RSPT01 Binary Outcomes Summary — rspt01

RSPT01 template may be used to summarize any binary outcome or response variable at a single time point. Typical application for oncology

Usage

rspt01_main(
  adam_db,
  dataset = "adrs",
  arm_var = "ARM",
  ref_group = NULL,
  odds_ratio = TRUE,
  perform_analysis = "unstrat",
  strata = NULL,
  conf_level = 0.95,
  methods = list(),
  ...
)

rspt01_pre(adam_db, ...)

rspt01_post(tlg, prune_0 = TRUE, ...)

rspt01

Format

An object of class chevron_t of length 1.

Arguments

adam_db: (list of data.frames) object containing the ADaM datasets
dataset: (string) the name of a table in the adam_db object.
arm_var: (string) variable used for column splitting
ref_group: (string) The name of the reference group, the value should be identical to the values in arm_var, if not specified, it will by default use the first level or value of arm_var.
odds_ratio: (flag) should the odds ratio be calculated, default is TRUE
perform_analysis: (string) option to display statistical comparisons using stratified analyses, or unstratified analyses, or both, e.g. c("unstrat", "strat"). Only unstratified will be displayed by default
strata: (string) stratification factors, e.g. strata = c("STRATA1", "STRATA2"), by default as NULL
conf_level: (numeric) the level of confidence interval, default is 0.95.
methods: (list) a named list, use a named list to control, for example: methods = list(prop_conf_method = "wald", diff_conf_method = "wald", strat_diff_conf_method = "ha", diff_pval_method = "fisher", strat_diff_pval_method = "schouten") prop_conf_method controls the methods of calculating proportion confidence interval, diff_conf_method controls the methods of calculating unstratified difference confidence interval, strat_diff_conf_method controls the methods of calculating stratified difference confidence interval, diff_pval_method controls the methods of calculating unstratified p-value for odds ratio, strat_diff_pval_method controls the methods of calculating stratified p-value for odds ratio, see more details in tern
...: not used.
tlg: (TableTree, Listing or ggplot) object typically produced by a main function.
prune_0: (flag) remove 0 count rows

Details

No overall value.

Functions

rspt01_main(): Main TLG function
rspt01_pre(): Preprocessing
rspt01_post(): Postprocessing

Examples

library(dplyr)
library(dunlin)

proc_data <- log_filter(syn_data, PARAMCD == "BESRSPI", "adrs")

run(rspt01, proc_data)
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#>                                          A: Drug X          B: Placebo         C: Combination   
#>                                           (N=134)            (N=134)               (N=132)      
#>   ——————————————————————————————————————————————————————————————————————————————————————————————
#>   Responders                            133 (99.3%)        127 (94.8%)           131 (99.2%)    
#>   95% CI (Wald, with correction)       (97.4, 100.0)       (90.6, 98.9)         (97.4, 100.0)   
#>   Unstratified Analysis                                                                         
#>     Difference in Response rate (%)                            -4.5                 -0.0        
#>       95% CI (Wald, with correction)                       (-9.3, 0.3)           (-2.8, 2.8)    
#>     p-value (Chi-Squared Test)                                0.0313               0.9915       
#>   Odds Ratio (95% CI)                                   0.14 (0.02 - 1.12)   0.98 (0.06 - 15.91)
#>   Complete Response (CR)                119 (88.8%)         97 (72.4%)           120 (90.9%)    
#>     95% CI (Wald, with correction)     (83.09, 94.52)     (64.45, 80.33)       (85.63, 96.19)   
#>   Partial Response (PR)                  14 (10.4%)         30 (22.4%)            11 (8.3%)     
#>     95% CI (Wald, with correction)     (4.90, 16.00)      (14.96, 29.82)        (3.24, 13.43)   
#>   Stable Disease (SD)                     1 (0.7%)           7 (5.2%)             1 (0.8%)      
#>     95% CI (Wald, with correction)      (0.00, 2.58)       (1.08, 9.36)         (0.00, 2.62)    

run(rspt01, proc_data,
  odds_ratio = FALSE, perform_analysis = c("unstrat", "strat"),
  strata = c("STRATA1", "STRATA2"), methods = list(diff_pval_method = "fisher")
)
#>                                                A: Drug X        B: Placebo     C: Combination
#>                                                 (N=134)          (N=134)          (N=132)    
#>   ———————————————————————————————————————————————————————————————————————————————————————————
#>   Responders                                  133 (99.3%)      127 (94.8%)      131 (99.2%)  
#>   95% CI (Wald, with correction)             (97.4, 100.0)     (90.6, 98.9)    (97.4, 100.0) 
#>   Unstratified Analysis                                                                      
#>     Difference in Response rate (%)                                -4.5             -0.0     
#>       95% CI (Wald, with correction)                           (-9.3, 0.3)      (-2.8, 2.8)  
#>     p-value (Fisher's Exact Test)                                 0.0662           1.0000    
#>   Stratified Analysis                                                                        
#>     Difference in Response rate (%)                                -4.4             0.1      
#>       95% CI (CMH, without correction)                         (-8.5, -0.3)     (-2.2, 2.3)  
#>     p-value (Cochran-Mantel-Haenszel Test)                        0.0344           0.9560    
#>   Complete Response (CR)                      119 (88.8%)       97 (72.4%)      120 (90.9%)  
#>     95% CI (Wald, with correction)           (83.09, 94.52)   (64.45, 80.33)   (85.63, 96.19)
#>   Partial Response (PR)                        14 (10.4%)       30 (22.4%)       11 (8.3%)   
#>     95% CI (Wald, with correction)           (4.90, 16.00)    (14.96, 29.82)   (3.24, 13.43) 
#>   Stable Disease (SD)                           1 (0.7%)         7 (5.2%)         1 (0.8%)   
#>     95% CI (Wald, with correction)            (0.00, 2.58)     (1.08, 9.36)     (0.00, 2.62)