# All tables examples

#### Ewen Harrison

    install.packages("finalfit")

## 1 Cross tables

Two-way tables are used extensively in healthcare research, e.g. a 2x2 table comparing two factors with two levels each, or table 1 from a typical clinical study or trial

The main functions all take a `dependent` variable - the outcome - and `explanatory` variables - predictors or exposures (any number categorical or continuous variables).

### 1.01 Default

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
      summary_factorlist(dependent, explanatory) -> t

label | levels | No | Yes
--- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3)
Age | <40 years | 68 (7.5) | 2 (7.4)
| 40-59 years | 334 (37.0) | 10 (37.0)
| 60+ years | 500 (55.4) | 15 (55.6)
Sex | Female | 432 (47.9) | 13 (48.1)
| Male | 470 (52.1) | 14 (51.9)
Obstruction | No | 715 (81.2) | 17 (63.0)
| Yes | 166 (18.8) | 10 (37.0)

Note, chi-squared warnings will be generated when the expected count in any cell is less than 5. Fisher’s exact test can be used as below, or go straight to a univariable logistic regression, e.g. `colon_s %>% finalfit(dependent, explanatory)`

### 1.02 Add or edit variable labels

    library(finalfit)
    library(dplyr)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        mutate(
            sex.factor = ff_label(sex.factor, "Gender")
        ) %>%
      summary_factorlist(dependent, explanatory) -> t

label | levels | No | Yes
--- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3)
Age | <40 years | 68 (7.5) | 2 (7.4)
| 40-59 years | 334 (37.0) | 10 (37.0)
| 60+ years | 500 (55.4) | 15 (55.6)
Gender | Female | 432 (47.9) | 13 (48.1)
| Male | 470 (52.1) | 14 (51.9)
Obstruction | No | 715 (81.2) | 17 (63.0)
| Yes | 166 (18.8) | 10 (37.0)

### 1.03 P-value for hypothesis test

Defaults are chi-squared for categorical explanatory variables and an F-test for continuous (`aov`, analysis of variance). Alternatives can be specified as per below.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.542
Age | <40 years | 68 (7.5) | 2 (7.4) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) |
Sex | Female | 432 (47.9) | 13 (48.1) | 1.000
| Male | 470 (52.1) | 14 (51.9) |
Obstruction | No | 715 (81.2) | 17 (63.0) | 0.035
| Yes | 166 (18.8) | 10 (37.0) |

### 1.04 With Fisher’s exact test

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
      summary_factorlist(dependent, explanatory, p = TRUE, p_cat = "fisher") -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.542
Age | <40 years | 68 (7.5) | 2 (7.4) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) |
Sex | Female | 432 (47.9) | 13 (48.1) | 1.000
| Male | 470 (52.1) | 14 (51.9) |
Obstruction | No | 715 (81.2) | 17 (63.0) | 0.026
| Yes | 166 (18.8) | 10 (37.0) |

### 1.05 Parametric explanatory variables

Summaries for continuous explanatory variables are mean (standard deviation) with `aov` statistical test by default. The statistical test can be changed to the Welch t-test when there are two dependent variable levels if desired.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
      summary_factorlist(dependent, explanatory, p = TRUE, p_cont_para = "t.test") -> t
    #> Warning in chisq.test(age.factor, perfor.factor): Chi-squared approximation may
    #> be incorrect

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.586
Age | <40 years | 68 (7.5) | 2 (7.4) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) |
Sex | Female | 432 (47.9) | 13 (48.1) | 1.000
| Male | 470 (52.1) | 14 (51.9) |
Obstruction | No | 715 (81.2) | 17 (63.0) | 0.035
| Yes | 166 (18.8) | 10 (37.0) |

### 1.06 Non-parametric explanatory variables

If desired, all continuous explanatory variables can be considered non-parametric. Summaries will be median (interquartile range) and the statistical test is Kruskal-Wallis/Mann-Whitney U. Use `cont_range = FALSE` if wish single-digit IQR, i.e. Q3-Q1.

    library(finalfit)
    explanatory = c("age", "nodes", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, cont = "median") -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Median (IQR) | 61.0 (53.0 to 69.0) | 60.0 (50.0 to 68.0) | 0.578
nodes | Median (IQR) | 2.0 (1.0 to 5.0) | 3.0 (2.0 to 4.0) | 0.125
Age | <40 years | 68 (7.5) | 2 (7.4) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) |
Sex | Female | 432 (47.9) | 13 (48.1) | 1.000
| Male | 470 (52.1) | 14 (51.9) |
Obstruction | No | 715 (81.2) | 17 (63.0) | 0.035
| Yes | 166 (18.8) | 10 (37.0) |

### 1.07 Select specific non-parametric variables

Many have asked in the past if only particular variables can be considered non-parametric. The argument `cont_nonpara` can take a vector (e.g. `c(1, 2, 3, 4)`) of values corresponding to the explanatory variable to specify which should be summarised as a median and be passed to a non-parametric test.

    library(finalfit)
    explanatory = c("age", "nodes", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, cont_nonpara = c(2)) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.542
nodes | Median (IQR) | 2.0 (1.0 to 5.0) | 3.0 (2.0 to 4.0) | 0.125
Age | <40 years | 68 (7.5) | 2 (7.4) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) |
Sex | Female | 432 (47.9) | 13 (48.1) | 1.000
| Male | 470 (52.1) | 14 (51.9) |
Obstruction | No | 715 (81.2) | 17 (63.0) | 0.035
| Yes | 166 (18.8) | 10 (37.0) |

### 1.08 Missing values for the explanatory variables

Always consider summarising missing values when describing your data.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.542
Age | <40 years | 68 (7.5) | 2 (7.4) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) |
Sex | Female | 432 (47.9) | 13 (48.1) | 1.000
| Male | 470 (52.1) | 14 (51.9) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 0.035
| Yes | 166 (18.4) | 10 (37.0) |
| (Missing) | 21 (2.3) | 0 (0.0) |

### 1.09 Pass missing values to statistical tests

This is a change from the default behaviour introduced in Finalfit 1.0.0. Previously, when missing data was presented it was also considered as a level in the statistical test. This may or may not be desired. Control this now using `na_to_p = TRUE` to include missing data in test. A message is produced reminding you that you are doing that.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                                             na_to_p = TRUE) -> t
    #> Explanatory variable(s) missing data included in hypothesis test (p-value).

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.542
Age | <40 years | 68 (7.5) | 2 (7.4) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) |
Sex | Female | 432 (47.9) | 13 (48.1) | 1.000
| Male | 470 (52.1) | 14 (51.9) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 0.042
| Yes | 166 (18.4) | 10 (37.0) |
| (Missing) | 21 (2.3) | 0 (0.0) |

### 1.10 Row proportions (rather than column)

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                                             column = FALSE) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.542
Age | <40 years | 68 (97.1) | 2 (2.9) | 1.000
| 40-59 years | 334 (97.1) | 10 (2.9) |
| 60+ years | 500 (97.1) | 15 (2.9) |
Sex | Female | 432 (97.1) | 13 (2.9) | 1.000
| Male | 470 (97.1) | 14 (2.9) |
Obstruction | No | 715 (97.7) | 17 (2.3) | 0.035
| Yes | 166 (94.3) | 10 (5.7) |
| (Missing) | 21 (100.0) | 0 (0.0) |

### 1.11 Total column

The terms total column was introduced before this function summarised continuous variables. It would be better to be “All data” or something similar, as the continous explanatory variables a summary statistic is produced for all data. However, to keep backwards compatibility we have left it unchanged for now. For producing row totals including continous explanatory variables, see `add_row_total` below.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
                                             column = TRUE, total_col = TRUE) -> t

label | levels | No | Yes | Total | p
--- | --- | --- | --- | --- | ---
Age (years) | Median (IQR) | 61.0 (53.0 to 69.0) | 60.0 (50.0 to 68.0) | 61.0 (53.0 to 69.0) | 0.578
Age | <40 years | 68 (7.5) | 2 (7.4) | 70 (7.5) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) | 344 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) | 515 (55.4) |
Sex | Female | 432 (47.9) | 13 (48.1) | 445 (47.9) | 1.000
| Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.035
| Yes | 166 (18.4) | 10 (37.0) | 176 (18.9) |
| (Missing) | 21 (2.3) | 0 (0.0) | 21 (2.3) |

### 1.12 Row totals with missing

This was introduced to deal with the problem of summarising missing data for continuous variables. By default, it provides the total N for the variable and includes a column enumerating missing values.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                                             total_col = TRUE,
                                             add_row_total = TRUE) -> t

label | Total N | Missing N | levels | No | Yes | Total | p
--- | --- | --- | --- | --- | --- | --- | ---
Age (years) | 929 (100.0) | 0 | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 59.8 (11.9) | 0.542
Age | 929 (100.0) | 0 | <40 years | 68 (7.5) | 2 (7.4) | 70 (7.5) | 1.000
|  |  | 40-59 years | 334 (37.0) | 10 (37.0) | 344 (37.0) |
|  |  | 60+ years | 500 (55.4) | 15 (55.6) | 515 (55.4) |
Sex | 929 (100.0) | 0 | Female | 432 (47.9) | 13 (48.1) | 445 (47.9) | 1.000
|  |  | Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | 908 (97.7) | 21 | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.035
|  |  | Yes | 166 (18.4) | 10 (37.0) | 176 (18.9) |
|  |  | (Missing) | 21 (2.3) | 0 (0.0) | 21 (2.3) |

### 1.13 Row totals without missing

Remove missing column.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                                             total_col = TRUE,
                                             add_row_total = TRUE,
                                             include_row_missing_col = FALSE) -> t

label | Total N | levels | No | Yes | Total | p
--- | --- | --- | --- | --- | --- | ---
Age (years) | 929 (100.0) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 59.8 (11.9) | 0.542
Age | 929 (100.0) | <40 years | 68 (7.5) | 2 (7.4) | 70 (7.5) | 1.000
|  | 40-59 years | 334 (37.0) | 10 (37.0) | 344 (37.0) |
|  | 60+ years | 500 (55.4) | 15 (55.6) | 515 (55.4) |
Sex | 929 (100.0) | Female | 432 (47.9) | 13 (48.1) | 445 (47.9) | 1.000
|  | Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | 908 (97.7) | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.035
|  | Yes | 166 (18.4) | 10 (37.0) | 176 (18.9) |
|  | (Missing) | 21 (2.3) | 0 (0.0) | 21 (2.3) |

### 1.14 Row totals with user-specified column names

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE,
                                             total_col = TRUE,
                                             add_row_total = TRUE,
                                             row_totals_colname = "N (total)",
                                             row_missing_colname = "N (missing)") -> t

label | N (total) | N (missing) | levels | No | Yes | Total | p
--- | --- | --- | --- | --- | --- | --- | ---
Age (years) | 929 (100.0) | 0 | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 59.8 (11.9) | 0.542
Age | 929 (100.0) | 0 | <40 years | 68 (7.5) | 2 (7.4) | 70 (7.5) | 1.000
|  |  | 40-59 years | 334 (37.0) | 10 (37.0) | 344 (37.0) |
|  |  | 60+ years | 500 (55.4) | 15 (55.6) | 515 (55.4) |
Sex | 929 (100.0) | 0 | Female | 432 (47.9) | 13 (48.1) | 445 (47.9) | 1.000
|  |  | Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | 908 (97.7) | 21 | No | 715 (81.2) | 17 (63.0) | 732 (80.6) | 0.035
|  |  | Yes | 166 (18.8) | 10 (37.0) | 176 (19.4) |

### 1.15 Order a variable by total

This is intended for when there is only one `explanatory` variable.

    library(finalfit)
    explanatory = c("extent.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
                                             column = TRUE, total_col = TRUE, orderbytotal = TRUE) -> t

label | levels | No | Yes | Total | p
--- | --- | --- | --- | --- | ---
Extent of spread | Serosa | 736 (81.6) | 23 (85.2) | 759 (81.7) | 0.200
| Muscle | 105 (11.6) | 1 (3.7) | 106 (11.4) |
| Adjacent structures | 40 (4.4) | 3 (11.1) | 43 (4.6) |
| Submucosa | 21 (2.3) | 0 (0.0) | 21 (2.3) |

### 1.17 Add column totals

Column totals can be added, and by default are presented with a row percentage.

    explanatory = c("age.factor", "sex.factor")
    dependent = "rx.factor"

    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE,
                                             add_col_totals = TRUE) -> t

label | levels | Obs | Lev | Lev+5FU | p
--- | --- | --- | --- | --- | ---
Total N (%) |  | 315 (33.9) | 310 (33.4) | 304 (32.7) |
Age | <40 years | 25 (7.9) | 19 (6.1) | 26 (8.6) | 0.572
| 40-59 years | 124 (39.4) | 115 (37.1) | 105 (34.5) |
| 60+ years | 166 (52.7) | 176 (56.8) | 173 (56.9) |
Sex | Female | 149 (47.3) | 133 (42.9) | 163 (53.6) | 0.028
| Male | 166 (52.7) | 177 (57.1) | 141 (46.4) |

### 1.18 Add column totals without proportion.

    explanatory = c("age.factor", "sex.factor")
    dependent = "rx.factor"

    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE,
                                             add_col_totals = TRUE,
                                             include_col_totals_percent = FALSE)    -> t

label | levels | Obs | Lev | Lev+5FU | p
--- | --- | --- | --- | --- | ---
Total N |  | 315 | 310 | 304 |
Age | <40 years | 25 (7.9) | 19 (6.1) | 26 (8.6) | 0.572
| 40-59 years | 124 (39.4) | 115 (37.1) | 105 (34.5) |
| 60+ years | 166 (52.7) | 176 (56.8) | 173 (56.9) |
Sex | Female | 149 (47.3) | 133 (42.9) | 163 (53.6) | 0.028
| Male | 166 (52.7) | 177 (57.1) | 141 (46.4) |

### 1.19 Add column totals with user-specified row name and prefix.

    explanatory = c("age.factor", "sex.factor")
    dependent = "rx.factor"

    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE,
                                             add_col_totals = TRUE,
                                             include_col_totals_percent = FALSE,
                                             col_totals_rowname = "",
                                             col_totals_prefix = "N=")  -> t

label | levels | Obs | Lev | Lev+5FU | p
--- | --- | --- | --- | --- | ---
|  | N=315 | N=310 | N=304 |
Age | <40 years | 25 (7.9) | 19 (6.1) | 26 (8.6) | 0.572
| 40-59 years | 124 (39.4) | 115 (37.1) | 105 (34.5) |
| 60+ years | 166 (52.7) | 176 (56.8) | 173 (56.9) |
Sex | Female | 149 (47.3) | 133 (42.9) | 163 (53.6) | 0.028
| Male | 166 (52.7) | 177 (57.1) | 141 (46.4) |

### 1.20 Label with `dependent` name

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
                                             column = TRUE, total_col = TRUE, add_dependent_label = TRUE) -> t

Dependent: Perforation |  | No | Yes | Total | p
--- | --- | --- | --- | --- | ---
Age (years) | Median (IQR) | 61.0 (53.0 to 69.0) | 60.0 (50.0 to 68.0) | 61.0 (53.0 to 69.0) | 0.578
Age | <40 years | 68 (7.5) | 2 (7.4) | 70 (7.5) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) | 344 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) | 515 (55.4) |
Sex | Female | 432 (47.9) | 13 (48.1) | 445 (47.9) | 1.000
| Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.035
| Yes | 166 (18.4) | 10 (37.0) | 176 (18.9) |
| (Missing) | 21 (2.3) | 0 (0.0) | 21 (2.3) |

The dependent name cannot be passed directly to the table intentionally. This is to avoid errors when code is copied and the name is not updated. Change the dependent label using the following. The prefix (“Dependent:”) and any suffix can be altered.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
      dplyr::mutate(
        perfor.factor = ff_label(perfor.factor, "Perforated cancer")
        ) %>%
      summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
        column = TRUE, total_col = TRUE, add_dependent_label = TRUE, dependent_label_prefix = "") -> t

Perforated cancer |  | No | Yes | Total | p
--- | --- | --- | --- | --- | ---
Age (years) | Median (IQR) | 61.0 (53.0 to 69.0) | 60.0 (50.0 to 68.0) | 61.0 (53.0 to 69.0) | 0.578
Age | <40 years | 68 (7.5) | 2 (7.4) | 70 (7.5) | 1.000
| 40-59 years | 334 (37.0) | 10 (37.0) | 344 (37.0) |
| 60+ years | 500 (55.4) | 15 (55.6) | 515 (55.4) |
Sex | Female | 432 (47.9) | 13 (48.1) | 445 (47.9) | 1.000
| Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.035
| Yes | 166 (18.4) | 10 (37.0) | 176 (18.9) |
| (Missing) | 21 (2.3) | 0 (0.0) | 21 (2.3) |

### 1.21 Dependent variable with any number of factor levels supported

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "extent.factor"
    colon_s %>%
      dplyr::mutate(
        perfor.factor = ff_label(perfor.factor, "Perforated cancer")
      ) %>%
      summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
        column = TRUE, total_col = TRUE, add_dependent_label = TRUE, dependent_label_prefix = "") -> t

Extent of spread |  | Submucosa | Muscle | Serosa | Adjacent structures | Total | p
--- | --- | --- | --- | --- | --- | --- | ---
Age (years) | Median (IQR) | 56.0 (50.0 to 64.0) | 61.5 (56.0 to 70.0) | 61.0 (53.0 to 69.0) | 61.0 (53.5 to 66.0) | 61.0 (53.0 to 69.0) | 0.334
Age | <40 years | 2 (9.5) | 8 (7.5) | 56 (7.4) | 4 (9.3) | 70 (7.5) | 0.338
| 40-59 years | 12 (57.1) | 32 (30.2) | 285 (37.5) | 15 (34.9) | 344 (37.0) |
| 60+ years | 7 (33.3) | 66 (62.3) | 418 (55.1) | 24 (55.8) | 515 (55.4) |
Sex | Female | 13 (61.9) | 47 (44.3) | 366 (48.2) | 19 (44.2) | 445 (47.9) | 0.483
| Male | 8 (38.1) | 59 (55.7) | 393 (51.8) | 24 (55.8) | 484 (52.1) |
Obstruction | No | 20 (95.2) | 88 (83.0) | 588 (77.5) | 36 (83.7) | 732 (78.8) | 0.040
| Yes | 1 (4.8) | 13 (12.3) | 157 (20.7) | 5 (11.6) | 176 (18.9) |
| (Missing) | 0 (0.0) | 5 (4.7) | 14 (1.8) | 2 (4.7) | 21 (2.3) |

### 1.22 Missing data in the dependent

If you are careful to count totals and you know your data, you should realise when there is data missing from the dependent, e.g.:

    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "mort_5yr"
    colon_s %>%
        ff_glimpse(dependent, explanatory)
    #> $Continuous
    #>           label var_type   n missing_n missing_percent mean   sd  min
    #> age Age (years)    <dbl> 929         0             0.0 59.8 11.9 18.0
    #>     quartile_25 median quartile_75  max
    #> age        53.0   61.0        69.0 85.0
    #>
    #> $Categorical
    #>                            label var_type   n missing_n missing_percent
    #> mort_5yr        Mortality 5 year    <fct> 915        14             1.5
    #> age.factor                   Age    <fct> 929         0             0.0
    #> sex.factor                   Sex    <fct> 929         0             0.0
    #> obstruct.factor      Obstruction    <fct> 908        21             2.3
    #>                 levels_n                                  levels levels_count
    #> mort_5yr               2            "Alive", "Died", "(Missing)" 511, 404, 14
    #> age.factor             3 "<40 years", "40-59 years", "60+ years" 70, 344, 515
    #> sex.factor             2                        "Female", "Male"     445, 484
    #> obstruct.factor        2                "No", "Yes", "(Missing)" 732, 176, 21
    #>                   levels_percent
    #> mort_5yr        55.0, 43.5,  1.5
    #> age.factor       7.5, 37.0, 55.4
    #> sex.factor                48, 52
    #> obstruct.factor 78.8, 18.9,  2.3

To make sure, a warning is generated when data are dropped from the dependent:

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "mort_5yr"
    colon_s %>%
      summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                                         total_col = TRUE,
                                         add_col_totals = TRUE, add_row_totals = TRUE) -> t
    #> Note: dependent includes missing data. These are dropped.

label | Total N | Missing N | levels | Alive | Died | Total | p
--- | --- | --- | --- | --- | --- | --- | ---
Total N (%) |  |  |  | 511 (55.8) | 404 (44.2) | 915 |
Age (years) | 915 (100.0) | 0 | Mean (SD) | 59.8 (11.4) | 59.9 (12.5) | 59.8 (11.9) | 0.986
Age | 915 (100.0) | 0 | <40 years | 31 (6.1) | 36 (8.9) | 67 (7.3) | 0.020
|  |  | 40-59 years | 208 (40.7) | 131 (32.4) | 339 (37.0) |
|  |  | 60+ years | 272 (53.2) | 237 (58.7) | 509 (55.6) |
Sex | 915 (100.0) | 0 | Female | 243 (47.6) | 194 (48.0) | 437 (47.8) | 0.941
|  |  | Male | 268 (52.4) | 210 (52.0) | 478 (52.2) |
Obstruction | 894 (97.7) | 21 | No | 408 (79.8) | 312 (77.2) | 720 (78.7) | 0.219
|  |  | Yes | 89 (17.4) | 85 (21.0) | 174 (19.0) |
|  |  | (Missing) | 14 (2.7) | 7 (1.7) | 21 (2.3) |

You may consider making the missing data explicit.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "mort_5yr"
    colon_s %>%
        mutate(
            mort_5yr = forcats::fct_explicit_na(mort_5yr)
        ) %>%
      summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                                         total_col = TRUE,
                                         add_col_totals = TRUE, add_row_totals = TRUE) -> t

label | Total N | Missing N | levels | Alive | Died | (Missing) | Total | p
--- | --- | --- | --- | --- | --- | --- | --- | ---
Total N (%) |  |  |  | 511 (55.0) | 404 (43.5) | 14 (1.5) | 929 |
Age (years) | 929 (100.0) | 0 | Mean (SD) | 59.8 (11.4) | 59.9 (12.5) | 53.9 (12.7) | 59.8 (11.9) | 0.185
Age | 929 (100.0) | 0 | <40 years | 31 (6.1) | 36 (8.9) | 3 (21.4) | 70 (7.5) | 0.018
|  |  | 40-59 years | 208 (40.7) | 131 (32.4) | 5 (35.7) | 344 (37.0) |
|  |  | 60+ years | 272 (53.2) | 237 (58.7) | 6 (42.9) | 515 (55.4) |
Sex | 929 (100.0) | 0 | Female | 243 (47.6) | 194 (48.0) | 8 (57.1) | 445 (47.9) | 0.776
|  |  | Male | 268 (52.4) | 210 (52.0) | 6 (42.9) | 484 (52.1) |
Obstruction | 908 (97.7) | 21 | No | 408 (79.8) | 312 (77.2) | 12 (85.7) | 732 (78.8) | 0.373
|  |  | Yes | 89 (17.4) | 85 (21.0) | 2 (14.3) | 176 (18.9) |
|  |  | (Missing) | 14 (2.7) | 7 (1.7) | 0 (0.0) | 21 (2.3) |

### 1.23 Directly include missing data in dependent

Rather than making the data explicit in the dataset, you can use `na_include_dependent = TRUE` to do the same in `summary_factorlist()`.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "mort_5yr"
    colon_s %>%
      summary_factorlist(dependent, explanatory, p = TRUE,
                                         na_include = TRUE, na_include_dependent = TRUE,
                                         total_col = TRUE,
                                         add_col_totals = TRUE, add_row_totals = TRUE) -> t

label | Total N | Missing N | levels | Alive | Died | (Missing) | Total | p
--- | --- | --- | --- | --- | --- | --- | --- | ---
Total N (%) |  |  |  | 511 (55.0) | 404 (43.5) | 14 (1.5) | 929 |
Age (years) | 915 (100.0) | 0 | Mean (SD) | 59.8 (11.4) | 59.9 (12.5) | 53.9 (12.7) | 59.8 (11.9) | 0.986
Age | 915 (100.0) | 0 | <40 years | 31 (6.1) | 36 (8.9) | 3 (21.4) | 70 (7.5) | 0.020
|  |  | 40-59 years | 208 (40.7) | 131 (32.4) | 5 (35.7) | 344 (37.0) |
|  |  | 60+ years | 272 (53.2) | 237 (58.7) | 6 (42.9) | 515 (55.4) |
Sex | 915 (100.0) | 0 | Female | 243 (47.6) | 194 (48.0) | 8 (57.1) | 445 (47.9) | 0.941
|  |  | Male | 268 (52.4) | 210 (52.0) | 6 (42.9) | 484 (52.1) |
Obstruction | 894 (97.7) | 21 | No | 408 (79.8) | 312 (77.2) | 12 (85.7) | 732 (78.8) | 0.219
|  |  | Yes | 89 (17.4) | 85 (21.0) | 2 (14.3) | 176 (18.9) |
|  |  | (Missing) | 14 (2.7) | 7 (1.7) | 0 (0.0) | 21 (2.3) |

### 1.24 Summarise complete cases

You may wish to see summaries for complete cases across included variables. Rather than selecting including variables and `drop_na()`, you can pass `na_complete_cases = TRUE` to `summary_factorlist()` to do the same.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "mort_5yr"
    colon_s %>%
      summary_factorlist(dependent, explanatory, p = TRUE,
                                         na_complete_cases = TRUE,
                                         total_col = TRUE,
                                         add_col_totals = TRUE, add_row_totals = TRUE) -> t
    #> Note: dependent includes missing data. These are dropped.

label | Total N | Missing N | levels | Alive | Died | Total | p
--- | --- | --- | --- | --- | --- | --- | ---
Total N (%) |  |  |  | 467 (55.7) | 372 (44.3) | 839 |
Age (years) | 839 (91.7) | 76 | Mean (SD) | 59.6 (11.5) | 60.0 (12.3) | 59.8 (11.9) | 0.986
Age | 839 (91.7) | 76 | <40 years | 30 (6.4) | 30 (8.1) | 60 (7.2) | 0.020
|  |  | 40-59 years | 190 (40.7) | 122 (32.8) | 312 (37.2) |
|  |  | 60+ years | 247 (52.9) | 220 (59.1) | 467 (55.7) |
Sex | 839 (91.7) | 76 | Female | 223 (47.8) | 183 (49.2) | 406 (48.4) | 0.941
|  |  | Male | 244 (52.2) | 189 (50.8) | 433 (51.6) |
Obstruction | 839 (91.7) | 76 | No | 381 (81.6) | 296 (79.6) | 677 (80.7) | 0.219
|  |  | Yes | 86 (18.4) | 76 (20.4) | 162 (19.3) |

### 1.25 Actively dropping missing data (and tidyverse functions that strip attributes)

You may wish to actively remove any rows with missing data, so you are explicit around which data are being used in models. Unfortunately some tidyverse functions silently remove variable attributes (labels). This is complained about then put right. But here is a workaround if it is happening with a variable you wish to use, such as `tidyr::drop_na()`.

    library(finalfit)
    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "mort_5yr"
    vlabels = colon_s %>%
        extract_variable_label()

    colon_s %>%
        select(dependent, explanatory) %>%
        tidyr::drop_na() %>% # Silently removes attributes
        ff_relabel(vlabels) %>% # Relabel
      summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                                         total_col = TRUE,
                                         add_col_totals = TRUE, add_row_totals = TRUE) -> t

label | Total N | Missing N | levels | Alive | Died | Total | p
--- | --- | --- | --- | --- | --- | --- | ---
Total N (%) |  |  |  | 497 (55.6) | 397 (44.4) | 894 |
Age (years) | 894 (100.0) | 0 | Mean (SD) | 59.7 (11.5) | 59.9 (12.5) | 59.7 (11.9) | 0.791
Age | 894 (100.0) | 0 | <40 years | 31 (6.2) | 35 (8.8) | 66 (7.4) | 0.024
|  |  | 40-59 years | 203 (40.8) | 129 (32.5) | 332 (37.1) |
|  |  | 60+ years | 263 (52.9) | 233 (58.7) | 496 (55.5) |
Sex | 894 (100.0) | 0 | Female | 237 (47.7) | 192 (48.4) | 429 (48.0) | 0.894
|  |  | Male | 260 (52.3) | 205 (51.6) | 465 (52.0) |
Obstruction | 894 (100.0) | 0 | No | 408 (82.1) | 312 (78.6) | 720 (80.5) | 0.219
|  |  | Yes | 89 (17.9) | 85 (21.4) | 174 (19.5) |

### 1.26 Explanatory variable defaults to factor when ≤5 distinct values

    library(finalfit)

    # Here, `extent` is a continuous variable with 4 distinct values.
    # Any continuous variable with 5 or fewer unique values is converted silently to factor
    # e.g.
    explanatory = c("extent")
    dependent = "mort_5yr"
    colon_s %>%
      summary_factorlist(dependent, explanatory) -> t

label | levels | Alive | Died
--- | --- | --- | ---
extent | 1 | 16 (3.1) | 4 (1.0)
| 2 | 78 (15.3) | 25 (6.2)
| 3 | 401 (78.5) | 349 (86.4)
| 4 | 16 (3.1) | 26 (6.4)

### 1.27 Keep as continous variable when ≤5 distinct values

    library(finalfit)

    # Here, `extent` is a continuous variable with 4 distinct values.
    # Any continuous variable with 5 or fewer unique values is converted silently to factor
    # e.g.
    explanatory = c("extent")
    dependent = "mort_5yr"
    colon_s %>%
      summary_factorlist(dependent, explanatory,
                                         cont_cut = 0) -> t

label | levels | Alive | Died
--- | --- | --- | ---
extent | Mean (SD) | 2.8 (0.5) | 3.0 (0.4)

### 1.28 Stratified crosstables

I’ve been meaning to include support for table stratification for a while. I have delayed for a good reason. Perhaps the most straightforward way to implement stratificiation is with `dplyr::group_by()`. However, the non-standard evaluation required for multiple strata may confuse as it is not implemented else where in the package.

This translates to whether variable names are passed in quotes or not.

Here is a solution, which while not that pretty, is effective.

Note that `tidyverse` functions every so often start stripping labels/attributes. Hence the addition of the help function.

    library(dplyr)
    explanatory = c("age.factor", "sex.factor")
    dependent = "perfor.factor"

    # Pick option below
    split = "rx.factor"
    split = c("rx.factor", "node4.factor")

    # Piped function to generate stratified crosstabs table
    colon_s %>%
      group_by(!!! syms(split)) %>% # Looks awkward, but avoids unquoted var names
      group_modify(~ summary_factorlist(.x, dependent, explanatory)) %>%
      ff_stratify_helper(colon_s) -> t

Treatment | >4 positive nodes | label | levels | No | Yes
--- | --- | --- | --- | --- | ---
Obs | No |  | <40 years | 14 (6.3) | 0 (0.0)
Obs | No |  | 40-59 years | 89 (40.3) | 3 (42.9)
Obs | No |  | 60+ years | 118 (53.4) | 4 (57.1)
Obs | No |  | Female | 101 (45.7) | 3 (42.9)
Obs | No |  | Male | 120 (54.3) | 4 (57.1)
Obs | Yes |  | <40 years | 10 (11.8) | 1 (50.0)
Obs | Yes |  | 40-59 years | 31 (36.5) | 1 (50.0)
Obs | Yes |  | 60+ years | 44 (51.8) | 0 (0.0)
Obs | Yes |  | Female | 44 (51.8) | 1 (50.0)
Obs | Yes |  | Male | 41 (48.2) | 1 (50.0)
Lev | No |  | <40 years | 14 (6.5) | 0 (0.0)
Lev | No |  | 40-59 years | 78 (36.3) | 3 (50.0)
Lev | No |  | 60+ years | 123 (57.2) | 3 (50.0)
Lev | No |  | Female | 89 (41.4) | 2 (33.3)
Lev | No |  | Male | 126 (58.6) | 4 (66.7)
Lev | Yes |  | <40 years | 4 (4.7) | 1 (25.0)
Lev | Yes |  | 40-59 years | 33 (38.8) | 1 (25.0)
Lev | Yes |  | 60+ years | 48 (56.5) | 2 (50.0)
Lev | Yes |  | Female | 39 (45.9) | 3 (75.0)
Lev | Yes |  | Male | 46 (54.1) | 1 (25.0)
Lev+5FU | No |  | <40 years | 15 (6.9) | 0 (0.0)
Lev+5FU | No |  | 40-59 years | 72 (33.2) | 2 (25.0)
Lev+5FU | No |  | 60+ years | 130 (59.9) | 6 (75.0)
Lev+5FU | No |  | Female | 115 (53.0) | 4 (50.0)
Lev+5FU | No |  | Male | 102 (47.0) | 4 (50.0)
Lev+5FU | Yes |  | <40 years | 11 (13.9) | 0 (NaN)
Lev+5FU | Yes |  | 40-59 years | 31 (39.2) | 0 (NaN)
Lev+5FU | Yes |  | 60+ years | 37 (46.8) | 0 (NaN)
Lev+5FU | Yes |  | Female | 44 (55.7) | 0 (NaN)
Lev+5FU | Yes |  | Male | 35 (44.3) | 0 (NaN)

### 1.29 Digits / decimal places

    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
      summary_factorlist(dependent, explanatory, p = TRUE, digits = c(1,2,3,4,0)) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.91) | 58.4 (13.30) | 0.542
Age | <40 years | 68 (7.5388) | 2 (7.4074) | 1.000
| 40-59 years | 334 (37.0288) | 10 (37.0370) |
| 60+ years | 500 (55.4324) | 15 (55.5556) |
Sex | Female | 432 (47.8936) | 13 (48.1481) | 1.000
| Male | 470 (52.1064) | 14 (51.8519) |
Obstruction | No | 715 (81.1578) | 17 (62.9630) | 0.035
| Yes | 166 (18.8422) | 10 (37.0370) |

### 1.30 Weighted tables

A simple weighting can be applied to tables. Explanatory continuous variables are multiplied by weights. Explanatory categorical variables are counted with a frequency weight (`sum(weights)`). This could be used with, say, inverse probability of treatment weightings (IPTW). The example uses random weights for demonstration purposes only. Hypothesis tests are not run on weighted data, p is set to FALSE.

    explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
    dependent = "perfor.factor"
    colon_s %>%
        mutate(my_weights = runif(929, 0, 1)) %>% # Random just to demonstrate
        summary_factorlist(dependent, explanatory, weights = "my_weights", digits = c(1, 1, 3, 1, 1))-> t

label | levels | No | Yes
--- | --- | --- | ---
Age (years) | Mean (SD) | 60.0 (12.0) | 59.2 (13.1)
Age | <40 years | 35.9 (7.7) | 0.8 (6.4)
| 40-59 years | 166.4 (35.8) | 4.1 (33.8)
| 60+ years | 263.0 (56.5) | 7.3 (59.8)
Sex | Female | 218.5 (47.0) | 6.0 (48.7)
| Male | 246.7 (53.0) | 6.3 (51.3)
Obstruction | No | 373.2 (81.9) | 7.3 (59.5)
| Yes | 82.5 (18.1) | 5.0 (40.5)

## 2 Model tables with `finalfit()`

### 2.01 Default

Logistic regression first.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770)

### 2.02 Hide reference levels

Most appropriate when all explanatory variables are continuous or well-known binary variables, such as sex.

    library(finalfit)
    explanatory = c("age", "sex.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, add_dependent_label = FALSE) %>%
        ff_remove_ref() %>%
        dependent_label(colon_s, dependent)-> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.4) | 59.9 (12.5) | 1.00 (0.99-1.01, p=0.986) | 1.00 (0.99-1.01, p=0.983)
Sex | Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.76-1.27, p=0.888)

### 2.03 Model metrics

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, metrics = TRUE) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770)

Number in dataframe = 929, Number in model = 894, Missing = 35, AIC = 1230.7, C-statistic = 0.56, H&L = Chi-sq(8) 5.69 (p=0.682)

### 2.04 Model metrics can be applied to all supported base models

    library(finalfit)
    glm(mort_5yr ~ age.factor + sex.factor + obstruct.factor + perfor.factor, data = colon_s, family = "binomial") %>%
        ff_metrics() -> t

Number in dataframe = 929, Number in model = 894, Missing = 35, AIC = 1230.7, C-statistic = 0.56, H&L = Chi-sq(8) 5.69 (p=0.682)

### 2.05 Reduced model

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    explanatory_multi = c("age.factor", "obstruct.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, explanatory_multi) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.424)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | -
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.26 (0.90-1.76, p=0.176)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | -

### 2.06 Include all models

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    explanatory_multi = c("age.factor", "obstruct.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, explanatory_multi, metrics = TRUE, keep_models = TRUE) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable full) | OR (multivariable)
--- | --- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426) | 0.81 (0.48-1.36, p=0.424)
Sex | Female | 243 (55.6) | 194 (44.4) | - | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902) | -
Obstruction | No | 408 (56.7) | 312 (43.3) | - | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186) | 1.26 (0.90-1.76, p=0.176)
Perforation | No | 497 (56.0) | 391 (44.0) | - | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770) | -

Number in dataframe = 929, Number in model = 894, Missing = 35, AIC = 1230.7, C-statistic = 0.56, H&L = Chi-sq(8) 5.69 (p=0.682)

Number in dataframe = 929, Number in model = 894, Missing = 35, AIC = 1226.8, C-statistic = 0.555, H&L = Chi-sq(8) 0.06 (p=1.000)

### 2.06 Interactions

Interactions can be specified in the normal way. Formatting the output is trickier. At the moment, we have left the default model output. This can be adjusted as necessary.

    library(finalfit)
    explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.65 (0.32-1.34, p=0.241) | 0.66 (0.32-1.36, p=0.258)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.80 (0.40-1.61, p=0.529) | 0.85 (0.42-1.71, p=0.647)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 1.24 (0.47-3.30, p=0.665) | 1.17 (0.44-3.15, p=0.752)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.26 (0.90-1.76, p=0.182)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.11 (0.50-2.41, p=0.795)
age.factor40-59 years:sex.factorMale | Interaction | - | - | 0.68 (0.23-1.97, p=0.479) | 0.74 (0.25-2.18, p=0.588)
age.factor60+ years:sex.factorMale | Interaction | - | - | 0.86 (0.30-2.39, p=0.766) | 0.89 (0.31-2.51, p=0.822)

### 2.07 Interactions: create interaction variable with two factors

    library(finalfit)
    #explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
    explanatory = c("obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        ff_interaction(age.factor, sex.factor) %>%
        finalfit(dependent, c(explanatory, "age.factor_sex.factor")) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.26 (0.90-1.76, p=0.182)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.11 (0.50-2.41, p=0.795)
Age:Sex | <40 years_Female | 18 (48.6) | 19 (51.4) | - | -
| <40 years_Male | 13 (43.3) | 17 (56.7) | 1.24 (0.47-3.30, p=0.665) | 1.17 (0.44-3.15, p=0.752)
| 40-59 years_Female | 96 (59.3) | 66 (40.7) | 0.65 (0.32-1.34, p=0.241) | 0.66 (0.32-1.36, p=0.258)
| 40-59 years_Male | 112 (63.3) | 65 (36.7) | 0.55 (0.27-1.12, p=0.100) | 0.57 (0.28-1.18, p=0.129)
| 60+ years_Female | 129 (54.2) | 109 (45.8) | 0.80 (0.40-1.61, p=0.529) | 0.85 (0.42-1.71, p=0.647)
| 60+ years_Male | 143 (52.8) | 128 (47.2) | 0.85 (0.42-1.69, p=0.638) | 0.88 (0.44-1.77, p=0.725)

### 2.08 Dependent name

The dependent name cannot be specified directly intentionally. This is to prevent errors when copying code. Re-label using `ff_label()`. The dependent prefix and suffix can also be altered.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        dplyr::mutate(
            mort_5yr = ff_label(mort_5yr, "5-year mortality")
        ) %>%
        finalfit(dependent, explanatory, dependent_label_prefix = "",
                         dependent_label_suffix = " (full model)") -> t

5-year mortality (full model) |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770)

### 2.09 Estimate name

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, estimate_name = "Odds ratio") -> t

Dependent: Mortality 5 year |  | Alive | Died | Odds ratio (univariable) | Odds ratio (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770)

### 2.10 Digits / decimal places

Number of digits to round to regression results. (1) estimate, (2) confidence interval limits, (3) p-value. Default is c(2,2,3). Trailing zeros are preserved. Number of decimal places for counts and mean (sd) / median (IQR) not currently supported. Defaults are senisble :)

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, digits = c(3,3,4)) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.542 (0.319-0.918, p=0.0230) | 0.574 (0.335-0.978, p=0.0412)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.750 (0.448-1.250, p=0.2704) | 0.810 (0.481-1.360, p=0.4261)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.981 (0.756-1.275, p=0.8886) | 0.983 (0.754-1.283, p=0.9023)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.249 (0.896-1.741, p=0.1892) | 1.255 (0.896-1.757, p=0.1859)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.180 (0.542-2.553, p=0.6716) | 1.122 (0.512-2.442, p=0.7699)

### 2.11 Confidence interval type

One of `c("profile", "default")` for GLM models (`confint.glm()`). Note, a little awkwardly, the ‘default’ setting is `profile`, rather than `default`. Profile levels are probably a little more accurate. Only go to default if taking a significant length of time for profile, i.e. data is greater than hundreds of thousands of lines.

For glmer/lmer models (`confint.merMod()`), `c("profile", "Wald", "boot")`. Not implemented for `lm()`, `coxph()` or `coxphlist`, which use default.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, confint_type = "default") -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.55-2.54, p=0.672) | 1.12 (0.52-2.43, p=0.770)

### 2.12 Confidence interval level

Probably never change this :) Note, the p-value is intentionally not included for confidence levels other than 95% to avoid confusion.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, confint_level = 0.90) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.35-0.84) | 0.57 (0.37-0.90)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.49-1.15) | 0.81 (0.52-1.25)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.79-1.22) | 0.98 (0.79-1.23)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.95-1.65) | 1.25 (0.95-1.66)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.62-2.25) | 1.12 (0.58-2.15)

### 2.13 Confidence interval separation

Some like to avoid the hyphen so as not to confuse with minus sign. Obviously not an issue in logistic regression.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory, confint_sep = " to ") -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32 to 0.92, p=0.023) | 0.57 (0.34 to 0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45 to 1.25, p=0.270) | 0.81 (0.48 to 1.36, p=0.426)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76 to 1.27, p=0.889) | 0.98 (0.75 to 1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90 to 1.74, p=0.189) | 1.25 (0.90 to 1.76, p=0.186)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54 to 2.55, p=0.672) | 1.12 (0.51 to 2.44, p=0.770)

### 2.14 Robust standard errors / confidence intervals

    explanatory = c("age", "sex.factor")
    dependent = 'mort_5yr'

    # Standard finalfit regression table
    t1 = colon_s %>%
      finalfit(dependent, explanatory, keep_fit_id = TRUE)

    # GLM with Stata-like robust standard errors
    t2 = colon_s %>%
      glmmulti(dependent, explanatory) %>%
      lmtest::coeftest(., vcov = sandwich::vcovHC(., "HC1")) %>%
      broom::tidy(conf.int = TRUE) %>%
      remove_intercept() %>%
      select(term, estimate, conf.low, conf.high, p.value) %>%
      mutate(across(c(estimate, conf.low, conf.high), exp)) %>% # or mutate_at(vars())
      as.data.frame() %>%
      condense_fit(estimate_name = "OR (multivariable robust SE)")

    ff_merge(t1, t2, last_merge = TRUE)

    #> Note: dependent includes missing data. These are dropped.
    #> Waiting for profiling to be done...
    #> Waiting for profiling to be done...
    #> Waiting for profiling to be done...

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable) | OR (multivariable robust SE)
--- | --- | --- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.4) | 59.9 (12.5) | 1.00 (0.99-1.01, p=0.986) | 1.00 (0.99-1.01, p=0.983) | 1.00 (0.99-1.01, p=0.984)
Sex | Female | 243 (55.6) | 194 (44.4) | - | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.76-1.27, p=0.888) | 0.98 (0.76-1.27, p=0.888)

### 2.15 Remove p-value

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    colon_s %>%
        finalfit(dependent, explanatory) %>%
        ff_remove_p() -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92) | 0.57 (0.34-0.98)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25) | 0.81 (0.48-1.36)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27) | 0.98 (0.75-1.28)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74) | 1.25 (0.90-1.76)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55) | 1.12 (0.51-2.44)

### 2.16 Mixed effects random-intercept model

At its simplest, a random-intercept model can be specified using a single quoted variable, e.g. `random_effect = "hospital"`. This is equivalent to `random_effect = "(1 | hospital)"`. Alternatively you can provide the full specification including parenthesis, e.g. `random_effect = "(1 | hospital) + (1 | country)"`.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    random_effect = "hospital"
    colon_s %>%
        finalfit(dependent, explanatory, random_effect = random_effect,
                         dependent_label_suffix = " (random intercept)") -> t

Dependent: Mortality 5 year (random intercept) |  | Alive | Died | OR (univariable) | OR (multilevel)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.75 (0.39-1.44, p=0.382)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 1.03 (0.55-1.96, p=0.916)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.80 (0.58-1.11, p=0.180)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.23 (0.82-1.83, p=0.320)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.03 (0.43-2.51, p=0.940)

### 2.16b Mixed effects random-intercept model with univariable estimates including random effects

Some recently asked about this and it is a good question. Here is an approach.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    random_effect = "hospital"

    colon_s %>%
        finalfit(dependent, explanatory, random_effect = random_effect, keep_fit_id = TRUE) %>%
        ff_merge(
            explanatory %>%
                purrr::map_df(~ glmmixed(colon_s, dependent, .x, random_effect = random_effect) %>%
                                                fit2df(estimate_suffix = " (univariable with RE)")),
            last_merge = TRUE
        ) %>%
        dplyr::relocate(7, .before = 6) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (univariable with RE) | OR (multilevel)
--- | --- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.68 (0.35-1.30, p=0.239) | 0.75 (0.39-1.44, p=0.382)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.93 (0.49-1.74, p=0.809) | 1.03 (0.55-1.96, p=0.916)
Sex | Female | 243 (55.6) | 194 (44.4) | - | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.81 (0.59-1.12, p=0.201) | 0.80 (0.58-1.11, p=0.180)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.23 (0.83-1.83, p=0.310) | 1.23 (0.82-1.83, p=0.320)
Perforation | No | 497 (56.0) | 391 (44.0) | - | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.07 (0.44-2.57, p=0.888) | 1.03 (0.43-2.51, p=0.940)

### 2.17 Mixed effects random-slope model

In the example below, allow the effect of age on outcome to vary by hospital. Note, this specification must have parentheses included.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    random_effect = "(age.factor | hospital)"
    colon_s %>%
        finalfit(dependent, explanatory, random_effect = random_effect,
                         dependent_label_suffix = " (random slope: age)") -> t

Dependent: Mortality 5 year (random slope: age) |  | Alive | Died | OR (univariable) | OR (multilevel)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.81 (0.37-1.81, p=0.611)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 1.08 (0.54-2.20, p=0.822)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.80 (0.58-1.11, p=0.179)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.24 (0.83-1.85, p=0.298)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.02 (0.42-2.48, p=0.967)

### 2.18 Mixed effects random-slope model directly from `lme4`

Clearly, as models get more complex, parameters such as random effect group variances may require to be extracted directly from model outputs.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    random_effect = "(age.factor | hospital)"
    colon_s %>%
        lme4::glmer(mort_5yr ~ age.factor + (age.factor | hospital), family = "binomial", data = .) %>%
        broom::tidy() -> t

### 2.19 Exclude all missing data in final model from univariable analyses

This can be useful if you want the numbers in the final table to match the final multivariable model. However, be careful to include a full explanation of this in the methods and the reason for exluding the missing data.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = 'mort_5yr'
    colon_s %>%
        dplyr::select(explanatory, dependent) %>%
        na.omit() %>%
        finalfit(dependent, explanatory) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (47.0) | 35 (53.0) | - | -
| 40-59 years | 203 (61.1) | 129 (38.9) | 0.56 (0.33-0.96, p=0.034) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 263 (53.0) | 233 (47.0) | 0.78 (0.47-1.31, p=0.356) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 237 (55.2) | 192 (44.8) | - | -
| Male | 260 (55.9) | 205 (44.1) | 0.97 (0.75-1.27, p=0.841) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 483 (55.7) | 384 (44.3) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.17 (0.54-2.53, p=0.691) | 1.12 (0.51-2.44, p=0.770)

### 2.20 Linear regression

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = 'nodes'
    colon_s %>%
        finalfit(dependent, explanatory) -> t

Dependent: nodes |  | unit | value | Coefficient (univariable) | Coefficient (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | Mean (sd) | 4.7 (4.5) | - | -
| 40-59 years | Mean (sd) | 3.6 (3.3) | -1.14 (-2.08 to -0.21, p=0.016) | -1.21 (-2.16 to -0.26, p=0.012)
| 60+ years | Mean (sd) | 3.6 (3.6) | -1.19 (-2.10 to -0.28, p=0.010) | -1.25 (-2.18 to -0.33, p=0.008)
Sex | Female | Mean (sd) | 3.7 (3.6) | - | -
| Male | Mean (sd) | 3.6 (3.6) | -0.14 (-0.60 to 0.33, p=0.565) | -0.07 (-0.54 to 0.40, p=0.779)
Obstruction | No | Mean (sd) | 3.7 (3.7) | - | -
| Yes | Mean (sd) | 3.5 (3.2) | -0.24 (-0.83 to 0.36, p=0.435) | -0.31 (-0.91 to 0.29, p=0.313)
Perforation | No | Mean (sd) | 3.7 (3.6) | - | -
| Yes | Mean (sd) | 3.9 (2.8) | 0.24 (-1.13 to 1.61, p=0.735) | 0.28 (-1.09 to 1.66, p=0.686)

### 2.21 Mixed effects random-intercept linear regression

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "nodes"
    random_effect = "hospital"
    colon_s %>%
        finalfit(dependent, explanatory, random_effect = random_effect,
                         dependent_label_suffix = " (random intercept)") -> t

Dependent: nodes (random intercept) |  | unit | value | Coefficient (univariable) | Coefficient (multilevel)
--- | --- | --- | --- | --- | ---
Age | <40 years | Mean (sd) | 4.7 (4.5) | - | -
| 40-59 years | Mean (sd) | 3.6 (3.3) | -1.14 (-2.08 to -0.21, p=0.016) | -0.79 (-1.65-0.07, p=0.035)
| 60+ years | Mean (sd) | 3.6 (3.6) | -1.19 (-2.10 to -0.28, p=0.010) | -0.98 (-1.81–0.14, p=0.011)
Sex | Female | Mean (sd) | 3.7 (3.6) | - | -
| Male | Mean (sd) | 3.6 (3.6) | -0.14 (-0.60 to 0.33, p=0.565) | -0.19 (-0.62-0.24, p=0.195)
Obstruction | No | Mean (sd) | 3.7 (3.7) | - | -
| Yes | Mean (sd) | 3.5 (3.2) | -0.24 (-0.83 to 0.36, p=0.435) | -0.37 (-0.92-0.17, p=0.091)
Perforation | No | Mean (sd) | 3.7 (3.6) | - | -
| Yes | Mean (sd) | 3.9 (2.8) | 0.24 (-1.13 to 1.61, p=0.735) | 0.23 (-1.01-1.48, p=0.357)

### 2.22 Mixed effects random-slope linear regression

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "nodes"
    random_effect = "(age.factor | hospital)"
    colon_s %>%
        finalfit(dependent, explanatory, random_effect = random_effect,
                         dependent_label_suffix = " (random slope: age)") -> t

Dependent: nodes (random slope: age) |  | unit | value | Coefficient (univariable) | Coefficient (multilevel)
--- | --- | --- | --- | --- | ---
Age | <40 years | Mean (sd) | 4.7 (4.5) | - | -
| 40-59 years | Mean (sd) | 3.6 (3.3) | -1.14 (-2.08 to -0.21, p=0.016) | -0.76 (-1.73-0.22, p=0.065)
| 60+ years | Mean (sd) | 3.6 (3.6) | -1.19 (-2.10 to -0.28, p=0.010) | -0.93 (-1.77–0.08, p=0.016)
Sex | Female | Mean (sd) | 3.7 (3.6) | - | -
| Male | Mean (sd) | 3.6 (3.6) | -0.14 (-0.60 to 0.33, p=0.565) | -0.19 (-0.62-0.24, p=0.196)
Obstruction | No | Mean (sd) | 3.7 (3.7) | - | -
| Yes | Mean (sd) | 3.5 (3.2) | -0.24 (-0.83 to 0.36, p=0.435) | -0.34 (-0.88-0.21, p=0.112)
Perforation | No | Mean (sd) | 3.7 (3.6) | - | -
| Yes | Mean (sd) | 3.9 (2.8) | 0.24 (-1.13 to 1.61, p=0.735) | 0.20 (-1.05-1.45, p=0.377)

### 2.23 Cox proportional hazards model (survival / time to event)

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "Surv(time, status)"
    colon_s %>%
        finalfit(dependent, explanatory) -> t

Dependent: Surv(time, status) |  | all | HR (univariable) | HR (multivariable)
--- | --- | --- | --- | ---
Age | <40 years | 70 (7.5) | - | -
| 40-59 years | 344 (37.0) | 0.76 (0.53-1.09, p=0.132) | 0.79 (0.55-1.13, p=0.196)
| 60+ years | 515 (55.4) | 0.93 (0.66-1.31, p=0.668) | 0.98 (0.69-1.40, p=0.926)
Sex | Female | 445 (47.9) | - | -
| Male | 484 (52.1) | 1.01 (0.84-1.22, p=0.888) | 1.02 (0.85-1.23, p=0.812)
Obstruction | No | 732 (80.6) | - | -
| Yes | 176 (19.4) | 1.29 (1.03-1.62, p=0.028) | 1.30 (1.03-1.64, p=0.026)
Perforation | No | 902 (97.1) | - | -
| Yes | 27 (2.9) | 1.17 (0.70-1.95, p=0.556) | 1.08 (0.64-1.81, p=0.785)

### 2.24 Cox proportional hazards model: change dependent label

As above, the dependent label cannot be specfied directly in the model to avoid errors. However, in survival modelling the surivial object specification can be long or awkward. Therefore, here is the work around.

    library(finalfit)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "Surv(time, status)"
    colon_s %>%
        finalfit(dependent, explanatory, add_dependent_label = FALSE) %>%
        dplyr::rename("Overall survival" = label) %>%
        dplyr::rename(" " = levels) -> t

Overall survival |  | all | HR (univariable) | HR (multivariable)
--- | --- | --- | --- | ---
Age | <40 years | 70 (7.5) | - | -
| 40-59 years | 344 (37.0) | 0.76 (0.53-1.09, p=0.132) | 0.79 (0.55-1.13, p=0.196)
| 60+ years | 515 (55.4) | 0.93 (0.66-1.31, p=0.668) | 0.98 (0.69-1.40, p=0.926)
Sex | Female | 445 (47.9) | - | -
| Male | 484 (52.1) | 1.01 (0.84-1.22, p=0.888) | 1.02 (0.85-1.23, p=0.812)
Obstruction | No | 732 (80.6) | - | -
| Yes | 176 (19.4) | 1.29 (1.03-1.62, p=0.028) | 1.30 (1.03-1.64, p=0.026)
Perforation | No | 902 (97.1) | - | -
| Yes | 27 (2.9) | 1.17 (0.70-1.95, p=0.556) | 1.08 (0.64-1.81, p=0.785)

## 3 Model tables manually using `ff_merge()`

### 3.1 Basic table

Note `summary_factorlist()` needs argument, `fit_id = TRUE`.

    library(finalfit)
    library(dplyr)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"

    ## Crosstable
    colon_s %>%
        summary_factorlist(dependent, explanatory, fit_id=TRUE) -> table_1

    ## Univariable
    colon_s %>%
        glmuni(dependent, explanatory) %>%
        fit2df(estimate_suffix=" (univariable)") -> table_2

    ## Merge

    table_1 %>%
        ff_merge(table_2) %>%
        select(-c(fit_id, index)) %>%
        dependent_label(colon_s, dependent)-> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable)
--- | --- | --- | --- | ---
Age | <40 years | 31 (6.1) | 36 (8.9) | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.54 (0.32-0.92, p=0.023)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.75 (0.45-1.25, p=0.270)
Sex | Female | 243 (47.6) | 194 (48.0) | -
| Male | 268 (52.4) | 210 (52.0) | 0.98 (0.76-1.27, p=0.889)
Obstruction | No | 408 (82.1) | 312 (78.6) | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90-1.74, p=0.189)
Perforation | No | 497 (97.3) | 391 (96.8) | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54-2.55, p=0.672)

### 3.2 Complex table (all in single pipe)

    library(finalfit)
    library(dplyr)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    random_effect = "hospital"
    dependent = "mort_5yr"

    # All in one pipe

    colon_s %>%
        ## Crosstable
        summary_factorlist(dependent, explanatory, fit_id=TRUE)  %>%

        ## Add univariable
        ff_merge(
            glmuni(colon_s, dependent, explanatory) %>%
                fit2df(estimate_suffix=" (univariable)")
        ) %>%

        ## Add multivariable
        ff_merge(
            glmmulti(colon_s, dependent, explanatory) %>%
                fit2df(estimate_suffix=" (multivariable)")
        ) %>%

        ## Add mixed effects
        ff_merge(
            glmmixed(colon_s, dependent, explanatory, random_effect) %>%
                fit2df(estimate_suffix=" (multilevel)"),
            last_merge = TRUE
        ) %>%
        dependent_label(colon_s, dependent) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable) | OR (multilevel)
--- | --- | --- | --- | --- | --- | ---
Age | <40 years | 31 (6.1) | 36 (8.9) | - | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041) | 0.75 (0.39-1.44, p=0.382)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426) | 1.03 (0.55-1.96, p=0.916)
Sex | Female | 243 (47.6) | 194 (48.0) | - | - | -
| Male | 268 (52.4) | 210 (52.0) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902) | 0.80 (0.58-1.11, p=0.180)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186) | 1.23 (0.82-1.83, p=0.320)
Perforation | No | 497 (97.3) | 391 (96.8) | - | - | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770) | 1.03 (0.43-2.51, p=0.940)

### 3.3 Other GLM models

#### Poisson

    library(finalfit)
    library(dplyr)

    ## Dobson (1990) Page 93: Randomized Controlled Trial :
    counts = c(18,17,15,20,10,20,25,13,12)
    outcome = gl(3,1,9)
    treatment = gl(3,3)
    d.AD <- data.frame(treatment, outcome, counts)

    dependent = "counts"
    explanatory = c("outcome", "treatment")

    fit_uni = d.AD %>%
        glmuni(dependent, explanatory, family = poisson) %>%
        fit2df(estimate_name = "Rate ratio (univariable)")

    fit_multi = d.AD %>%
        glmmulti(dependent, explanatory, family = poisson) %>%
        fit2df(estimate_name = "Rate ratio (multivariable)")

    # All in one pipe
    d.AD %>%
        ## Crosstable
        summary_factorlist(dependent, explanatory, cont = "median", fit_id=TRUE)  %>%

        ## Add univariable
        ff_merge(fit_uni, estimate_name = "Rate ratio") %>%

        ## Add multivariable
        ff_merge(fit_multi, estimate_name = "Rate ratio",
                         last_merge = TRUE) %>%
        dependent_label(d.AD, dependent) -> t

Dependent: counts |  | unit | value | Rate ratio (univariable) | Rate ratio (multivariable)
--- | --- | --- | --- | --- | ---
outcome | 1 | Median (IQR) | 20.0 (19.0 to 22.5) | - | -
| 2 | Median (IQR) | 13.0 (11.5 to 15.0) | 0.63 (0.42-0.94, p=0.025) | 0.63 (0.42-0.94, p=0.025)
| 3 | Median (IQR) | 15.0 (13.5 to 17.5) | 0.75 (0.51-1.09, p=0.128) | 0.75 (0.51-1.09, p=0.128)
treatment | 1 | Median (IQR) | 17.0 (16.0 to 17.5) | - | -
| 2 | Median (IQR) | 20.0 (15.0 to 20.0) | 1.00 (0.67-1.48, p=1.000) | 1.00 (0.67-1.48, p=1.000)
| 3 | Median (IQR) | 13.0 (12.5 to 19.0) | 1.00 (0.67-1.48, p=1.000) | 1.00 (0.67-1.48, p=1.000)

#### Gamma

    library(finalfit)
    library(dplyr)

    # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
    clotting <- data.frame(
        u = c(5,10,15,20,30,40,60,80,100),
        lot1 = c(118,58,42,35,27,25,21,19,18),
        lot2 = c(69,35,26,21,18,16,13,12,12))

    dependent = "lot1"
    explanatory = "log(u)"

    fit_uni = clotting %>%
        glmuni(dependent, explanatory, family = Gamma) %>%
        fit2df(estimate_name = "Coefficient", exp = FALSE, digits = c(3,3,4))

    # All in one pipe
    clotting %>%
        ## Crosstable
        summary_factorlist(dependent, explanatory, cont = "median", fit_id=TRUE)  %>%

        ## Add fit
        ff_merge(fit_uni, last_merge = TRUE) %>%
        dependent_label(colon_s, dependent) -> t

Dependent: lot1 |  | unit | value | Coefficient
--- | --- | --- | --- | ---
u | [5.0,100.0] | Median (IQR) | 27.0 (21.0 to 42.0) | -
NA | NA | NA | NA | 0.015 (0.015-0.016, p<0.0001)

### 3.4 Weighted regression

Note this is updated August 2022. Weights can specified from data and `summary_factorlist()` table is now weighted.

    library(finalfit)
    library(dplyr)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"

    colon_s %>%
        mutate(myweights = runif(dim(colon_s)[1])) %>% # random just for example
        finalfit(dependent, explanatory, weights = "myweights") -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770)

### 3.5 Using base R functions

Note `ff_formula()` convenience function to make multivariable formula (`y ~ x1 + x2 + x3` etc.) from a `dependent` and `explanatory` vector of names.

    library(finalfit)
    library(dplyr)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"

    # All in one pipe

    colon_s %>%
        ## Crosstable
        summary_factorlist(dependent, explanatory, fit_id=TRUE)  %>%

        ## Add univariable
        ff_merge(
            glmuni(colon_s, dependent, explanatory) %>%
                fit2df(estimate_suffix=" (univariable)")
        ) %>%

        ## Add multivariable
        ff_merge(
            glm(
                ff_formula(dependent, explanatory), data = colon_s, family = "binomial", weights = NULL
            ) %>%
                fit2df(estimate_suffix=" (multivariable)"),
            last_merge = TRUE
        ) %>%
        dependent_label(colon_s, dependent) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.54 (0.32-0.92, p=0.023) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.75 (0.45-1.25, p=0.270) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 243 (47.6) | 194 (48.0) | - | -
| Male | 268 (52.4) | 210 (52.0) | 0.98 (0.76-1.27, p=0.889) | 0.98 (0.75-1.28, p=0.902)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90-1.74, p=0.189) | 1.25 (0.90-1.76, p=0.186)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54-2.55, p=0.672) | 1.12 (0.51-2.44, p=0.770)

### 3.6 Edit table rows

This can be done as any dataframe would be edited.

    library(finalfit)
    library(dplyr)
    explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"

    # Run model for term test
    fit <- glm(
        ff_formula(dependent, explanatory),
        data=colon_s, family = binomial
    )

    # Not run
    #term_test <- survey::regTermTest(fit, "age.factor:sex.factor")

    # Run final table with results of term test
    colon_s %>%
        finalfit(dependent, explanatory) %>%
        rbind(c(
            "age.factor:sex.factor (overall)",
            "Interaction",
            "-",
            "-",
            "-",
            paste0("p = 0.775")
        ))-> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
Age | <40 years | 31 (46.3) | 36 (53.7) | - | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.65 (0.32-1.34, p=0.241) | 0.66 (0.32-1.36, p=0.258)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.80 (0.40-1.61, p=0.529) | 0.85 (0.42-1.71, p=0.647)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 1.24 (0.47-3.30, p=0.665) | 1.17 (0.44-3.15, p=0.752)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.25 (0.90-1.74, p=0.189) | 1.26 (0.90-1.76, p=0.182)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.18 (0.54-2.55, p=0.672) | 1.11 (0.50-2.41, p=0.795)
age.factor40-59 years:sex.factorMale | Interaction | - | - | 0.68 (0.23-1.97, p=0.479) | 0.74 (0.25-2.18, p=0.588)
age.factor60+ years:sex.factorMale | Interaction | - | - | 0.86 (0.30-2.39, p=0.766) | 0.89 (0.31-2.51, p=0.822)
age.factor:sex.factor (overall) | Interaction | - | - | - | p = 0.775

### 3.7 Base model + individual explanatory variables

This was an email enquiry about how to build on a base model. The example request was in a survival context.

This has been updated August 2019. We have left the original example of building the table from scratch as a comparison.

`ff_permute()` allows combinations of variables to be built on a base model. See options on help page to,

- include the base model and/or the full model,
- present the permuted variables at the top or bottom of the table,
- produce separate model tables, or the default which is a single table.

    library(dplyr)
    mydata = colon_s
    explanatory_base = c("age.factor", "sex.factor")
    explanatory_permute = c("obstruct.factor", "perfor.factor", "node4.factor")
    dependent = "Surv(time, status)"

    mydata %>%
        ff_permute(dependent, explanatory_base, explanatory_permute) %>%
        rename("Overall survival" = `Dependent: Surv(time, status)`, # optional tidying
                     `n (%)` = "all") -> t

Overall survival |  | n (%) | HR (univariable) | HR (multivariable) 1 | HR (multivariable) 2 | HR (multivariable) 3 | HR (multivariable) 4 | HR (multivariable) 5
--- | --- | --- | --- | --- | --- | --- | --- | ---
Age | <40 years | 70 (7.5) | - | - | - | - | - | -
| 40-59 years | 344 (37.0) | 0.76 (0.53-1.09, p=0.132) | 0.76 (0.53-1.08, p=0.129) | 0.79 (0.55-1.13, p=0.198) | 0.76 (0.53-1.08, p=0.127) | 0.85 (0.59-1.22, p=0.379) | 0.90 (0.63-1.30, p=0.590)
| 60+ years | 515 (55.4) | 0.93 (0.66-1.31, p=0.668) | 0.93 (0.66-1.31, p=0.660) | 0.98 (0.69-1.40, p=0.931) | 0.92 (0.65-1.31, p=0.656) | 1.09 (0.77-1.55, p=0.615) | 1.19 (0.83-1.69, p=0.346)
Sex | Female | 445 (47.9) | - | - | - | - | - | -
| Male | 484 (52.1) | 1.01 (0.84-1.22, p=0.888) | 1.02 (0.85-1.23, p=0.847) | 1.02 (0.85-1.24, p=0.803) | 1.02 (0.85-1.22, p=0.854) | 1.04 (0.87-1.26, p=0.647) | 1.05 (0.87-1.27, p=0.597)
Obstruction | No | 732 (80.6) | - | - | - | - | - | -
| Yes | 176 (19.4) | 1.29 (1.03-1.62, p=0.028) | - | 1.31 (1.04-1.64, p=0.022) | - | - | 1.35 (1.07-1.70, p=0.011)
Perforation | No | 902 (97.1) | - | - | - | - | - | -
| Yes | 27 (2.9) | 1.17 (0.70-1.95, p=0.556) | - | - | 1.18 (0.70-1.97, p=0.535) | - | 1.16 (0.69-1.94, p=0.581)
>4 positive nodes | No | 674 (72.6) | - | - | - | - | - | -
| Yes | 255 (27.4) | 2.60 (2.15-3.14, p<0.001) | - | - | - | 2.64 (2.18-3.19, p<0.001) | 2.68 (2.21-3.26, p<0.001)

    library(finalfit)
    library(dplyr)

    mydata = colon_s
    base_explanatory = c("age.factor", "sex.factor")
    explanatory = c("obstruct.factor", "perfor.factor", "node4.factor")
    dependent = "Surv(time, status)"

    mydata %>%
        # Counts
        summary_factorlist(dependent, c(base_explanatory,
                                                                        explanatory),
                                             column = TRUE,
                                             fit_id = TRUE) %>%

        # Univariable
        ff_merge(
            coxphuni(mydata, dependent, c(base_explanatory, explanatory)) %>%
                fit2df(estimate_suffix = " (Univariable)")
        ) %>%

        # Base
        ff_merge(
            coxphmulti(mydata, dependent, base_explanatory) %>%
                fit2df(estimate_suffix = " (Base model)")
        ) %>%

        # Model 1
        ff_merge(
            coxphmulti(mydata, dependent, c(base_explanatory, explanatory[1])) %>%
                fit2df(estimate_suffix = " (Model 1)")
        ) %>%

        # Model 2
        ff_merge(
            coxphmulti(mydata, dependent, c(base_explanatory, explanatory[2])) %>%
                fit2df(estimate_suffix = " (Model 2)")
        ) %>%

        # Model 3
        ff_merge(
            coxphmulti(mydata, dependent, c(base_explanatory, explanatory[3])) %>%
                fit2df(estimate_suffix = " (Model 3)")
        ) %>%

        # Full
        ff_merge(
            coxphmulti(mydata, dependent, c(base_explanatory, explanatory)) %>%
                fit2df(estimate_suffix = " (Full)"),
            last_merge = TRUE
        ) %>%

        # Tidy-up
        rename("Overall survival" = label) %>%
        rename(" " = levels) %>%
        rename(`n (%)` = all) -> t

Overall survival |  | n (%) | HR (Univariable) | HR (Base model) | HR (Model 1) | HR (Model 2) | HR (Model 3) | HR (Full)
--- | --- | --- | --- | --- | --- | --- | --- | ---
Age | <40 years | 70 (7.5) | - | - | - | - | - | -
| 40-59 years | 344 (37.0) | 0.76 (0.53-1.09, p=0.132) | 0.76 (0.53-1.08, p=0.129) | 0.79 (0.55-1.13, p=0.198) | 0.76 (0.53-1.08, p=0.127) | 0.85 (0.59-1.22, p=0.379) | 0.90 (0.63-1.30, p=0.590)
| 60+ years | 515 (55.4) | 0.93 (0.66-1.31, p=0.668) | 0.93 (0.66-1.31, p=0.660) | 0.98 (0.69-1.40, p=0.931) | 0.92 (0.65-1.31, p=0.656) | 1.09 (0.77-1.55, p=0.615) | 1.19 (0.83-1.69, p=0.346)
Sex | Female | 445 (47.9) | - | - | - | - | - | -
| Male | 484 (52.1) | 1.01 (0.84-1.22, p=0.888) | 1.02 (0.85-1.23, p=0.847) | 1.02 (0.85-1.24, p=0.803) | 1.02 (0.85-1.22, p=0.854) | 1.04 (0.87-1.26, p=0.647) | 1.05 (0.87-1.27, p=0.597)
Obstruction | No | 732 (80.6) | - | - | - | - | - | -
| Yes | 176 (19.4) | 1.29 (1.03-1.62, p=0.028) | - | 1.31 (1.04-1.64, p=0.022) | - | - | 1.35 (1.07-1.70, p=0.011)
Perforation | No | 902 (97.1) | - | - | - | - | - | -
| Yes | 27 (2.9) | 1.17 (0.70-1.95, p=0.556) | - | - | 1.18 (0.70-1.97, p=0.535) | - | 1.16 (0.69-1.94, p=0.581)
>4 positive nodes | No | 674 (72.6) | - | - | - | - | - | -
| Yes | 255 (27.4) | 2.60 (2.15-3.14, p<0.001) | - | - | - | 2.64 (2.18-3.19, p<0.001) | 2.68 (2.21-3.26, p<0.001)

## 4 Support for complex survey structures via `library(survey)`

### 4.1 Linear regression

Examples taken from `survey::svyglm()` help page.

    library(survey)
    library(dplyr)

    data(api)
    dependent = "api00"
    explanatory = c("ell", "meals", "mobility")

    # Label data frame
    apistrat = apistrat %>%
      mutate(
      api00 = ff_label(api00, "API in 2000 (api00)"),
      ell = ff_label(ell, "English language learners (percent)(ell)"),
      meals = ff_label(meals, "Meals eligible (percent)(meals)"),
      mobility = ff_label(mobility, "First year at the school (percent)(mobility)"),
      sch.wide = ff_label(sch.wide, "School-wide target met (sch.wide)")
      )

    # Linear example
    dependent = "api00"
    explanatory = c("ell", "meals", "mobility")

    # Stratified design
    dstrat = svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

    # Univariable fit
    fit_uni = dstrat %>%
      svyglmuni(dependent, explanatory) %>%
      fit2df(estimate_suffix = " (univariable)")

    # Multivariable fit
    fit_multi = dstrat %>%
      svyglmmulti(dependent, explanatory) %>%
      fit2df(estimate_suffix = " (multivariable)")

    # Pipe together
    apistrat %>%
      summary_factorlist(dependent, explanatory, fit_id = TRUE) %>%
      ff_merge(fit_uni) %>%
      ff_merge(fit_multi, last_merge = TRUE) %>%
      dependent_label(apistrat, dependent) -> t

Dependent: API in 2000 (api00) |  | unit | value | Coefficient (univariable) | Coefficient (multivariable)
--- | --- | --- | --- | --- | ---
English language learners (percent)(ell) | [0.0,84.0] | Mean (sd) | 652.8 (121.0) | -3.73 (-4.36–3.10, p<0.001) | -0.48 (-1.25-0.29, p=0.222)
Meals eligible (percent)(meals) | [0.0,100.0] | Mean (sd) | 652.8 (121.0) | -3.38 (-3.71–3.05, p<0.001) | -3.14 (-3.70–2.58, p<0.001)
First year at the school (percent)(mobility) | [1.0,99.0] | Mean (sd) | 652.8 (121.0) | -1.43 (-3.31-0.46, p=0.137) | 0.23 (-0.55-1.00, p=0.567)

### 4.2 Binomial example

Note model family needs specified and exponentiation set to `TRUE` if desired.

    library(survey)
    library(dplyr)

    data(api)
    dependent = "sch.wide"
    explanatory = c("ell", "meals", "mobility")

    # Label data frame
    apistrat = apistrat %>%
      mutate(
      api00 = ff_label(api00, "API in 2000 (api00)"),
      ell = ff_label(ell, "English language learners (percent)(ell)"),
      meals = ff_label(meals, "Meals eligible (percent)(meals)"),
      mobility = ff_label(mobility, "First year at the school (percent)(mobility)"),
      sch.wide = ff_label(sch.wide, "School-wide target met (sch.wide)")
      )

    # Univariable fit
    fit_uni = dstrat %>%
      svyglmuni(dependent, explanatory, family = "quasibinomial") %>%
      fit2df(exp = TRUE, estimate_name = "OR", estimate_suffix = " (univariable)")

    # Multivariable fit
    fit_multi = dstrat %>%
      svyglmmulti(dependent, explanatory, family = "quasibinomial") %>%
      fit2df(exp = TRUE, estimate_name = "OR", estimate_suffix = " (multivariable)")

    # Pipe together
    apistrat %>%
      summary_factorlist(dependent, explanatory, fit_id = TRUE) %>%
      ff_merge(fit_uni) %>%
      ff_merge(fit_multi, last_merge = TRUE) %>%
      dependent_label(apistrat, dependent) -> t

Dependent: School-wide target met (sch.wide) |  | No | Yes | OR (univariable) | OR (multivariable)
--- | --- | --- | --- | --- | ---
English language learners (percent)(ell) | Mean (SD) | 22.5 (19.3) | 20.5 (20.0) | 1.00 (0.98-1.01, p=0.715) | 1.00 (0.97-1.02, p=0.851)
Meals eligible (percent)(meals) | Mean (SD) | 46.0 (29.1) | 44.7 (29.0) | 1.00 (0.99-1.01, p=0.968) | 1.00 (0.98-1.02, p=0.732)
First year at the school (percent)(mobility) | Mean (SD) | 13.9 (8.6) | 17.2 (13.0) | 1.06 (1.00-1.12, p=0.049) | 1.06 (1.00-1.13, p=0.058)