Tables gallery

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 has not been implemented, given it is so easy to 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 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)

1.05 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)

1.06 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)

1.07 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)	NA	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)

1.08 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)

1.09 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)	NA	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)	NA	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)

1.10 Dependent variable with 0 to 5 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)	NA	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)

1.11 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)

1.12 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)

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)	0.45 (0.19 to 1.07, p=0.035)
	60+ years	3.6 (3.6)	-1.19 (-2.10 to -0.28, p=0.010)	0.38 (0.16 to 0.87, p=0.011)
Sex	Female	3.7 (3.6)	-	-
	Male	3.6 (3.6)	-0.14 (-0.60 to 0.33, p=0.565)	0.83 (0.54 to 1.27, p=0.195)
Obstruction	No	3.7 (3.7)	-	-
	Yes	3.5 (3.2)	-0.24 (-0.83 to 0.36, p=0.435)	0.69 (0.40 to 1.19, p=0.091)
Perforation	No	3.7 (3.6)	-	-
	Yes	3.9 (2.8)	0.24 (-1.13 to 1.61, p=0.735)	1.26 (0.36 to 4.40, 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)	0.47 (0.18 to 1.25, p=0.065)
	60+ years	3.6 (3.6)	-1.19 (-2.10 to -0.28, p=0.010)	0.40 (0.17 to 0.92, p=0.016)
Sex	Female	3.7 (3.6)	-	-
	Male	3.6 (3.6)	-0.14 (-0.60 to 0.33, p=0.565)	0.83 (0.54 to 1.27, p=0.196)
Obstruction	No	3.7 (3.7)	-	-
	Yes	3.5 (3.2)	-0.24 (-0.83 to 0.36, p=0.435)	0.71 (0.41 to 1.23, p=0.112)
Perforation	No	3.7 (3.6)	-	-
	Yes	3.9 (2.8)	0.24 (-1.13 to 1.61, p=0.735)	1.22 (0.35 to 4.24, 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)		HR (univariable)	HR (multivariable)
Age	<40 years	-	-
	40-59 years	0.76 (0.53-1.09, p=0.132)	0.79 (0.55-1.13, p=0.196)
	60+ years	0.93 (0.66-1.31, p=0.668)	0.98 (0.69-1.40, p=0.926)
Sex	Female	-	-
	Male	1.01 (0.84-1.22, p=0.888)	1.02 (0.85-1.23, p=0.812)
Obstruction	No	-	-
	Yes	1.29 (1.03-1.62, p=0.028)	1.30 (1.03-1.64, p=0.026)
Perforation	No	-	-
	Yes	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		HR (univariable)	HR (multivariable)
Age	<40 years	-	-
	40-59 years	0.76 (0.53-1.09, p=0.132)	0.79 (0.55-1.13, p=0.196)
	60+ years	0.93 (0.66-1.31, p=0.668)	0.98 (0.69-1.40, p=0.926)
Sex	Female	-	-
	Male	1.01 (0.84-1.22, p=0.888)	1.02 (0.85-1.23, p=0.812)
Obstruction	No	-	-
	Yes	1.29 (1.03-1.62, p=0.028)	1.30 (1.03-1.64, p=0.026)
Perforation	No	-	-
	Yes	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 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)

3.4 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

Ewen Harrison

1 Cross tables

1.01 Default

1.02 Add or edit variable labels

1.03 P-value for hypothesis test

1.04 Median (interquartile range) instead of mean (standard deviation)

1.05 Missing values for the explanatory variables

1.06 Column proportions (rather than row)

1.07 Total column

1.08 Order a variable by total

1.09 Label with dependent name

1.10 Dependent variable with 0 to 5 factor levels supported

1.11 Explanatory variable defaults to factor when ≤5 distinct values

1.12 Keep as continous variable when ≤5 distinct values

2 Model tables with finalfit()

2.01 Default

2.02 Hide reference levels

2.03 Model metrics

2.04 Model metrics can be applied to all supported base models

2.05 Reduced model

2.06 Include all models

2.06 Interactions

2.07 Interactions: create interaction variable with two factors

2.08 Dependent name

2.09 Estimate name

2.10 Digits / decimal places

2.11 Confidence interval type

2.12 Confidence interval level

2.13 Confidence interval separation

2.14 Mixed effects random-intercept model

2.15 Mixed effects random-slope model

2.16 Mixed effects random-slope model directly from lme4

2.17 Exclude all missing data in final model from univariable analyses

2.18 Linear regression

2.19 Mixed effects random-intercept linear regression

2.20 Mixed effects random-slope linear regression

2.21 Cox proportional hazards model (survival / time to event)

2.22 Cox proportional hazards model: change dependent label

3 Model tables manually using ff_merge()

3.1 Basic table

3.2 Complex table (all in single pipe)

3.3 Using base R functions

3.4 Edit table rows

Contents

1.09 Label with `dependent` name

2 Model tables with `finalfit()`

2.16 Mixed effects random-slope model directly from `lme4`

3 Model tables manually using `ff_merge()`