![[Stable]](figures/lifecycle-stable.svg)
Layout creating function which summarizes a logistic variable regression for binary outcome with categorical/continuous covariates in model statement. For each covariate category (if categorical) or specified values (if continuous), present degrees of freedom, regression parameter estimate and standard error (SE) relative to reference group or category. Report odds ratios for each covariate category or specified values and corresponding Wald confidence intervals as default but allow user to specify other confidence levels. Report p-value for Wald chi-square test of the null hypothesis that covariate has no effect on response in model containing all specified covariates. Allow option to include one two-way interaction and present similar output for each interaction degree of freedom. Note: For the formula, the variable names need to be standard dataframe column name without special characters.
Arguments
- lyt
(
layout)
input layout where analyses will be added to.- conf_level
(
proportion)
confidence level of the interval.- drop_and_remove_str
string to be dropped and removed
Examples
library(dplyr)
library(broom)
adrs_f <- tern_ex_adrs %>%
filter(PARAMCD == "BESRSPI") %>%
filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
mutate(
Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
RACE = factor(RACE),
SEX = factor(SEX)
)
formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
mod1 <- fit_logistic(
data = adrs_f,
variables = list(
response = "Response",
arm = "ARMCD",
covariates = c("AGE", "RACE")
)
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
mod2 <- fit_logistic(
data = adrs_f,
variables = list(
response = "Response",
arm = "ARMCD",
covariates = c("AGE", "RACE"),
interaction = "AGE"
)
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
df <- tidy(mod1, conf_level = 0.99)
df2 <- tidy(mod2, conf_level = 0.99)
# flagging empty strings with "_"
df <- df_explicit_na(df, na_level = "_")
df2 <- df_explicit_na(df2, na_level = "_")
result1 <- basic_table() %>%
summarize_logistic(
conf_level = 0.95,
drop_and_remove_str = "_"
) %>%
build_table(df = df)
result1
#> Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 95% CI p-value
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Planned Arm Code 2 0.3004
#> Reference ARM A, n = 64
#> ARM B, n = 68 1 -1.775 1.144 0.17 (<0.01, 3.23) 0.1209
#> ARM C, n = 52 1 17.192 3626.588 >999.99 (0.00, >999.99) 0.9962
#> Age
#> Age 1 0.170 0.095 1.19 (0.93, 1.51) 0.0746
#> Race 2 0.7967
#> Reference ASIAN, n = 110
#> BLACK OR AFRICAN AMERICAN, n = 40 1 17.923 4001.705 >999.99 (0.00, >999.99) 0.9964
#> WHITE, n = 34 1 -0.656 0.974 0.52 (0.04, 6.37) 0.5002
result2 <- basic_table() %>%
summarize_logistic(
conf_level = 0.95,
drop_and_remove_str = "_"
) %>%
build_table(df = df2)
result2
#> Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 95% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Race 2 0.9361
#> Reference ASIAN, n = 110
#> BLACK OR AFRICAN AMERICAN, n = 40 1 18.150 3944.701 >999.99 (0.00, >999.99) 0.9963
#> WHITE, n = 34 1 -0.373 1.026 0.69 (0.05, 9.68) 0.7164
#> Planned Arm Code 2 0.2539
#> Reference ARM A, n = 64
#> ARM B, n = 68 1 -11.527 6.962 0.0978
#> Age
#> 35 0.48 (0.01, 15.48)
#> ARM C, n = 52 1 16.333 20278.387 0.9994
#> Age
#> 35 >999.99 (0.00, >999.99)
#> Age
#> Age 1 -0.039 0.151 0.7981
#> Planned Arm Code
#> ARM A 0.96 (0.65, 1.42)
#> ARM B 1.31 (0.92, 1.86)
#> ARM C 0.99 (0.00, >999.99)
#> Interaction of Planned Arm Code * Age 2 0.3276
#> Reference ARM A, n = 64
#> ARM B, n = 68 1 0.308 0.206 0.1352
#> ARM C, n = 52 1 0.029 548.592 1.0000