Below is a listing of tests available internally within gtsummary.
Tests listed with ... may have additional arguments
passed to them using add_p(test.args=). For example, to
calculate a p-value from t.test() assuming equal variance, use
tbl_summary(trial, by = trt) %>% add_p(age ~ "t.test", test.args = age ~ list(var.equal = TRUE))
tbl_summary() %>% add_p()
| alias | description | pseudo-code | details |
't.test' | t-test | t.test(variable ~ as.factor(by), data = data, conf.level = 0.95, ...) | |
'aov' | One-way ANOVA | aov(variable ~ as.factor(by), data = data) %>% summary() | |
'mood.test' | Mood two-sample test of scale | mood.test(variable ~ as.factor(by), data = data, ...) | Not to be confused with the Brown-Mood test of medians |
'oneway.test' | One-way ANOVA | oneway.test(variable ~ as.factor(by), data = data, ...) | |
'kruskal.test' | Kruskal-Wallis test | kruskal.test(data[[variable]], as.factor(data[[by]])) | |
'wilcox.test' | Wilcoxon rank-sum test | wilcox.test(as.numeric(variable) ~ as.factor(by), data = data, conf.int = TRUE, conf.level = conf.level, ...) | |
'chisq.test' | chi-square test of independence | chisq.test(x = data[[variable]], y = as.factor(data[[by]]), ...) | |
'chisq.test.no.correct' | chi-square test of independence | chisq.test(x = data[[variable]], y = as.factor(data[[by]]), correct = FALSE) | |
'fisher.test' | Fisher's exact test | fisher.test(data[[variable]], as.factor(data[[by]]), conf.level = 0.95, ...) | |
'mcnemar.test' | McNemar's test | tidyr::pivot_wider(id_cols = group, ...); mcnemar.test(by_1, by_2, conf.level = 0.95, ...) | |
'mcnemar.test.wide' | McNemar's test | mcnemar.test(data[[variable]], data[[by]], conf.level = 0.95, ...) | |
'lme4' | random intercept logistic regression | lme4::glmer(by ~ (1 \UFF5C group), data, family = binomial) %>% anova(lme4::glmer(by ~ variable + (1 \UFF5C group), data, family = binomial)) | |
'paired.t.test' | Paired t-test | tidyr::pivot_wider(id_cols = group, ...); t.test(by_1, by_2, paired = TRUE, conf.level = 0.95, ...) | |
'paired.wilcox.test' | Paired Wilcoxon rank-sum test | tidyr::pivot_wider(id_cols = group, ...); wilcox.test(by_1, by_2, paired = TRUE, conf.int = TRUE, conf.level = 0.95, ...) | |
'prop.test' | Test for equality of proportions | prop.test(x, n, conf.level = 0.95, ...) | |
'ancova' | ANCOVA | lm(variable ~ by + adj.vars) | |
'emmeans' | Estimated Marginal Means or LS-means | lm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, glm(family = binomial) and emmeans(regrid = "response") arguments are used. When group is specified, lme4::lmer() and lme4::glmer() are used with the group as a random intercept. |
tbl_svysummary() %>% add_p()
| alias | description | pseudo-code | details |
'svy.t.test' | t-test adapted to complex survey samples | survey::svyttest(~variable + by, data) | |
'svy.wilcox.test' | Wilcoxon rank-sum test for complex survey samples | survey::svyranktest(~variable + by, data, test = 'wilcoxon') | |
'svy.kruskal.test' | Kruskal-Wallis rank-sum test for complex survey samples | survey::svyranktest(~variable + by, data, test = 'KruskalWallis') | |
'svy.vanderwaerden.test' | van der Waerden's normal-scores test for complex survey samples | survey::svyranktest(~variable + by, data, test = 'vanderWaerden') | |
'svy.median.test' | Mood's test for the median for complex survey samples | survey::svyranktest(~variable + by, data, test = 'median') | |
'svy.chisq.test' | chi-squared test with Rao & Scott's second-order correction | survey::svychisq(~variable + by, data, statistic = 'F') | |
'svy.adj.chisq.test' | chi-squared test adjusted by a design effect estimate | survey::svychisq(~variable + by, data, statistic = 'Chisq') | |
'svy.wald.test' | Wald test of independence for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'Wald') | |
'svy.adj.wald.test' | adjusted Wald test of independence for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'adjWald') | |
'svy.lincom.test' | test of independence using the exact asymptotic distribution for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'lincom') | |
'svy.saddlepoint.test' | test of independence using a saddlepoint approximation for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'saddlepoint') | |
'emmeans' | Estimated Marginal Means or LS-means | survey::svyglm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, survey::svyglm(family = binomial) and emmeans(regrid = "response") arguments are used. |
tbl_survfit() %>% add_p()
| alias | description | pseudo-code |
'logrank' | Log-rank test | survival::survdiff(Surv(.) ~ variable, data, rho = 0) |
'tarone' | Tarone-Ware test | survival::survdiff(Surv(.) ~ variable, data, rho = 1.5) |
'petopeto_gehanwilcoxon' | Peto & Peto modification of Gehan-Wilcoxon test | survival::survdiff(Surv(.) ~ variable, data, rho = 1) |
'survdiff' | G-rho family test | survival::survdiff(Surv(.) ~ variable, data, ...) |
'coxph_lrt' | Cox regression (LRT) | survival::coxph(Surv(.) ~ variable, data, ...) |
'coxph_wald' | Cox regression (Wald) | survival::coxph(Surv(.) ~ variable, data, ...) |
'coxph_score' | Cox regression (Score) | survival::coxph(Surv(.) ~ variable, data, ...) |
tbl_continuous() %>% add_p()
| alias | description | pseudo-code |
'anova_2way' | Two-way ANOVA | lm(continuous_variable ~ by + variable) |
't.test' | t-test | t.test(continuous_variable ~ as.factor(variable), data = data, conf.level = 0.95, ...) |
'aov' | One-way ANOVA | aov(continuous_variable ~ as.factor(variable), data = data) %>% summary() |
'kruskal.test' | Kruskal-Wallis test | kruskal.test(data[[continuous_variable]], as.factor(data[[variable]])) |
'wilcox.test' | Wilcoxon rank-sum test | wilcox.test(as.numeric(continuous_variable) ~ as.factor(variable), data = data, ...) |
'lme4' | random intercept logistic regression | lme4::glmer(by ~ (1 \UFF5C group), data, family = binomial) %>% anova(lme4::glmer(variable ~ continuous_variable + (1 \UFF5C group), data, family = binomial)) |
'ancova' | ANCOVA | lm(continuous_variable ~ variable + adj.vars) |
tbl_summary() %>% add_difference()
| alias | description | difference statistic | pseudo-code | details |
't.test' | t-test | mean difference | t.test(variable ~ as.factor(by), data = data, conf.level = 0.95, ...) | |
'wilcox.test' | Wilcoxon rank-sum test | wilcox.test(as.numeric(variable) ~ as.factor(by), data = data, conf.int = TRUE, conf.level = conf.level, ...) | ||
'paired.t.test' | Paired t-test | mean difference | tidyr::pivot_wider(id_cols = group, ...); t.test(by_1, by_2, paired = TRUE, conf.level = 0.95, ...) | |
'prop.test' | Test for equality of proportions | rate difference | prop.test(x, n, conf.level = 0.95, ...) | |
'ancova' | ANCOVA | mean difference | lm(variable ~ by + adj.vars) | |
'ancova_lme4' | ANCOVA with random intercept | mean difference | lme4::lmer(variable ~ by + adj.vars + (1 \UFF5C group), data) | |
'cohens_d' | Cohen's D | standardized mean difference | effectsize::cohens_d(variable ~ by, data, ci = conf.level, verbose = FALSE, ...) | |
'hedges_g' | Hedge's G | standardized mean difference | effectsize::hedges_g(variable ~ by, data, ci = conf.level, verbose = FALSE, ...) | |
'paired_cohens_d' | Paired Cohen's D | standardized mean difference | tidyr::pivot_wider(id_cols = group, ...); effectsize::cohens_d(by_1, by_2, paired = TRUE, conf.level = 0.95, verbose = FALSE, ...) | |
'paired_hedges_g' | Paired Hedge's G | standardized mean difference | tidyr::pivot_wider(id_cols = group, ...); effectsize::hedges_g(by_1, by_2, paired = TRUE, conf.level = 0.95, verbose = FALSE, ...) | |
'smd' | Standardized Mean Difference | standardized mean difference | smd::smd(x = data[[variable]], g = data[[by]], std.error = TRUE) | |
'emmeans' | Estimated Marginal Means or LS-means | adjusted mean difference | lm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, glm(family = binomial) and emmeans(regrid = "response") arguments are used. When group is specified, lme4::lmer() and lme4::glmer() are used with the group as a random intercept. |
tbl_svysummary() %>% add_difference()
| alias | description | difference statistic | pseudo-code | details |
'smd' | Standardized Mean Difference | standardized mean difference | smd::smd(x = variable, g = by, w = weights(data), std.error = TRUE) | |
'svy.t.test' | t-test adapted to complex survey samples | survey::svyttest(~variable + by, data) | ||
'emmeans' | Estimated Marginal Means or LS-means | adjusted mean difference | survey::svyglm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, survey::svyglm(family = binomial) and emmeans(regrid = "response") arguments are used. |
Custom Functions
To report a p-value (or difference) for a test not available in gtsummary, you can create a
custom function. The output is a data frame that is one line long. The
structure is similar to the output of broom::tidy() of a typical
statistical test. The add_p() and add_difference() functions will look for columns called
"p.value", "estimate", "statistic", "std.error", "parameter",
"conf.low", "conf.high", and "method".
You can also pass an Analysis Results Dataset (ARD) object with the results for your custom result. These objects follow the structures outlined by the {cards} and {cardx} packages.
Example calculating a p-value from a t-test assuming a common variance between groups.
ttest_common_variance <- function(data, variable, by, ...) {
data <- data[c(variable, by)] %>% dplyr::filter(complete.cases(.))
t.test(data[[variable]] ~ factor(data[[by]]), var.equal = TRUE) %>%
broom::tidy()
}
trial[c("age", "trt")] %>%
tbl_summary(by = trt) %>%
add_p(test = age ~ "ttest_common_variance")A custom add_difference() is similar, and accepts arguments conf.level=
and adj.vars= as well.
ttest_common_variance <- function(data, variable, by, conf.level, ...) {
data <- data[c(variable, by)] %>% dplyr::filter(complete.cases(.))
t.test(data[[variable]] ~ factor(data[[by]]), conf.level = conf.level, var.equal = TRUE) %>%
broom::tidy()
}Function Arguments
For tbl_summary() objects, the custom function will be passed the
following arguments: custom_pvalue_fun(data=, variable=, by=, group=, type=, conf.level=, adj.vars=).
While your function may not utilize each of these arguments, these arguments
are passed and the function must accept them. We recommend including ...
to future-proof against updates where additional arguments are added.
The following table describes the argument inputs for each gtsummary table type.
| argument | tbl_summary | tbl_svysummary | tbl_survfit | tbl_continuous |
data= | A data frame | A survey object | A survfit() object | A data frame |
variable= | String variable name | String variable name | NA | String variable name |
by= | String variable name | String variable name | NA | String variable name |
group= | String variable name | NA | NA | String variable name |
type= | Summary type | Summary type | NA | NA |
conf.level= | Confidence interval level | NA | NA | NA |
adj.vars= | Character vector of adjustment variable names (e.g. used in ANCOVA) | NA | NA | Character vector of adjustment variable names (e.g. used in ANCOVA) |
continuous_variable= | NA | NA | NA | String of the continuous variable name |