ARM + SEX + AGE + RACE") %>%
  build_table(df = df)
result

y ~ ARM + SEX + AGE + RACE                             Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 95% CI     p-value
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Planned Arm Code                                               2                                                                                 0.0346 
  Reference ARM A, n = 134                                                                                                                              
  ARM B, n = 134                                               1                  -2.162             1.084           0.12       (0.01, 0.96)     0.0461 
  ARM C, n = 132                                               1                  -0.090             1.426           0.91       (0.06, 14.97)    0.9499 
Sex                                                                                                                                                     
  Reference M, n = 169                                                                                                                                  
  F, n = 231                                                   1                  0.364              0.701           1.44       (0.36, 5.69)     0.6032 
Age                                                                                                                                                     
  Age                                                          1                  0.071              0.053           1.07       (0.97, 1.19)     0.1866 
Race                                                           5                                                                                 0.9685 
  Reference AMERICAN INDIAN OR ALASKA NATIVE, n = 25                                                                                                    
  ASIAN, n = 208                                               1                 -16.246            2017.122         0.00      (0.00, >999.99)   0.9936 
  BLACK OR AFRICAN AMERICAN, n = 91                            1                 -15.205            2017.122         0.00      (0.00, >999.99)   0.9940 
  WHITE, n = 74                                                1                 -15.955            2017.122         0.00      (0.00, >999.99)   0.9937 
  MULTIPLE, n = 1                                              1                  -0.363           10941.553         0.70      (0.00, >999.99)   1.0000 
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER, n = 1             1                  1.036            10941.553         2.82      (0.00, >999.99)   0.9999 

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.

Multi-Variable Logistic Regression Setting an Event, Alpha Level, and Level for Interaction

Preview

model <- fit_logistic(
  adrs,
  variables = list(
    response = "Response",
    arm = "ARMCD",
    covariates = c("SEX", "AGE"),
    interaction = "AGE"
  ),
  response_definition = "1 - response"
)
conf_level <- 0.9
df <- broom::tidy(model, conf_level = conf_level, at = c(30, 50))

# empty string flag
df <- df_explicit_na(df, na_level = "_NA_")

result <- basic_table() %>%
  summarize_logistic(
    conf_level = conf_level,
    drop_and_remove_str = "_NA_"
  ) %>%
  append_topleft("Estimations at age 30 and 50") %>%
  build_table(df = df)
result

Estimations at age 30 and 50            Degrees of Freedom   Parameter Estimate   Standard Error   Odds Ratio     Wald 90% CI      p-value
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Sex                                                                                                                                       
  Reference M, n = 169                                                                                                                    
  F, n = 231                                    1                  -0.381             0.710           0.68        (0.21, 2.20)     0.5915 
Planned Arm Code                                2                                                                                  0.2768 
  Reference ARM A, n = 134                                                                                                                
  ARM B, n = 134                                1                 -20.020             13.714                                       0.1443 
    Age                                                                                                                                   
      30                                                                                             234.91     (0.30, >999.99)           
      50                                                                                            >999.99     (0.04, >999.99)           
  ARM C, n = 132                                1                 -15.622             14.810                                       0.2915 
    Age                                                                                                                                   
      30                                                                                             31.95      (0.03, >999.99)           
      50                                                                                            >999.99     (<0.01, >999.99)          
Age                                                                                                                                       
  Age                                           1                  -0.877             0.581                                        0.1309 
    Planned Arm Code                                                                                                                      
      ARM A                                                                                           0.42        (0.16, 1.08)            
      ARM B                                                                                           0.97        (0.89, 1.06)            
      ARM C                                                                                           0.79        (0.55, 1.11)            
Interaction of Planned Arm Code * Age           2                                                                                  0.2213 
  Reference ARM A, n = 134                                                                                                                
  ARM B, n = 134                                1                  0.849              0.583                                        0.1449 
  ARM C, n = 132                                1                  0.636              0.618                                        0.3034 

Try this using WebR

Experimental use!

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Data Setup

library(dplyr)
library(tern)

adsl <- random.cdisc.data::cadsl
adrs <- random.cdisc.data::cadrs

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

adsl <- adsl %>%
  dplyr::filter(SEX %in% c("F", "M"))

adrs <- adrs %>%
  dplyr::filter(PARAMCD == "BESRSPI") %>%
  dplyr::mutate(
    Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
    SEX = factor(SEX, c("M", "F")),
    RACE = factor(
      RACE,
      levels = c(
        "AMERICAN INDIAN OR ALASKA NATIVE", "ASIAN", "BLACK OR AFRICAN AMERICAN",
        "WHITE", "MULTIPLE", "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER"
      )
    )
  ) %>%
  var_relabel(Response = "Response", SEX = "Sex", RACE = "Race")

teal App

Preview

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADRS <- random.cdisc.data::cadrs %>%
    filter(PARAMCD %in% c("BESRSPI", "INVET"))
})
datanames <- c("ADSL", "ADRS")
datanames(data) <- datanames

Warning: `datanames<-()` was deprecated in teal.data 0.6.1.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data

join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADRS <- data[["ADRS"]]
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")
  )
)

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_logistic(
      label = "Logistic Regression",
      dataname = "ADRS",
      arm_var = choices_selected(
        choices = variable_choices(ADRS, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        choices = value_choices(ADRS, "PARAMCD", "PARAM"),
        selected = "BESRSPI"
      ),
      cov_var = choices_selected(
        choices = c("SEX", "AGE", "BMRKR1", "BMRKR2"),
        selected = "SEX"
      )
    )
  )
)

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.

#| '!! 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://pharmaverse.r-universe.dev", getOption("webr_pkg_repos")))

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

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADRS <- random.cdisc.data::cadrs %>%
    filter(PARAMCD %in% c("BESRSPI", "INVET"))
})
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(
  ACTARMCD = 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_logistic(
      label = "Logistic Regression",
      dataname = "ADRS",
      arm_var = choices_selected(
        choices = variable_choices(ADRS, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        choices = value_choices(ADRS, "PARAMCD", "PARAM"),
        selected = "BESRSPI"
      ),
      cov_var = choices_selected(
        choices = c("SEX", "AGE", "BMRKR1", "BMRKR2"),
        selected = "SEX"
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-12-21 17:45:22 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     2024-12-21
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
 abind                   1.4-8       2024-09-12 [1] RSPM
 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
 car                     3.1-3       2024-09-27 [1] RSPM
 carData                 3.0-5       2022-01-06 [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                 1.10.6      2024-12-12 [1] RSPM
 estimability            1.5.1       2024-05-12 [1] RSPM
 evaluate                1.0.1       2024-10-10 [1] RSPM
 fastmap                 1.2.0       2024-05-15 [1] RSPM
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 formatR                 1.14        2023-01-17 [1] CRAN (R 4.4.1)
 formatters            * 0.5.9.9008  2024-12-21 [1] https://p~
 Formula                 1.2-5       2023-02-24 [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
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 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                   1.4.1       2024-11-27 [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
 nestcolor               0.1.2.9017  2024-12-21 [1] https://p~
 nlme                    3.1-166     2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.10.0      2024-12-17 [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                1.3.2       2024-11-28 [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       0.3.16.9001 2024-12-21 [1] https://p~
 rbibutils               2.3         2024-10-04 [1] RSPM
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 rmarkdown               2.29        2024-11-04 [1] RSPM
 rtables               * 0.6.10.9014 2024-12-21 [1] https://p~
 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                 * 1.10.0      2024-12-14 [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                3.8-3       2024-12-17 [1] RSPM
 teal                  * 0.15.2.9097 2024-12-21 [1] https://p~
 teal.code             * 0.5.0.9021  2024-12-21 [1] https://p~
 teal.data             * 0.6.0.9023  2024-12-21 [1] https://p~
 teal.logger             0.3.0.9003  2024-12-21 [1] https://p~
 teal.modules.clinical * 0.9.1.9043  2024-12-21 [1] https://p~
 teal.reporter           0.3.1.9021  2024-12-21 [1] https://p~
 teal.slice            * 0.5.1.9019  2024-12-21 [1] https://p~
 teal.transform        * 0.5.0.9018  2024-12-21 [1] https://p~
 teal.widgets            0.4.2.9025  2024-12-21 [1] https://p~
 tern                  * 0.9.6.9017  2024-12-21 [1] https://p~
 tern.gee                0.1.5.9004  2024-12-21 [1] https://p~
 testthat                3.2.2       2024-12-10 [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
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 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

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

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