# 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 (maximum of 5 levels) - 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
    #> Warning in chisq.test(tab, correct = FALSE): Chi-squared approximation may
    #> be incorrect

label | levels | No | Yes
--- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3)
Age | <40 years | 68 (97.1) | 2 (2.9)
| 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)
| Male | 470 (97.1) | 14 (2.9)
Obstruction | No | 715 (97.7) | 17 (2.3)
| Yes | 166 (94.3) | 10 (5.7)

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)
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union
    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 (97.1) | 2 (2.9)
| 40-59 years | 334 (97.1) | 10 (2.9)
| 60+ years | 500 (97.1) | 15 (2.9)
Gender | Female | 432 (97.1) | 13 (2.9)
| Male | 470 (97.1) | 14 (2.9)
Obstruction | No | 715 (97.7) | 17 (2.3)
| Yes | 166 (94.3) | 10 (5.7)

### 1.03 P-value for hypothesis test

Chi-squared for categorical, Kruskal-Wallis/Mann-Whitney for continuous

    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) | 0.578
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) | 0.979
| Male | 470 (97.1) | 14 (2.9) |
Obstruction | No | 715 (97.7) | 17 (2.3) | 0.018
| Yes | 166 (94.3) | 10 (5.7) |

### 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, catTest = catTestfisher) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Mean (SD) | 59.8 (11.9) | 58.4 (13.3) | 0.578
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) | 0.026
| Yes | 166 (94.3) | 10 (5.7) |

### 1.05 Median (interquartile range) instead of mean (standard deviation)

… for continuous variables.

    library(finalfit)
    explanatory = c("age", "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 (16.0) | 60.0 (18.0) | 0.578
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) | 0.979
| Male | 470 (97.1) | 14 (2.9) |
Obstruction | No | 715 (97.7) | 17 (2.3) | 0.018
| Yes | 166 (94.3) | 10 (5.7) |

### 1.06 Missing values for the explanatory variables

Always do this 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, cont = "median", na_include = TRUE) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Median (IQR) | 61.0 (16.0) | 60.0 (18.0) | 0.578
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) | 0.979
| Male | 470 (97.1) | 14 (2.9) |
Obstruction | No | 715 (97.7) | 17 (2.3) | 0.042
| Yes | 166 (94.3) | 10 (5.7) |
| Missing | 21 (100.0) | 0 (0.0) |

### 1.07 Column proportions (rather than row)

    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) -> t

label | levels | No | Yes | p
--- | --- | --- | --- | ---
Age (years) | Median (IQR) | 61.0 (16.0) | 60.0 (18.0) | 0.578
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) | 0.979
| 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) |

### 1.08 Total column

    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 (16.0) | 60.0 (18.0) | 61.0 (16.0) | 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) | 0.979
| Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.042
| Yes | 166 (18.4) | 10 (37.0) | 176 (18.9) |
| Missing | 21 (2.3) | 0 (0.0) | 21 (2.3) |

### 1.09 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) |

### 1.10 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 (16.0) | 60.0 (18.0) | 61.0 (16.0) | 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) | 0.979
| Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.042
| 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 (16.0) | 60.0 (18.0) | 61.0 (16.0) | 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) | 0.979
| Male | 470 (52.1) | 14 (51.9) | 484 (52.1) |
Obstruction | No | 715 (79.3) | 17 (63.0) | 732 (78.8) | 0.042
| Yes | 166 (18.4) | 10 (37.0) | 176 (18.9) |
| Missing | 21 (2.3) | 0 (0.0) | 21 (2.3) |

### 1.11 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 |  | Adjacent structures | Muscle | Serosa | Submucosa | Total | p
--- | --- | --- | --- | --- | --- | --- | ---
Age (years) | Median (IQR) | 61.0 (12.5) | 61.5 (14.0) | 61.0 (16.0) | 56.0 (14.0) | 61.0 (16.0) | 0.334
Age | <40 years | 4 (9.3) | 8 (7.5) | 56 (7.4) | 2 (9.5) | 70 (7.5) | 0.338
| 40-59 years | 15 (34.9) | 32 (30.2) | 285 (37.5) | 12 (57.1) | 344 (37.0) |
| 60+ years | 24 (55.8) | 66 (62.3) | 418 (55.1) | 7 (33.3) | 515 (55.4) |
Sex | Female | 19 (44.2) | 47 (44.3) | 366 (48.2) | 13 (61.9) | 445 (47.9) | 0.483
| Male | 24 (55.8) | 59 (55.7) | 393 (51.8) | 8 (38.1) | 484 (52.1) |
Obstruction | No | 36 (83.7) | 88 (83.0) | 588 (77.5) | 20 (95.2) | 732 (78.8) | 0.037
| Yes | 5 (11.6) | 13 (12.3) | 157 (20.7) | 1 (4.8) | 176 (18.9) |
| Missing | 2 (4.7) | 5 (4.7) | 14 (1.8) | 0 (0.0) | 21 (2.3) |

### 1.12 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 (80.0) | 4 (20.0)
| 2 | 78 (75.7) | 25 (24.3)
| 3 | 401 (53.5) | 349 (46.5)
| 4 | 16 (38.1) | 26 (61.9)

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

    library(finalfit)
    explanatory = c("extent")
    dependent = "mort_5yr"
    colon_s %>%
      summary_factorlist(dependent, explanatory, cont_cut = 3) -> t

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

### 1.14 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 (doesn’t work with `group_by_`). This translates to whether variable names are passed in quotes or not. Finally,. `dplyr::do()` is planned for deprecation, but there is no good alternative at the moment. Anyway, here is a solution, which while not that pretty, is very effective.

    library(dplyr)
    # Piped function to generate stratified crosstabs table
    explanatory = c("age.factor", "sex.factor")
    dependent = "rx.factor"

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

    colon_s %>%
      group_by(!!! syms(split)) %>% #Looks awkward, but this keeps quoted var names (rather than unquoted)
      do(
        summary_factorlist(., dependent, explanatory, p = TRUE)
      ) %>%
      data.frame() %>%
      dependent_label(colon_s, dependent, prefix = "") %>%
      colname2label(split) -> t

Perforation | >4 positive nodes | Treatment |  | Obs | Lev | Lev.5FU | p
--- | --- | --- | --- | --- | --- | --- | ---
No | No | Age | <40 years | 14 (32.6) | 14 (32.6) | 15 (34.9) | 0.663
No | No |  | 40-59 years | 89 (37.2) | 78 (32.6) | 72 (30.1) |
No | No |  | 60+ years | 118 (31.8) | 123 (33.2) | 130 (35.0) |
No | No | Sex | Female | 101 (33.1) | 89 (29.2) | 115 (37.7) | 0.050
No | No |  | Male | 120 (34.5) | 126 (36.2) | 102 (29.3) |
No | Yes | Age | <40 years | 10 (40.0) | 4 (16.0) | 11 (44.0) | 0.322
No | Yes |  | 40-59 years | 31 (32.6) | 33 (34.7) | 31 (32.6) |
No | Yes |  | 60+ years | 44 (34.1) | 48 (37.2) | 37 (28.7) |
No | Yes | Sex | Female | 44 (34.6) | 39 (30.7) | 44 (34.6) | 0.448
No | Yes |  | Male | 41 (33.6) | 46 (37.7) | 35 (28.7) |
Yes | No | Age | <40 years | 0 (NaN) | 0 (NaN) | 0 (NaN) | 0.604
Yes | No |  | 40-59 years | 3 (37.5) | 3 (37.5) | 2 (25.0) |
Yes | No |  | 60+ years | 4 (30.8) | 3 (23.1) | 6 (46.2) |
Yes | No | Sex | Female | 3 (33.3) | 2 (22.2) | 4 (44.4) | 0.823
Yes | No |  | Male | 4 (33.3) | 4 (33.3) | 4 (33.3) |
Yes | Yes | Age | <40 years | 1 (50.0) | 1 (50.0) | NA | 0.472
Yes | Yes |  | 40-59 years | 1 (50.0) | 1 (50.0) | NA |
Yes | Yes |  | 60+ years | 0 (0.0) | 2 (100.0) | NA |
Yes | Yes | Sex | Female | 1 (25.0) | 3 (75.0) | NA | 0.540
Yes | Yes |  | Male | 1 (50.0) | 1 (50.0) | NA |

## 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 (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)

### 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 (52.4) | 210 (52.0) | 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 (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)

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 (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=0.424)
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) | 1.26 (0.90-1.76, p=0.176)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 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) | OR (multivariable reduced)
--- | --- | --- | --- | --- | --- | ---
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) | 0.57 (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) | 0.81 (0.48-1.36, p=0.424)
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) | 1.26 (0.90-1.76, p=0.176)
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) | -

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 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.65 (0.32-1.34, p=0.241) | 0.66 (0.32-1.36, p=0.258)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.80 (0.40-1.61, p=0.529) | 0.85 (0.42-1.71, p=0.647)
Sex | Female | 243 (47.6) | 194 (48.0) | - | -
| Male | 268 (52.4) | 210 (52.0) | 1.24 (0.47-3.30, p=0.665) | 1.17 (0.44-3.15, p=0.752)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90-1.74, p=0.189) | 1.26 (0.90-1.76, p=0.182)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54-2.55, p=0.672) | 1.11 (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 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90-1.74, p=0.189) | 1.26 (0.90-1.76, p=0.182)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54-2.55, p=0.672) | 1.11 (0.50-2.41, p=0.795)
Age:Sex | <40 years|Female | 18 (3.5) | 19 (4.7) | - | -
| <40 years|Male | 13 (2.5) | 17 (4.2) | 1.24 (0.47-3.30, p=0.665) | 1.17 (0.44-3.15, p=0.752)
| 40-59 years|Female | 96 (18.8) | 66 (16.3) | 0.65 (0.32-1.34, p=0.241) | 0.66 (0.32-1.36, p=0.258)
| 40-59 years|Male | 112 (21.9) | 65 (16.1) | 0.55 (0.27-1.12, p=0.100) | 0.57 (0.28-1.18, p=0.129)
| 60+ years|Female | 129 (25.2) | 109 (27.0) | 0.80 (0.40-1.61, p=0.529) | 0.85 (0.42-1.71, p=0.647)
| 60+ years|Male | 143 (28.0) | 128 (31.7) | 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 (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)

### 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 (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)

### 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 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.542 (0.319-0.918, p=0.0230) | 0.574 (0.335-0.978, p=0.0412)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.750 (0.448-1.250, p=0.2704) | 0.810 (0.481-1.360, p=0.4261)
Sex | Female | 243 (47.6) | 194 (48.0) | - | -
| Male | 268 (52.4) | 210 (52.0) | 0.981 (0.756-1.275, p=0.8886) | 0.983 (0.754-1.283, p=0.9023)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.249 (0.896-1.741, p=0.1892) | 1.255 (0.896-1.757, p=0.1859)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 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 (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 (0.55-2.54, 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 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.54 (0.35-0.84) | 0.57 (0.37-0.90)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.75 (0.49-1.15) | 0.81 (0.52-1.25)
Sex | Female | 243 (47.6) | 194 (48.0) | - | -
| Male | 268 (52.4) | 210 (52.0) | 0.98 (0.79-1.22) | 0.98 (0.79-1.23)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.95-1.65) | 1.25 (0.95-1.66)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 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 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.54 (0.32 to 0.92, p=0.023) | 0.57 (0.34 to 0.98, p=0.041)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.75 (0.45 to 1.25, p=0.270) | 0.81 (0.48 to 1.36, p=0.426)
Sex | Female | 243 (47.6) | 194 (48.0) | - | -
| Male | 268 (52.4) | 210 (52.0) | 0.98 (0.76 to 1.27, p=0.889) | 0.98 (0.75 to 1.28, p=0.902)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90 to 1.74, p=0.189) | 1.25 (0.90 to 1.76, p=0.186)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54 to 2.55, p=0.672) | 1.12 (0.51 to 2.44, p=0.770)

### 2.14 Mixed effects random-intercept model

At its simplest, a random-intercept model can be specified using a single quoted variable. In this example, it is the equivalent of quoting `random_effect = "(1 | hospital)"`.

    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 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.54 (0.32-0.92, p=0.023) | 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) | 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) | 0.80 (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) | 1.23 (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) | 1.03 (0.43-2.51, p=0.940)

### 2.15 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 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.54 (0.32-0.92, p=0.023) | 0.81 (0.37-1.81, p=0.611)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.75 (0.45-1.25, p=0.270) | 1.08 (0.54-2.20, p=0.822)
Sex | Female | 243 (47.6) | 194 (48.0) | - | -
| Male | 268 (52.4) | 210 (52.0) | 0.98 (0.76-1.27, p=0.889) | 0.80 (0.58-1.11, p=0.179)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90-1.74, p=0.189) | 1.24 (0.83-1.85, p=0.298)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54-2.55, p=0.672) | 1.02 (0.42-2.48, p=0.967)

### 2.16 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

term | estimate | std.error | statistic | p.value | group
--- | --- | --- | --- | --- | ---
(Intercept) | -0.2537662 | 0.8983060 | -0.2824941 | 0.7775646 | fixed
age.factor40-59 years | -0.3285638 | 0.3830047 | -0.8578582 | 0.3909707 | fixed
age.factor60+ years | -0.0531730 | 0.3450263 | -0.1541128 | 0.8775208 | fixed
sd_(Intercept).hospital | 1.8670680 | NA | NA | NA | hospital
sd_age.factor40-59 years.hospital | 0.3382630 | NA | NA | NA | hospital
sd_age.factor60+ years.hospital | 0.0826644 | NA | NA | NA | hospital
cor_(Intercept).age.factor40-59 years.hospital | -0.9999999 | NA | NA | NA | hospital
cor_(Intercept).age.factor60+ years.hospital | -0.9999997 | NA | NA | NA | hospital
cor_age.factor40-59 years.age.factor60+ years.hospital | 0.9999998 | NA | NA | NA | hospital

### 2.17 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 (6.2) | 35 (8.8) | - | -
| 40-59 years | 203 (40.8) | 129 (32.5) | 0.56 (0.33-0.96, p=0.034) | 0.57 (0.34-0.98, p=0.041)
| 60+ years | 263 (52.9) | 233 (58.7) | 0.78 (0.47-1.31, p=0.356) | 0.81 (0.48-1.36, p=0.426)
Sex | Female | 237 (47.7) | 192 (48.4) | - | -
| Male | 260 (52.3) | 205 (51.6) | 0.97 (0.75-1.27, p=0.841) | 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 | 483 (97.2) | 384 (96.7) | - | -
| Yes | 14 (2.8) | 13 (3.3) | 1.17 (0.54-2.53, p=0.691) | 1.12 (0.51-2.44, p=0.770)

### 2.18 Linear regression

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

Dependent: nodes |  | Mean (sd) | Coefficient (univariable) | Coefficient (multivariable)
--- | --- | --- | --- | ---
Age | <40 years | 4.7 (4.5) | - | -
| 40-59 years | 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 | 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 | 3.7 (3.6) | - | -
| Male | 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 | 3.7 (3.7) | - | -
| Yes | 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 | 3.7 (3.6) | - | -
| Yes | 3.9 (2.8) | 0.24 (-1.13 to 1.61, p=0.735) | 0.28 (-1.09 to 1.66, p=0.686)

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

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

### 2.21 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)

### 2.22 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 (46.3) | 36 (53.7) | -
| 40-59 years | 208 (61.4) | 131 (38.6) | 0.54 (0.32-0.92, p=0.023)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.75 (0.45-1.25, p=0.270)
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)
Perforation | No | 497 (56.0) | 391 (44.0) | -
| Yes | 14 (51.9) | 13 (48.1) | 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)")
        ) %>%
        select(-c(fit_id, index)) %>%
        dependent_label(colon_s, dependent) -> t

Dependent: Mortality 5 year |  | Alive | Died | OR (univariable) | OR (multivariable) | 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) | 0.57 (0.34-0.98, p=0.041) | 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) | 0.81 (0.48-1.36, p=0.426) | 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) | 0.98 (0.75-1.28, p=0.902) | 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) | 1.25 (0.90-1.76, p=0.186) | 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) | 1.12 (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") %>%

        select(-c(fit_id, index)) %>%
        dependent_label(d.AD, dependent) -> t

Dependent: counts |  | Median (IQR) | Rate ratio (univariable) | Rate ratio (multivariable)
--- | --- | --- | --- | ---
outcome | 1 | 20.0 (19.0 to 22.5) | - | -
| 2 | 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 | 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 | 17.0 (16.0 to 17.5) | - | -
| 2 | 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 | 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) %>%

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

Dependent: lot1 |  | Median (IQR) | Coefficient
--- | --- | --- | ---
log(u) | [1.61,4.61] | 27.0 (21.0 to 42.0) | 0.015 (0.015-0.016, p<0.0001)

### 3.4 Weighted regression

    library(finalfit)
    library(dplyr)
    explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
    dependent = "mort_5yr"
    weights = runif(dim(colon_s)[1]) # random just for example

    # All in one pipe
    colon_s %>%
        ## Crosstable
        summary_factorlist(dependent, explanatory, fit_id=TRUE)  %>%

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

        ## Add multivariable
        ff_merge(
            glmmulti(colon_s, dependent, explanatory, weights = weights, family = quasibinomial) %>%
                fit2df(estimate_suffix=" (multivariable)")
        ) %>%
        select(-c(fit_id, index)) %>%
        dependent_label(colon_s, dependent) -> 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) | 0.51 (0.29-0.87, p=0.014) | 0.54 (0.31-0.94, p=0.030)
| 60+ years | 272 (53.4) | 237 (46.6) | 0.76 (0.45-1.28, p=0.305) | 0.83 (0.49-1.41, p=0.493)
Sex | Female | 243 (55.6) | 194 (44.4) | - | -
| Male | 268 (56.1) | 210 (43.9) | 0.91 (0.70-1.19, p=0.494) | 0.92 (0.70-1.20, p=0.517)
Obstruction | No | 408 (56.7) | 312 (43.3) | - | -
| Yes | 89 (51.1) | 85 (48.9) | 1.16 (0.82-1.62, p=0.401) | 1.18 (0.83-1.66, p=0.354)
Perforation | No | 497 (56.0) | 391 (44.0) | - | -
| Yes | 14 (51.9) | 13 (48.1) | 1.00 (0.46-2.13, p=0.993) | 1.00 (0.46-2.14, p=0.994)

### 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)")
        ) %>%

        select(-c(fit_id, index)) %>%
        dependent_label(colon_s, dependent) -> 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)

### 3.6 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 (6.1) | 36 (8.9) | - | -
| 40-59 years | 208 (40.7) | 131 (32.4) | 0.65 (0.32-1.34, p=0.241) | 0.66 (0.32-1.36, p=0.258)
| 60+ years | 272 (53.2) | 237 (58.7) | 0.80 (0.40-1.61, p=0.529) | 0.85 (0.42-1.71, p=0.647)
Sex | Female | 243 (47.6) | 194 (48.0) | - | -
| Male | 268 (52.4) | 210 (52.0) | 1.24 (0.47-3.30, p=0.665) | 1.17 (0.44-3.15, p=0.752)
Obstruction | No | 408 (82.1) | 312 (78.6) | - | -
| Yes | 89 (17.9) | 85 (21.4) | 1.25 (0.90-1.74, p=0.189) | 1.26 (0.90-1.76, p=0.182)
Perforation | No | 497 (97.3) | 391 (96.8) | - | -
| Yes | 14 (2.7) | 13 (3.2) | 1.18 (0.54-2.55, p=0.672) | 1.11 (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)")
        ) %>%

        # Tidy-up
        select(-c(fit_id, index)) %>%
        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) %>%
      select(-fit_id, -index) %>%
      dependent_label(apistrat, dependent) -> t

Dependent: API in 2000 (api00) |  | Mean (sd) | Coefficient (univariable) | Coefficient (multivariable)
--- | --- | --- | --- | ---
English language learners (percent)(ell) | [0,84] | 652.8 (121.0) | -3.73 (-4.35–3.11, p<0.001) | -0.48 (-1.25-0.29, p=0.222)
Meals eligible (percent)(meals) | [0,100] | 652.8 (121.0) | -3.38 (-3.71–3.05, p<0.001) | -3.14 (-3.70–2.59, p<0.001)
First year at the school (percent)(mobility) | [1,99] | 652.8 (121.0) | -1.43 (-3.30-0.44, p=0.137) | 0.23 (-0.54-1.00, 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) %>%
      select(-fit_id, -index) %>%
      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 (0.98-1.01, 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)