ewenharrison / finalfit Goto Github PK

Hi, I am currently conducting an analysis of missing data for my MA dissertation using the finalfit package. I am having some issues with the function missing_compare. I have posted a full description here. ff-glimpse, missing_pairs and finalit functions do actually work.
Anyway, my code is the following

explanatory_edu  <- c("ch_edu", "a4g")
dependent <- "br"
sl_cfa %>% 
  missing_compare(dependent, explanatory_edu)

and I am getting the following error message

> sl_cfa %>% 
+   missing_compare(dependent, explanatory_edu)
Error in factor(g, levels = unique(g)) : object 'g' not found

All three variables are dichotomous factors without labels

> str(sl_cfa$br)
 Factor w/ 2 levels "0","1": NA 2 2 1 1 NA 2 2 1 1 ...
> str(sl_cfa$ch_edu)
 Factor w/ 2 levels "0","1": 1 2 NA 1 1 NA 1 1 2 1 ...
> str(sl_cfa$a4g)
 Factor w/ 2 levels "0","1": NA 1 NA NA NA NA 1 2 1 2 ...

Let's create toy dataset:

set.seed(1)
dat<-data.frame(
   y=rnorm(10),
   x1=gl(2,5, labels=c("A","B")),
   x2=(1:10)
 )

And use finalfit with y as dependent variable:

dat %>% finalfit.lm(dependent =  'y', explanatory =  c('x1','x2'))


  Dependent: y         Mean (sd)      Coefficient (univariable)    Coefficient (multivariable)
1           x1      A  0.4 (0.8)                              -                              -
2                   B -0.4 (1.1) -0.84 (-2.24 to 0.56, p=0.205) -2.19 (-4.99 to 0.60, p=0.106)
3           x2 [1,10]  0.0 (1.0) -0.06 (-0.33 to 0.21, p=0.610)  0.27 (-0.22 to 0.76, p=0.230)

So far so good. But when I make x2 non-integer, the same code fails:

set.seed(1)
dat<-data.frame(
   y=rnorm(10),
   x1=gl(2,5, labels=c("A","B")),
   x2=(1:10)/10
 )
 dat %>% finalfit.lm(dependent =  'y', explanatory =  c('x1','x2'))

Error in if (cj == upper) next : missing value where TRUE/FALSE needed

I tried several other integer and non-integer expalnatory variables. Results are the same.

What am I missing?

Hello. Your finalfit package is very nice. I very often use this package. Thank you!
Today, I updated this package to ver. 1.00.

I used na_to_p = FALSE option because I didn't use missing values to statistical tests.
But calculated fisher P included missing values.
Next, using dependent_label_prefix = "" option resulted in excluding missing for some reason.

First, thanks for the great tool!

I guess univariate results are not the proper ones to show in a forest plot but I would like to see the comparison between both univariate and multivariate. Could it be possible to add an option to this in hr_plot? As a quick workaround I guess it could be easy to parse coxphuni table as a source for hr_plot, right?

Also, I was wondering how subgroups could be plotted in the same forest plot. Something like I have HR for the whole cohort and then HR for specific subgroups (treatment A and treatment B). Would be great to have them all plotted in the same image. Something like this or this

Hi,

Am fairly frustrated right now because I keep getting this error:

finalfit(temp,temp$diff_secs_num,as.numeric(temp$call.length))
Error in summary_factorlist(.data, dependent, explanatory, p = FALSE, :
Dependent variable length is 0
In addition: Warning message:
In if (!d_is.factor & !d_is.surv) { :
the condition has length > 1 and only the first element will be used

As you see here, the dependent is not 0:

length(temp$diff_secs_num)
[1] 1290113

Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 1.00 2.00 54.77 14.00 2996.00

Please see the difference in output between these two:

colon_t = data.frame(ind1 = c(1, 2, 3, 4, 5), ind2 = c(0, 1, 0 ,1 ,0), dep = c(0,1,2,0,1))
colon_t %>% mutate(
  dep.factor = factor(dep, levels = c(0, 1, 2))
) -> colon_t
colon_t %>% or_plot("dep.factor", c("ind1", "ind2"))

colon_u = data.frame(ind1 = c(1, 2, 3, 4, 5), ind2 = c(0, 1, 0 ,1 ,0), dep = c(0,1,0,0,1))
colon_u %>% mutate(
  dep.factor = factor(dep, levels = c(0, 1))
) -> colon_u
colon_u %>% or_plot("dep.factor", c("ind1", "ind2"))

Is it just me not knowing how statistics work, or is there some bug here?

data %>%
  finalfit(dependent, explanatory, dependent_label_prefix = "") -> table
table %>%
  knitr::kable(row.names = FALSE, align=c("l", "l", "c", "c", "l", "l"), booktabs = TRUE) %>%
  kableExtra::kable_styling(latex_options = c("scale_down"))

When I add the "metrics = TRUE" term to finalfit function, this code is no longer able to knit to a pdf in R Markdown, but without it, I can.

It gives the error "LaTex Error: Not in outer par mode." I am on a mac and am using MacTex.``

I came across this whilst working in the safe haven and have reproduced here with mtcars
This works fine:

library(finalfit)
library(tidyverse)

mtcars$am <- factor(mtcars$am)

explanatory = c("cyl", "hp", "wt")
dependent = "am"

mtcars %>% 
  finalfit(dependent, explanatory) -> tab_1
tab_1

But if I try this: (without directly loading Hmisc)

Hmisc::label(mtcars$cyl) = "Cylinders"
Hmisc::label(mtcars$hp) = "Horsepower"
Hmisc::label(mtcars$wt) = "Weight"
Hmisc::label(mtcars$am) = "Auto or Manual"

explanatory = c("cyl", "hp", "wt")
dependent = "am"

mtcars %>% 
  finalfit(dependent, explanatory) -> tab_1
tab_1

I get the follwoing error

Error: `x` and `labels` must be same type

I'm sure the error was different in the safe haven, something like

Error: "x" must be either numeric or

something else that I can't remember (sorry)

Would you consider making the total_col argument of the summary_factorlist() function work for numeric variables? So far, this argument works for factors (and maybe character vars) only. Thanks in advance, Norbert

Hello,

Thanks for the great package!

Is there an option remove the "0" row or the "No" row via the finalfit function? I use a lot of binary variables, and it would be nice just to show the row for the "1" or "Yes".

For example, in the picture above, having "Female" "1" "12888 (41.3)" "972 (44.7)" "1.15 (1.05-1.25, p=0.002" "1.15 (1.05-1.25, p=0.002" all in the same row with no row with "0" or "-".

Hi! Thanks for your package. I 've been using it a lot and it significantly improves my workflow

I (accidentally) stumbled upon this small issue and thought I would I'd let you know

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

mtcars_df <- 
  mtcars %>% 
    dplyr::select(mpg:disp) %>% 
    sjlabelled::set_label(
      c("mileage", "cylinder","display")
    ) 

mtcars_tb <- 
  mtcars %>% 
    tibble::as_tibble() %>% 
    dplyr::select(mpg:disp) %>% 
    sjlabelled::set_label(
      c("mileage", "cylinder","display")
    ) 

dplyr::lst(mtcars_df, mtcars_tb) %>% purrr::map(~ class(.))
#> $mtcars_df
#> [1] "data.frame"
#> 
#> $mtcars_tb
#> [1] "tbl_df"     "tbl"        "data.frame"

mtcars_df %>% purrr::map(~ attributes(.))
#> $mpg
#> $mpg$label
#>       mpg 
#> "mileage" 
#> 
#> 
#> $cyl
#> $cyl$label
#>        cyl 
#> "cylinder" 
#> 
#> 
#> $disp
#> $disp$label
#>      disp 
#> "display"
mtcars_tb %>% purrr::map(~ attributes(.))
#> $mpg
#> $mpg$label
#>       mpg 
#> "mileage" 
#> 
#> 
#> $cyl
#> $cyl$label
#>        cyl 
#> "cylinder" 
#> 
#> 
#> $disp
#> $disp$label
#>      disp 
#> "display"

# REMOVE THE LABELS information and CHECK

mtcars_df %>% 
  finalfit::remove_labels() %>% 
  purrr::map(~ attributes(.))
#> $mpg
#> NULL
#> 
#> $cyl
#> NULL
#> 
#> $disp
#> NULL

mtcars_tb %>% 
  finalfit::remove_labels() %>% 
  purrr::map(~ attributes(.))
#> $mpg
#> $mpg$label
#>       mpg 
#> "mileage" 
#> 
#> 
#> $cyl
#> $cyl$label
#>        cyl 
#> "cylinder" 
#> 
#> 
#> $disp
#> $disp$label
#>      disp 
#> "display"

^{Created on 2019-10-23 by the reprex package (v0.3.0)}

In the documentation, the logistic regression with reduced model output is said to contain "a multivariable model contains a subset of the variables included specified in the full univariable set."

This use of language is odd, because univariable typically means that there is only one explanatory variable, whereas multivariable means that there are several explanatory variables (Tsai, 2013).

Would it be helpful to change this terminology or allow users to alter the default language?

Hi Ewen,

Firstly, thank you for a great package and tailoring your vignettes to a clinical research context. As a surgeon/scientist I have found them to be extremely useful.

I did however perform some crosschecking on some of my own data (no missing values) and found that the p-values on my stratified "table 1" formed by summary_factorlist() (for both ChiSq and Fishers) did not match the results given by the base R commands; chisq.test() and fisher.test().

What command does summary_factorlist() call behind the scenes to possibly explain this discordance?

Secondly, is there anyway to specify which factor variables to apply to the option catTest = catTestfisher (similar to the TableOne package)?

Kind regards,

Kai

ff_row_totals() does two things: it adds row totals and the number of missing values. But sometimes, you do not need to present missing values (or you do not have them).

It could be worth therefore to have instead two functions ff_row_totals_without_missing() and ff_row_totals_with_missing().

Best regards

Would you consider supporting nlme/lme models in the nlme library? (It appears that you already support nlmer and lmer models.)

It seems that variable colon_s is not included in the package.

Is it possible to see how colon_s looks like?

Thanks a lot.

Tien-Cheng.

Hi Ewen.

Is there a way to obtaining a demographic table without stratification?

Am I omitting something from the guide? Maybe ff_merge

Regards,

Is it possible to include variable, which name contains "forbidden" characters like white space and inside backticks? This code:

>   dt<-data.frame(
+     y=factor(c('yes','yes','yes','no','no','no')) ,
+     x1=c(1,3,5,2,4,6),
+     `second x`=c(1,2,4,6,5,3),
+     check.names=F
+   )
> 
> finalfit(dt,
+          dependent='y',
+          explanatory=c('x1', '`second x`'))

Produces this:

  Dependent: y                  no       yes          OR (univariable)      OR (multivariable)
3           x1 Mean (SD) 4.0 (2.0) 3.0 (2.0) 0.70 (0.20-1.83, p=0.485) 0.50 (NA-2.97, p=0.600)
2     second x Mean (SD) 4.7 (1.5) 2.3 (1.5)                         -                       -
1         <NA>      <NA>      <NA>      <NA> 0.30 (0.02-1.07, p=0.183) 0.22 (NA-1.08, p=0.347)

Looks like (at least) finalfit_internal_functions use tabs for alignment and that breaks viewing files on github.

One of the methods that I often use for generating models in R is to make a list of candidate models and then evaluate which is the best to select as the final model by one or more metrics. For that, I've written an AIC method that can compare lists of formula and put it into the PKNCA package. It's not a good fit for the underlying goal of the package, and I'd like to find a better home for the function and a few related functions (AIC.list, get.best.model, and get.first.model) as briefly described in billdenney/pknca#71.

Would you be interested in including functions (with tests) with a pull request that does the following?

• AIC.list: evaluate the AIC of a list of models.
  • AIC.logical: to handle NA in a list of models.
  • AIC.NULL: to handle NULL in a list of models.
• get.best.model: select the best model from a list of models by minimum AIC.
• get.first.model: select the first model that converged from a list of models.

Is it possible to create a table with more than 2 dependent variables in finalfit?

I as looking for something like this:

Thank you for the package :)

There is a package collection like yours here: https://github.com/leeper/tttable I thought you may be interested in.

Best wishes

This interferes with picking up interactions. Write in error.

Hi Ewan,

Is it possible to export the results of or_plot() as a .png?

I am working in the safe haven so not writing reports/thesis in there but exporting all plots/tables and embedding them in .pdf manually (I know!!).

Anyhow, when I try....(reprex won't let me change the plot width so a bit squashed here, but my actual plot looks ok)

  
library(finalfit)

explanatory <- c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent <- 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory)
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Warning: Removed 4 rows containing missing values (geom_errorbarh).

^{Created on 2019-03-05 by the reprex package (v0.2.1)}

...and then

library(ggplot2)
ggsave(filename = "or_plot_test.png", width = 9, height = 6.75, dpi = 600)

^{Created on 2019-03-05 by the reprex package (v0.2.1)}

....The resulting .png looks like.....

I've tried fiddling with widths and heights etc. Is there anything else I could do to get the plot to render correctly in the .png??

Edit - I just tried exporting to .pdf instead with the same results

Hi,
Using finalfit::coxphmulti(), is it possible to pass additional parameters to the underlying survival::coxph() function, in particular the "weights" argument? The use case would be running a survival model for a population that is already balanced between treatment vs. control groups using inverse probability of treatment weighting.

Thanks.

Hi,

Normally you would write this to change a continuous variable to have the median reported:

colon_s %>%
summary_factorlist(dependent, explanatory, p = TRUE, cont = "median") -> t

However, when writing code for a logistic regression there doesnt seem to be the ability to put cont = "median" into it.

Hello,

I noticed that the builds on Travis take quite some time as do the local checks. For instance:

checking examples (4m 9.9s)
   Examples with CPU (user + system) or elapsed time > 5s
                       user system elapsed
   ff_permute         37.63   0.14   54.54
   finalfit           22.25   0.09   26.56
   ff_remove_ref      10.01   0.04   11.97
   ff_plot             9.39   0.06   13.00
   ff_merge            7.47   0.05    8.33
   or_plot             7.43   0.08    8.89
   boot_predict        7.17   0.32    8.93
   dependent_label     7.02   0.14    9.23
   fit2df              6.19   0.11    6.50
   ff_stratify_helper  5.08   0.04    5.86
   hr_plot             4.58   0.06    5.24
   surv_plot           3.31   0.40    5.33

Is there a way to speed these up?

Secondly, I noticed that some code is quite repetitive and was wondering whether helpers could be written for these and perhaps this might reduce the execution time(especially for the multiple apply calls).

Thank you,

Nelson

Hi Ewan,

Once again, great package which continues to prove itself as being extremely useful.

I would like to produce an HR plot using the pooled coxph results from mice::pool() via the coxfit option in hr_plot.

I have been able to the same in a separate OR plot via glmfit = fits_pool as per your example, but I cannot find a way to make the same work for the HR plot.

It gives the error; "object 'HR' not found" - it seems possibly to do with coxfit calling on fit2df looking for an estimate name 'HR'??

survcomp::forestplot.surv uses a set of user-specified vectors as its input, which would work, but the format is not as nice and I would rather keep it all in finalfit if possible.

Any suggestions would be appreciated.

Cheers,

Kai

Hi,
I am using your package
I have a problema running
explanatory = c("x")
dependent = 'mort_5yr'
colon_s %>%
summary_factorlist(dependent, explanatory,
p=TRUE, add_dependent_label=TRUE)
if I use a numeric variable "x" with less than 6 values, the variable is considered as factor, and the test is a Chi-square, not a t-test

can you help me, please?

thank you very much in advance for your attention, and for your great package!

Just a comment regarding function such as ff_column_totals() or ff_row_totals():

To facilitate the use of these functions, it could be relevant that summary_factorlist() adds to the output some attributes (using attr()) to store dependent and explanory variables as well as the name of the dataset. Therefore, functions as ff_row_totals() could, as default values, directly read these attributes.

Is it currently possible/are there any plans to make it possible to include more than one random effect in mixed model outputs?

I notice that some model estimates are calculated correctly but are not necessarily printed in the correct order whenever a unicode-expression is employed in one of the factor levels of a variable. This may not be an issue with your excellent package but may be worth taking note.

Here is a simple example:
colon_s <- colon_s %>% mutate(agege65= factor(if_else(age>=65,"\u2265 65 years","< 65 years")),
nodesge3 = factor(if_else(nodes>=3,"\u2265 3","< 3"))
)

explanatory = c("agege65","nodesge3","adhere.factor")
dependent = "Surv(time, status)"

colon_s %>%
finalfit(dependent, explanatory) -> t6
knitr::kable(t6, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))

It appears that all estimates corresponding to variables with unicode expression are automatically placed at the bottom of the table. Do you know why this is happening? Any fix for it? If I remove the unicode expression, I am able to get the output in the correct place (but I do not get to keep the desired greater or equal mathematical notation).

I've noticed that in recent versions of finalfit (at least 1.0.1), quantitative variables (eg AGE) don't appear in hr_plot when I set the remove_ref option to TRUE, but do appear when remove_ref is set to FALSE.

Did I miss something ?

Sincerly yours

Romuald Riem

I've been using finalfit to create a summary stats table (i.e., table 1) and it's great. I've been looking through the very helpful online vignettes for an option to report summary stats for dummies / logicals with a percent rather than n, however, and haven't been able to find anything. For example, rather than reporting both male and female levels, just one row with percent female would suffice and, in many cases, be more easily interpreted by the reader. Did I miss something or perhaps this could be added in the future?

When computing p-value with summary_factorlist() and using ff_percent_only(), p-values are transformed into NA

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

        label      levels          No         Yes     p
1 Age (years)   Mean (SD) 59.8 (11.9) 58.4 (13.3) 0.578
2         Age   <40 years         7.5         7.4  <NA>
3             40-59 years        37.0        37.0  <NA>
4               60+ years        55.4        55.6  <NA>
5         Sex      Female        47.9        48.1  <NA>
6                    Male        52.1        51.9  <NA>
7 Obstruction          No        81.2        63.0  <NA>
8                     Yes        18.8        37.0  <NA>

The problem is maybe that you remove the number of observations after the computation of summary_factorlist() and it could be difficult to consider all the different use scenarios.

Maybe, it could be easier to consider instead an option directly within summary_factorlist() indicating how n and % should be presented? For allowing an high flexibility, you could use the syntax of str_glue(). For example an argument cat_format with default value equal to "{n} ({percent})". It would be very easy for users to adapt the output.

It is possible to write 👍

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

but it is not possible to write::

colon_s %>%
    or_plot(dependent, explanatory, confint_level = 0.90) 

because L_95 is harcoded into the function.

Thanks.

Hi Ewen.
I am a physician and want to state your Amazing job with Finalfit.
I was wondering. Is it easy to include conditional bold formatting for significative P-values for HTML or word output?
When you try to do this through kableextra-conditional formatting it is not possible because the table results from finalfit are dataframes without some column names.
Or do you know an easy way to do this ?

Regards,

Great Job !

Just an additional feedback from the teaching session I just had recently: for a binary dependant variable, we do not need to have percentage for both 'yes' and 'no' level. It could be useful to have an option in summary_factorlist() to not display the columns of the reference level of the dependent variable.

Best regards

It would be nice to have NAs in column containing parameter values when condense=F. Now we have "-" there which forces this column to be of character type instead of (desired) numeric.

Dear developers,

I went through the code but could not find an option for this.

It would be nice to have hypothesis test names of corresponding p-values as a footnote when using summary_factorlist.

Best wishes.

Hello,

I think, it would be nice to have option to include quartiles along with median (instead of IQR).

IQR tells little about location of "middle half" of data, whereas quartiles do.

I mean something like Median (Q1-Q3) e.g. 20.7 (14.5-22.5).

What do you think?

Regards, and thanks for great work,
Lukasz

After upgrading to the development version of dplyr , finalfit throws an error.

> b %>%
+   finalfit(dependent, explanatory, metrics=TRUE) -> t_log
Error: Predicate functions must return a single `TRUE` or `FALSE`, not a logical vector of length 0

The code was working perfectly just before I upgraded. I also updated to the development version of finalfit from github within the last hour or so (seeing a mention of dplyr-problems).

> rlang::last_trace()
<error/rlang_error>
Predicate functions must return a single `TRUE` or `FALSE`, not a logical vector of length 0
Backtrace:
     █
  1. └─b %>% finalfit(dependent, explanatory, metrics = TRUE)
  2.   ├─base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
  3.   └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
  4.     └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
  5.       └─`_fseq`(`_lhs`)
  6.         └─magrittr::freduce(value, `_function_list`)
  7.           ├─base::withVisible(function_list[[k]](value))
  8.           └─function_list[[k]](value)
  9.             └─finalfit::finalfit(., dependent, explanatory, metrics = TRUE)
 10.               ├─base::do.call(finalfit.glm, args)
 11.               └─(function (.data, dependent, explanatory, explanatory_multi = NULL, ...
 12.                 └─finalfit::summary_factorlist(...)
 13.                   └─`%>%`(...)
 14.                     ├─base::withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
 15.                     └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
 16.                       └─base::eval(quote(`_fseq`(`_lhs`)), env, env)
 17.                         └─finalfit:::`_fseq`(`_lhs`)
 18.                           └─magrittr::freduce(value, `_function_list`)
 19.                             └─function_list[[i]](value)
 20.                               └─purrr::keep(., ~.x < cont_cut)
 21.                                 └─purrr:::probe(.x, .p, ...)
 22.                                   └─purrr::map_lgl(.x, .p, ...)
 23.                                     └─purrr:::.f(.x[[i]], ...)

When making a table using a summary_factorlist(), I passed a large list of explanatory variables, and the function threw and error because one of the variables had zero events in all arms. Is there a way to still include this variable in the table? Or at the very least throw an informative error message so it is clear which variable is causing the problem?

When total_col = TRUE in summary_factorlist(), continuous variables are reported as NA rather than the mean (SD).

Would it be difficult to incorporating weighting into this package? This would be incredibly helpful for both administrative datasets as well as propensity score matching reporting. Thanks!

In summary_factorlist(), if missing data rows are reported for categorical data, the P-value comparing 2 groups treats this as an additional level. Is it possible to report the missing data, without changing the P-values?

Hello,

thank you very much for your package. Do you have any plan for allowing the use of interactions in models?

For finalfit, there are displayed even if variable names are not formatted. For or_plot, they simply disappear on the plot.

finalfit(colon_s, "mort_5yr", "age.factor * sex.factor")
or_plot(colon_s, "mort_5yr", "age.factor * sex.factor")

Hi,
I'd like to thank you for your finalfit package, I had started by myself for Cox and GLM, but your routine is so nicely done with the graphic representation that I couldn't compete and I adopted it!

I got an issue with the followings missing_compare() and finalfit(), while having no problems with missing_plot():

test

When I use the finalfit(dependent,explanatory) for every variable I got no problem, for instance:
test%>%finalfit("surv_d90","ICU")

I can create a loop that goes through all the explanatory variables, without fatal error, but when I use the finalfit() or missing_compare() code for all explanatory variables:

Error: Tibble columns must have consistent lengths, only values of length one are recycled:

• Length 6: Column p
• Length 8: Columns explanatory, estimate, confint[, 1], confint[, 2]
  Run rlang::last_error() to see where the error occurred.
  In addition: There were 26 warnings (use warnings() to see them)

I've tried to implement the data.frame as Tibble was involved, without succes either
test%>% data.frame%>% finalfit("surv_d90",explanatory)

Thank you for your help, and sorry if I missed some important point!

benoit

Is it possible to have an option to report standardized differences instead of P-values? I am thinking about propensity score matching situations.

Amazing package!
In some areas, it is getting more common to present confidence intervals without P-values.
Would be great to have an argument P which will omit the p-values if it is set to FALSE.

Currently, I use the following workaround:

fit.pois = df %>%
glmmulti("count", c("a","b"), family = poisson, offset=(log(df$pop))) %>%
fit2df(estimate_name = "Incidence risk ratio", digits =c(2,1,0))
fit.pois$Incidence risk ratio <- gsub(", p[=<]1", "", fit.pois$Incidence risk ratio)

All the best
Jan

P.S: another workaround would be to set confint_level = 0.95000001