Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 Marker (non-linear terms) <NA> <NA> >0.9
2 Stage <NA> <NA> <NA>
3 T1 <NA> <NA> <NA>
4 T2 1.3 -4.5, 7.2 0.7
5 T3 2.7 -3.8, 9.1 0.4
6 T4 -1.8 -7.9, 4.3 0.6
Code
tbl2 %>% as.data.frame()
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 Marker (non-linear terms) <NA> <NA> 0.8
2 Stage <NA> <NA> <NA>
3 T1 <NA> <NA> <NA>
4 T2 0.96 -5.0, 6.9 0.8
5 T3 2.4 -4.1, 8.9 0.5
6 T4 -1.7 -7.8, 4.5 0.6
Code
lm(age ~ marker + I(marker^2) + stage, na.omit(trial)) %>% tbl_regression() %>%
add_global_p() %>% combine_terms(formula = . ~ . - marker - I(marker^2)) %>%
as.data.frame()
Message
combine_terms: Creating a reduced model with
`reduced_model <- stats::update(x$model_obj, formula. = . ~ . - marker - I(marker^2))`
combine_terms: Calculating p-value comparing full and reduced models with
`stats::anova(x$model_obj, reduced_model)`
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 Marker Level (ng/mL) <NA> <NA> 0.9
2 T Stage <NA> <NA> 0.5
3 T1 <NA> <NA> <NA>
4 T2 2.1 -3.9, 8.1 <NA>
5 T3 2.7 -3.9, 9.3 <NA>
6 T4 -1.8 -8.0, 4.4 <NA>
Code
glm(response ~ age + marker + sp2marker + sp3marker, data = trial %>% dplyr::bind_cols(
Hmisc::rcspline.eval(.$marker, nk = 4, inclx = FALSE, norm = 0) %>%
as.data.frame() %>% stats::setNames(c("sp2marker", "sp3marker"))) %>%
filter(complete.cases(.) == TRUE), family = "binomial") %>% tbl_regression(
exponentiate = TRUE) %>% combine_terms(formula_update = . ~ . - marker -
sp2marker - sp3marker, test = "LRT") %>% as.data.frame()
Message
combine_terms: Creating a reduced model with
`reduced_model <- stats::update(x$model_obj, formula. = . ~ . - marker - sp2marker - sp3marker)`
combine_terms: Calculating p-value comparing full and reduced models with
`stats::anova(x$model_obj, reduced_model, test = "LRT")`
Output
**Characteristic** **OR** **95% CI** **p-value**
1 Age 1.02 1.00, 1.04 0.11
2 Marker Level (ng/mL) <NA> <NA> 0.5
Code
survival::coxph(survival::Surv(ttdeath, death) ~ grade + Hmisc::rcspline.eval(
marker, nk = 4, inclx = TRUE, norm = 0), data = na.omit(trial)) %>%
tbl_regression() %>% combine_terms(formula_update = . ~ . - Hmisc::rcspline.eval(
marker, nk = 4, inclx = TRUE, norm = 0)) %>% as.data.frame()
Message
combine_terms: Creating a reduced model with
`reduced_model <- stats::update(x$model_obj, formula. = . ~ . - Hmisc::rcspline.eval(marker, nk = 4, inclx = TRUE, norm = 0))`
combine_terms: Calculating p-value comparing full and reduced models with
`stats::anova(x$model_obj, reduced_model)`
Output
**Characteristic** **log(HR)**
1 Grade <NA>
2 I <NA>
3 II 0.13
4 III 0.58
5 Hmisc::rcspline.eval(marker, nk = 4, inclx = TRUE, norm = 0) <NA>
**95% CI** **p-value**
1 <NA> <NA>
2 <NA> <NA>
3 -0.41, 0.67 0.6
4 0.09, 1.1 0.021
5 <NA> 0.7
Code
survival::survreg(survival::Surv(ttdeath, death) ~ grade + Hmisc::rcspline.eval(
marker, nk = 4, inclx = TRUE, norm = 0), data = na.omit(trial)) %>%
tbl_regression() %>% combine_terms(formula_update = . ~ . - Hmisc::rcspline.eval(
marker, nk = 4, inclx = TRUE, norm = 0)) %>% as.data.frame()
Condition
Warning:
The `exponentiate` argument is not supported in the `tidy()` method for `survreg` objects and will be ignored.
Message
combine_terms: Creating a reduced model with
`reduced_model <- stats::update(x$model_obj, formula. = . ~ . - Hmisc::rcspline.eval(marker, nk = 4, inclx = TRUE, norm = 0))`
combine_terms: Calculating p-value comparing full and reduced models with
`stats::anova(x$model_obj, reduced_model)`
Condition
Warning:
The `exponentiate` argument is not supported in the `tidy()` method for `survreg` objects and will be ignored.
Output
**Characteristic** **Beta**
1 Grade <NA>
2 I <NA>
3 II -0.05
4 III -0.21
5 Hmisc::rcspline.eval(marker, nk = 4, inclx = TRUE, norm = 0) <NA>
**95% CI** **p-value**
1 <NA> <NA>
2 <NA> <NA>
3 -0.24, 0.15 0.6
4 -0.39, -0.03 0.021
5 <NA> 0.7
Code
geepack::geeglm(as.formula("weight ~ Diet + Time + sp2Time + sp3Time"), data = ChickWeight %>%
dplyr::bind_cols(Hmisc::rcspline.eval(.$Time, nk = 4, inclx = FALSE, norm = 0) %>%
as.data.frame() %>% stats::setNames(c("sp2Time", "sp3Time"))), family = gaussian,
id = Chick, corstr = "exchangeable") %>% tbl_regression() %>% combine_terms(
formula_update = . ~ . - Time - sp2Time - sp3Time) %>% as.data.frame()
Message
combine_terms: Creating a reduced model with
`reduced_model <- stats::update(x$model_obj, formula. = . ~ . - Time - sp2Time - sp3Time)`
combine_terms: Calculating p-value comparing full and reduced models with
`stats::anova(x$model_obj, reduced_model)`
Output
**Characteristic** **Beta** **95% CI** **p-value**
1 Diet <NA> <NA> <NA>
2 1 <NA> <NA> <NA>
3 2 16 -4.5, 37 0.13
4 3 37 18, 55 <0.001
5 4 30 18, 43 <0.001
6 Time <NA> <NA> <0.001
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