An "all-in-one" function that takes a single dependent variable with a vector of explanatory variable names (continuous or categorical variables) to produce a final table for publication including summary statistics. The appropriate model is selected on the basis of dependent variable and whether a random effect is specified.

finalfit.lm method (not called directly)

finalfit.glm method (not called directly)

finalfit.coxph method (not called directly)

finalfit(
  .data,
  dependent,
  explanatory,
  explanatory_multi = NULL,
  random_effect = NULL,
  column = FALSE,
  keep_models = FALSE,
  metrics = FALSE,
  add_dependent_label = TRUE,
  dependent_label_prefix = "Dependent: ",
  dependent_label_suffix = "",
  keep_fit_id = FALSE,
  ...
)

finalfit.lm(
  .data,
  dependent,
  explanatory,
  explanatory_multi = NULL,
  random_effect = NULL,
  column = FALSE,
  keep_models = FALSE,
  metrics = FALSE,
  add_dependent_label = TRUE,
  dependent_label_prefix = "Dependent: ",
  dependent_label_suffix = "",
  keep_fit_id = FALSE,
  ...
)

finalfit.glm(
  .data,
  dependent,
  explanatory,
  explanatory_multi = NULL,
  random_effect = NULL,
  column = FALSE,
  keep_models = FALSE,
  metrics = FALSE,
  add_dependent_label = TRUE,
  dependent_label_prefix = "Dependent: ",
  dependent_label_suffix = "",
  keep_fit_id = FALSE,
  ...
)

finalfit.coxph(
  .data,
  dependent,
  explanatory,
  explanatory_multi = NULL,
  random_effect = NULL,
  column = TRUE,
  keep_models = FALSE,
  metrics = FALSE,
  add_dependent_label = TRUE,
  dependent_label_prefix = "Dependent: ",
  dependent_label_suffix = "",
  keep_fit_id = FALSE,
  ...
)

Arguments

.data

Data frame or tibble.

dependent

Character vector of length 1: quoted name of dependent variable. Can be continuous, a binary factor, or a survival object of form Surv(time, status).

explanatory

Character vector of any length: quoted name(s) of explanatory variables.

explanatory_multi

Character vector of any length: quoted name(s) of a subset of explanatory variables to generate reduced multivariable model (must only contain variables contained in explanatory).

random_effect

Character vector of length 1, either, (1) name of random intercept variable, e.g. "var1", (automatically convered to "(1 | var1)"); or, (2) the full lme4 specification, e.g. "(var1 | var2)". Note parenthesis MUST be included in (2) but NOT included in (1).

column

Logical: Compute margins by column rather than row.

keep_models

Logical: include full multivariable model in output when working with reduced multivariable model (explanatory_multi) and/or mixed effect models (random_effect).

metrics

Logical: include useful model metrics in output in publication format.

add_dependent_label

Add the name of the dependent label to the top left of table.

dependent_label_prefix

Add text before dependent label.

dependent_label_suffix

Add text after dependent label.

keep_fit_id

Keep original model output coefficient label (internal).

...

Other arguments to pass to fit2df: estimate_name, digits, confint_type, confint_level, confint_sep.

Value

Returns a data frame with the final model table.

Examples

library(finalfit) library(dplyr) # Summary, univariable and multivariable analyses of the form: # glm(depdendent ~ explanatory, family="binomial") # lmuni(), lmmulti(), lmmixed(), glmuni(), glmmulti(), glmmixed(), glmmultiboot(), # coxphuni(), coxphmulti() data(colon_s) # Modified from survival::colon explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% finalfit(dependent, explanatory)
#> Note: dependent includes missing data. These are dropped.
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#> Dependent: Mortality 5 year Alive Died #> Age <40 years 31 (46.3) 36 (53.7) #> 40-59 years 208 (61.4) 131 (38.6) #> 60+ years 272 (53.4) 237 (46.6) #> Sex Female 243 (55.6) 194 (44.4) #> Male 268 (56.1) 210 (43.9) #> Obstruction No 408 (56.7) 312 (43.3) #> Yes 89 (51.1) 85 (48.9) #> Perforation No 497 (56.0) 391 (44.0) #> Yes 14 (51.9) 13 (48.1) #> OR (univariable) OR (multivariable) #> - - #> 0.54 (0.32-0.92, p=0.023) 0.57 (0.34-0.98, p=0.041) #> 0.75 (0.45-1.25, p=0.270) 0.81 (0.48-1.36, p=0.426) #> - - #> 0.98 (0.76-1.27, p=0.889) 0.98 (0.75-1.28, p=0.902) #> - - #> 1.25 (0.90-1.74, p=0.189) 1.25 (0.90-1.76, p=0.186) #> - - #> 1.18 (0.54-2.55, p=0.672) 1.12 (0.51-2.44, p=0.770)
# Multivariable analysis with subset of explanatory # variable set used in univariable analysis 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)
#> Note: dependent includes missing data. These are dropped.
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#> Dependent: Mortality 5 year Alive Died #> Age <40 years 31 (46.3) 36 (53.7) #> 40-59 years 208 (61.4) 131 (38.6) #> 60+ years 272 (53.4) 237 (46.6) #> Sex Female 243 (55.6) 194 (44.4) #> Male 268 (56.1) 210 (43.9) #> Obstruction No 408 (56.7) 312 (43.3) #> Yes 89 (51.1) 85 (48.9) #> Perforation No 497 (56.0) 391 (44.0) #> Yes 14 (51.9) 13 (48.1) #> OR (univariable) OR (multivariable) #> - - #> 0.54 (0.32-0.92, p=0.023) 0.57 (0.34-0.98, p=0.041) #> 0.75 (0.45-1.25, p=0.270) 0.81 (0.48-1.36, p=0.424) #> - - #> 0.98 (0.76-1.27, p=0.889) - #> - - #> 1.25 (0.90-1.74, p=0.189) 1.26 (0.90-1.76, p=0.176) #> - - #> 1.18 (0.54-2.55, p=0.672) -
# Summary, univariable and multivariable analyses of the form: # lme4::glmer(dependent ~ explanatory + (1 | random_effect), family="binomial") explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") explanatory_multi = c("age.factor", "obstruct.factor") random_effect = "hospital" dependent = "mort_5yr" # colon_s %>% # finalfit(dependent, explanatory, explanatory_multi, random_effect) # Include model metrics: colon_s %>% finalfit(dependent, explanatory, explanatory_multi, metrics=TRUE)
#> Note: dependent includes missing data. These are dropped.
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#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
#> [[1]] #> Dependent: Mortality 5 year Alive Died #> Age <40 years 31 (46.3) 36 (53.7) #> 40-59 years 208 (61.4) 131 (38.6) #> 60+ years 272 (53.4) 237 (46.6) #> Sex Female 243 (55.6) 194 (44.4) #> Male 268 (56.1) 210 (43.9) #> Obstruction No 408 (56.7) 312 (43.3) #> Yes 89 (51.1) 85 (48.9) #> Perforation No 497 (56.0) 391 (44.0) #> Yes 14 (51.9) 13 (48.1) #> OR (univariable) OR (multivariable) #> - - #> 0.54 (0.32-0.92, p=0.023) 0.57 (0.34-0.98, p=0.041) #> 0.75 (0.45-1.25, p=0.270) 0.81 (0.48-1.36, p=0.424) #> - - #> 0.98 (0.76-1.27, p=0.889) - #> - - #> 1.25 (0.90-1.74, p=0.189) 1.26 (0.90-1.76, p=0.176) #> - - #> 1.18 (0.54-2.55, p=0.672) - #> #> [[2]] #> #> 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) #>
# Summary, univariable and multivariable analyses of the form: # survival::coxph(dependent ~ explanatory) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "Surv(time, status)" colon_s %>% finalfit(dependent, explanatory)
#> Dependent: Surv(time, status) all #> Age <40 years 70 (100.0) #> 40-59 years 344 (100.0) #> 60+ years 515 (100.0) #> Sex Female 445 (100.0) #> Male 484 (100.0) #> Obstruction No 732 (100.0) #> Yes 176 (100.0) #> Perforation No 902 (100.0) #> Yes 27 (100.0) #> HR (univariable) HR (multivariable) #> - - #> 0.76 (0.53-1.09, p=0.132) 0.79 (0.55-1.13, p=0.196) #> 0.93 (0.66-1.31, p=0.668) 0.98 (0.69-1.40, p=0.926) #> - - #> 1.01 (0.84-1.22, p=0.888) 1.02 (0.85-1.23, p=0.812) #> - - #> 1.29 (1.03-1.62, p=0.028) 1.30 (1.03-1.64, p=0.026) #> - - #> 1.17 (0.70-1.95, p=0.556) 1.08 (0.64-1.81, p=0.785)
# Rather than going all-in-one, any number of subset models can # be manually added on to a summary_factorlist() table using finalfit.merge(). # This is particularly useful when models take a long-time to run or are complicated. # Note requirement for fit_id=TRUE. # `fit2df` is a subfunction extracting most common models to a dataframe. explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% finalfit(dependent, explanatory, metrics=TRUE)
#> Note: dependent includes missing data. These are dropped.
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#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
#> [[1]] #> Dependent: Mortality 5 year Alive Died #> Age <40 years 31 (46.3) 36 (53.7) #> 40-59 years 208 (61.4) 131 (38.6) #> 60+ years 272 (53.4) 237 (46.6) #> Sex Female 243 (55.6) 194 (44.4) #> Male 268 (56.1) 210 (43.9) #> Obstruction No 408 (56.7) 312 (43.3) #> Yes 89 (51.1) 85 (48.9) #> Perforation No 497 (56.0) 391 (44.0) #> Yes 14 (51.9) 13 (48.1) #> OR (univariable) OR (multivariable) #> - - #> 0.54 (0.32-0.92, p=0.023) 0.57 (0.34-0.98, p=0.041) #> 0.75 (0.45-1.25, p=0.270) 0.81 (0.48-1.36, p=0.426) #> - - #> 0.98 (0.76-1.27, p=0.889) 0.98 (0.75-1.28, p=0.902) #> - - #> 1.25 (0.90-1.74, p=0.189) 1.25 (0.90-1.76, p=0.186) #> - - #> 1.18 (0.54-2.55, p=0.672) 1.12 (0.51-2.44, p=0.770) #> #> [[2]] #> #> 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) #>
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") explanatory_multi = c("age.factor", "obstruct.factor") random_effect = "hospital" dependent = 'mort_5yr' # Separate tables colon_s %>% summary_factorlist(dependent, explanatory, fit_id=TRUE) -> example.summary
#> Note: dependent includes missing data. These are dropped.
colon_s %>% glmuni(dependent, explanatory) %>% fit2df(estimate_suffix=" (univariable)") -> example.univariable
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colon_s %>% glmmulti(dependent, explanatory) %>% fit2df(estimate_suffix=" (multivariable)") -> example.multivariable
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# Edited as CRAN slow to run these # colon_s %>% # glmmixed(dependent, explanatory, random_effect) %>% # fit2df(estimate_suffix=" (multilevel") -> example.multilevel # Pipe together example.summary %>% finalfit_merge(example.univariable) %>% finalfit_merge(example.multivariable, last_merge = TRUE)
#> label levels Alive Died OR (univariable) #> 1 Age <40 years 31 (6.1) 36 (8.9) - #> 2 40-59 years 208 (40.7) 131 (32.4) 0.54 (0.32-0.92, p=0.023) #> 3 60+ years 272 (53.2) 237 (58.7) 0.75 (0.45-1.25, p=0.270) #> 8 Sex Female 243 (47.6) 194 (48.0) - #> 9 Male 268 (52.4) 210 (52.0) 0.98 (0.76-1.27, p=0.889) #> 4 Obstruction No 408 (82.1) 312 (78.6) - #> 5 Yes 89 (17.9) 85 (21.4) 1.25 (0.90-1.74, p=0.189) #> 6 Perforation No 497 (97.3) 391 (96.8) - #> 7 Yes 14 (2.7) 13 (3.2) 1.18 (0.54-2.55, p=0.672) #> OR (multivariable) #> 1 - #> 2 0.57 (0.34-0.98, p=0.041) #> 3 0.81 (0.48-1.36, p=0.426) #> 8 - #> 9 0.98 (0.75-1.28, p=0.902) #> 4 - #> 5 1.25 (0.90-1.76, p=0.186) #> 6 - #> 7 1.12 (0.51-2.44, p=0.770)
# finalfit_merge(example.multilevel)