combine_terms.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables

combine_terms works without error

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