Tabulation of a GEE Model

Source: R/tabulate_gee.R

Functions to produce tables from a fitted GEE produced with fit_gee().

# S3 method for class 'tern_gee'
as.rtable(x, type = c("coef", "cov"), ...)

s_lsmeans_logistic(df, .in_ref_col)

a_lsmeans_logistic(df, .in_ref_col)

summarize_gee_logistic(
  lyt,
  ...,
  table_names = "lsmeans_logistic_summary",
  .stats = NULL,
  .formats = NULL,
  .indent_mods = NULL,
  .labels = NULL
)

Arguments

x

(data.frame)
the object which should be converted to an rtable.

type

(character)
type of table to extract from tern_gee object.

...

additional arguments for methods.

df

(data.frame)
data set resulting from lsmeans().

.in_ref_col

(logical)
TRUE when working with the reference level, FALSE otherwise.

lyt

(layout)
input layout where analyses will be added to.

table_names

(character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.indent_mods

(named integer)
indent modifiers for the labels.

.labels

(named character)
labels for the statistics (without indent).

Value

The functions have different purposes:

  • as.rtable() returns either the coefficient table or the covariance matrix as an rtables object.

  • s_lsmeans_logistic() returns several least square mean statistics from the GEE.

  • a_lsmeans_logistic() is the formatted analysis function and returns the formatted statistics.

  • summarize_gee_logistic() is the analyze function and returns the modified rtables layout.

Functions

  • as.rtable(tern_gee): Extracts the coefficient table or covariance matrix estimate from a tern_gee object.

  • s_lsmeans_logistic(): Statistics function which extracts estimates from a lsmeans() data frame based on a logistic GEE model.

  • a_lsmeans_logistic(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

  • summarize_gee_logistic(): Analyze function for tabulating least-squares means estimates from logistic GEE least square mean results.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

df <- fev_data %>%
  mutate(AVAL = as.integer(fev_data$FEV1 > 30))
df_counts <- df %>%
  select(USUBJID, ARMCD) %>%
  unique()

lsmeans_df <- lsmeans(fit_gee(vars = vars_gee(arm = "ARMCD"), data = df))

s_lsmeans_logistic(lsmeans_df[1, ], .in_ref_col = TRUE)
#> $n
#> [1] 420
#> 
#> $adj_prop_se
#> [1] 0.88390403 0.01948562
#> 
#> $adj_prop_ci
#> [1] 0.8398239 0.9170515
#> attr(,"label")
#> [1] "95% CI"
#> 
#> $odds_ratio_est
#> character(0)
#> 
#> $odds_ratio_ci
#> character(0)
#> attr(,"label")
#> [1] "95% CI"
#> 
#> $log_odds_ratio_est
#> character(0)
#> 
#> $log_odds_ratio_ci
#> character(0)
#> attr(,"label")
#> [1] "95% CI"
#> 

s_lsmeans_logistic(lsmeans_df[2, ], .in_ref_col = FALSE)
#> $n
#> [1] 380
#> 
#> $adj_prop_se
#> [1] 0.94325891 0.01386798
#> 
#> $adj_prop_ci
#> [1] 0.9090296 0.9651032
#> attr(,"label")
#> [1] "95% CI"
#> 
#> $odds_ratio_est
#> [1] 2.18346
#> 
#> $odds_ratio_ci
#> [1] 1.161678 4.103974
#> attr(,"label")
#> [1] "95% CI"
#> 
#> $log_odds_ratio_est
#> [1] 0.7809108
#> 
#> $log_odds_ratio_ci
#> [1] 0.1498659 1.4119557
#> attr(,"label")
#> [1] "95% CI"
#> 
basic_table() %>%
  split_cols_by("ARMCD") %>%
  add_colcounts() %>%
  summarize_gee_logistic(
    .in_ref_col = FALSE
  ) %>%
  build_table(lsmeans_df, alt_counts_df = df_counts)
#>                                     PBO            TRT     
#>                                   (N=105)         (N=95)   
#> ———————————————————————————————————————————————————————————
#> n                                   420            380     
#> Adjusted Mean Proportion (SE)   0.88 (0.02)    0.94 (0.01) 
#>   95% CI                        (0.84, 0.92)   (0.91, 0.97)
#> Odds Ratio                           NA            2.18    
#>   95% CI                             NA        (1.16, 4.10)
#> Log Odds Ratio                       NA            0.78    
#>   95% CI                             NA        (0.15, 1.41)