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

Value

the main function returns an rtables object.

the preprocessing function returns a list of data.frame.

the postprocessing function returns an rtables object or an ElementaryTable (null report).

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
#>                                           A: Drug X         B: Placebo        C: Combination  
#>                                            (N=15)             (N=15)              (N=15)      
#>   ————————————————————————————————————————————————————————————————————————————————————————————
#>   Responders                             15 (100.0%)        14 (93.3%)          15 (100.0%)   
#>   95% CI (Wald, with correction)        (96.7, 100.0)      (77.4, 100.0)       (96.7, 100.0)  
#>   Unstratified Analysis                                                                       
#>     Difference in Response rate (%)                            -6.7                 0.0       
#>       95% CI (Wald, with correction)                       (-26.0, 12.6)        (-6.7, 6.7)   
#>     p-value (Chi-Squared Test)                                0.3091              1.0000      
#>   Odds Ratio (95% CI)                                    0.00 (0.00 - Inf)   1.00 (0.00 - Inf)
#>   Complete Response (CR)                 15 (100.0%)        11 (73.3%)          14 (93.3%)    
#>     95% CI (Wald, with correction)     (96.67, 100.00)    (47.62, 99.05)      (77.38, 100.00) 
#>   Partial Response (PR)                   0 (0.0%)           3 (20.0%)           1 (6.7%)     
#>     95% CI (Wald, with correction)      (0.00, 3.33)       (0.00, 43.58)       (0.00, 22.62)  
#>   Stable Disease (SD)                     0 (0.0%)           1 (6.7%)            0 (0.0%)     
#>     95% CI (Wald, with correction)      (0.00, 3.33)       (0.00, 22.62)       (0.00, 3.33)   

run(rspt01, proc_data,
  odds_ratio = FALSE, perform_analysis = c("unstrat", "strat"),
  strata = c("STRATA1", "STRATA2"), methods = list(diff_pval_method = "fisher")
)
#> Warning: Less than 5 observations in some strata.
#> Warning: Less than 5 observations in some strata.
#> Warning: <5 data points in some strata. CMH test may be incorrect.
#> Warning: <5 data points in some strata. CMH test may be incorrect.
#>                                                 A: Drug X        B: Placebo     C: Combination 
#>                                                  (N=15)            (N=15)           (N=15)     
#>   —————————————————————————————————————————————————————————————————————————————————————————————
#>   Responders                                   15 (100.0%)       14 (93.3%)       15 (100.0%)  
#>   95% CI (Wald, with correction)              (96.7, 100.0)    (77.4, 100.0)     (96.7, 100.0) 
#>   Unstratified Analysis                                                                        
#>     Difference in Response rate (%)                                 -6.7              0.0      
#>       95% CI (Wald, with correction)                           (-26.0, 12.6)      (-6.7, 6.7)  
#>     p-value (Fisher's Exact Test)                                  1.0000           1.0000     
#>   Stratified Analysis                                                                          
#>     Difference in Response rate (%)                                 -6.8              0.0      
#>       95% CI (CMH, without correction)                          (-18.8, 5.3)      (0.0, 0.0)   
#>     p-value (Cochran-Mantel-Haenszel Test)                         0.3613             NE       
#>   Complete Response (CR)                       15 (100.0%)       11 (73.3%)       14 (93.3%)   
#>     95% CI (Wald, with correction)           (96.67, 100.00)   (47.62, 99.05)   (77.38, 100.00)
#>   Partial Response (PR)                         0 (0.0%)         3 (20.0%)         1 (6.7%)    
#>     95% CI (Wald, with correction)            (0.00, 3.33)     (0.00, 43.58)     (0.00, 22.62) 
#>   Stable Disease (SD)                           0 (0.0%)          1 (6.7%)         0 (0.0%)    
#>     95% CI (Wald, with correction)            (0.00, 3.33)     (0.00, 22.62)     (0.00, 3.33)