Displaying subject totals (“Big N’s”) at the top of a table is a verycommon and necessary part of building tables for clinical trials.{tfrmt} allows you to do this via thebig_n argument, whichallows you to use abig_n_structure object to specify whichvalues in the ARD should be added to the columns and how they should beformatted.
Overall Big Ns
To dynamically add big N’s to column labels, the information needs tobe included in the ARD. As a simple example, let’s take a smalldemographics table that just has Age and Sex for Placebo, Treatment andTotal. Below is our ARD prior to incorporating the big Ns:
data<-tibble(Group=rep(c("Age (y)","Sex","Age (y)","Sex"),c(3,3,6,12)), Label=rep(c("n","Mean (SD)","Male","Female"),c(6,6,6,6)), Column=rep(c("Placebo","Treatment","Total"), times=8), Param=rep(c("n","mean","sd","n","pct","n","pct"),c(6,3,3,3,3,3,3)), Value=c(15,13,28,14,13,27,73.56,74.231,71.84,9.347,7.234,8.293,8,7,15,8/14,7/13,15/27,6,6,12,6/14,6/13,12/27))%>%# Note because tfrmt only does rounding we will need to have the percents multiplied by 100mutate(Value=case_when(Param=="pct"~Value*100,TRUE~Value), ord1=if_else(Group=="Age (y)",1,2), ord2=if_else(Label=="n",1,2))data#># A tibble: 24 × 7#> Group Label Column Param Value ord1 ord2#><chr><chr><chr><chr><dbl><dbl><dbl>#> 1 Age (y) n Placebo n 15 1 1#> 2 Age (y) n Treatment n 13 1 1#> 3 Age (y) n Total n 28 1 1#> 4 Sex n Placebo n 14 2 1#> 5 Sex n Treatment n 13 2 1#> 6 Sex n Total n 27 2 1#> 7 Age (y) Mean (SD) Placebo mean 73.6 1 2#> 8 Age (y) Mean (SD) Treatment mean 74.2 1 2#> 9 Age (y) Mean (SD) Total mean 71.8 1 2#>10 Age (y) Mean (SD) Placebo sd 9.35 1 2#># ℹ 14 more rowsIn this study we have 30 subjects per treatment arm for a total of 60subjects. Because the ARD is 1 row per unique value, our Ns willcontribute 3 rows to the ARD. To make the N’s go to the right place, weneed to supply information about thecolumn column values.Thecolumn column values should map to those alreadyavailable in the ARD. Then we can combine this information with the dataabove to make the full ARD for this table.
big_ns<-tibble(Column=c("Placebo","Treatment","Total"), Param="bigN", Value=c(30,30,60))data<-bind_rows(data,big_ns)Now we can make ourtfrmt as normal, but this time weare going to add abig_n_structure tobig_n.Here we will specify that values with the parameter of"bigN" should be appended to the column labels and theyshould be formatted as"N = XX" and on a separate line.
tfrmt( group=Group, label=Label, column=Column, value=Value, param=Param, sorting_cols=c(ord1,ord2), body_plan=body_plan(frmt_structure(group_val=".default", label_val=".default",frmt_combine("{n} {pct}", n=frmt("X"), pct=frmt("(xx.x%)", missing=" "))),frmt_structure(group_val="Age (y)", label_val="Mean (SD)",frmt_combine("{mean} ({sd})", mean=frmt("XX.X"), sd=frmt("x.xx"))),frmt_structure(group_val=".default", label_val="n",frmt("xx"))), col_plan=col_plan(everything(),-starts_with("ord"),"Total"), row_grp_plan=row_grp_plan(row_grp_structure(group_val=".default",element_block(post_space=" "))), big_n=big_n_structure(param_val="bigN", n_frmt=frmt("\nN = xx")))%>%print_to_gt(data)| PlaceboN = 30 | TreatmentN = 30 | TotalN = 60 | |
|---|---|---|---|
| Age (y) | |||
| n | 15 | 13 | 28 |
| Mean (SD) | 73.6 (9.35) | 74.2 (7.23) | 71.8 (8.29) |
| Sex | |||
| n | 14 | 13 | 27 |
| Male | 8 (57.1%) | 7 (53.8%) | 15 (55.6%) |
| Female | 6 (42.9%) | 6 (46.2%) | 12 (44.4%) |
No matter how complicated the table, this feature will work as longas the ARD is set up appropriately with the correctcolumncolumn values on the big N rows. For tables with spanning columns(i.e. multiplecolumn columns), the big N will be appendedto the lowest levelcolumn column value that is supplied(based on the order of the column variables supplied to thetfrmt). Let’s look at this table, which has 3 layers ofcolumn spanning:
data<-tribble(~group,~label,~span2,~span1,~my_col,~parm,~val,"g1","rowlabel1","column cols","cols 1,2","col1" ,"value",1,"g1","rowlabel1","column cols","cols 1,2","col2" ,"value",1,"g1","rowlabel1",NA,NA,"mycol3","value",1,"g1","rowlabel1","column cols","col 4" ,"col4" ,"value",1,"g1","rowlabel1",NA,NA,"mycol5","value",1,"g1","rowlabel2","column cols","cols 1,2","col1" ,"value",2,"g1","rowlabel2","column cols","cols 1,2","col2" ,"value",2,"g1","rowlabel2",NA,NA ,"mycol3","value",2,"g1","rowlabel2","column cols","col 4" ,"col4" ,"value",2,"g1","rowlabel2",NA,NA,"mycol5","value",2,"g2","rowlabel3","column cols","cols 1,2","col1" ,"value",3,"g2","rowlabel3","column cols","cols 1,2","col2" ,"value",3,"g2","rowlabel3",NA,NA,"mycol3","value",3,"g2","rowlabel3","column cols","col 4" ,"col4" ,"value",3,"g2","rowlabel3",NA,NA,"mycol5","value",3,)spanning_tfrmt<-tfrmt( group=group, label=label, param=parm, value=val, column=c(span2,span1,my_col), body_plan=body_plan(frmt_structure(group_val=".default", label_val=".default",frmt("x"))), col_plan=col_plan(group,label,starts_with("col")))print_to_gt(spanning_tfrmt,data)column cols | |||||
|---|---|---|---|---|---|
cols 1,2 | col 4 | mycol3 | mycol5 | ||
| col1 | col2 | col4 | |||
| g1 | |||||
| rowlabel1 | 1 | 1 | 1 | 1 | 1 |
| rowlabel2 | 2 | 2 | 2 | 2 | 2 |
| g2 | |||||
| rowlabel3 | 3 | 3 | 3 | 3 | 3 |
If we just want to put the big N on the highest “column cols”, weneed to add a row to the ARD wherespan2 equals"column cols" and all othercolumn columns aremissing.
with_big_n_data<-tribble(~group,~label,~span2,~span1,~my_col,~parm,~val,NA,NA,"column cols",NA,NA ,"bigN",18,)%>%bind_rows(data)#Now we can add the big_n to the tfrmt from beforen_span_tfrmt<-spanning_tfrmt%>%tfrmt(big_n=big_n_structure(param_val="bigN", n_frmt=frmt("\nN = xx")))print_to_gt(n_span_tfrmt, .data=with_big_n_data)column colsN = 18 | |||||
|---|---|---|---|---|---|
cols 1,2 | col 4 | mycol3 | mycol5 | ||
| col1 | col2 | col4 | |||
| g1 | |||||
| rowlabel1 | 1 | 1 | 1 | 1 | 1 |
| rowlabel2 | 2 | 2 | 2 | 2 | 2 |
| g2 | |||||
| rowlabel3 | 3 | 3 | 3 | 3 | 3 |
If we also want big N’s oncol 1,2,col4,andmycol3, we will need Add 3 more rows to our ARD. Onethat goes down to thespan1 level of specificity and twothat go all the way down tomy_col level of specificity.(Note: because we are keeping theparm values the same wecan use the same tfrmt)
with_more_big_n_data<-tribble(~group,~label,~span2,~span1,~my_col,~parm,~val,NA,NA,"column cols","cols 1,2",NA ,"bigN",12,NA,NA,"column cols","col 4" ,"col4" ,"bigN",6,NA,NA,NA,NA,"mycol3","bigN",6)%>%bind_rows(with_big_n_data)print_to_gt(n_span_tfrmt, .data=with_more_big_n_data)column colsN = 18 | |||||
|---|---|---|---|---|---|
cols 1,2N = 12 | col 4 | mycol3N = 6 | mycol5 | ||
| col1 | col2 | col4N = 6 | |||
| g1 | |||||
| rowlabel1 | 1 | 1 | 1 | 1 | 1 |
| rowlabel2 | 2 | 2 | 2 | 2 | 2 |
| g2 | |||||
| rowlabel3 | 3 | 3 | 3 | 3 | 3 |
Page-Level Big Ns
Thepage_plan allows users to split up large tables inmany ways. Let’s suppose ourpage_plan splits our table sothat each subtable represents a different subpopulation defined by thegroup variable in the ARD.
In this case, our page_plan looks something like this:
page_structure(group_val=".default")Because our subtables contain different populations, we want the bigNs displayed in the headers to be table-specific. To achieve this, wecan supply multiple sets of big Ns in the ARD. We just need to specifythe specific values of thegroup parameter that each big Nbelongs to. This functionality can be enabled via theby_page argument inbig_n_structure.
For example, let’s take the following data, where we have calculatedbig Ns for each level of the grouping variablegrp. NOTE:the bigN rows should be in the same order as the rest of the data(i.e. A, B rather than B, A for thegrp variable).
dat<-crossing( grp=c("A","B"), lbl=c("a","b"), col=c("Placebo","Treatment"), param="mean")%>%mutate(val=c(1.254,3.483,5.123,4.239,4.364,8.435,7.645,2.312))big_ns<-tibble(col=c("Placebo","Placebo","Treatment","Treatment"), grp=c("A","B","A","B"), param="bigN", val=c(34,36,42,39))dat<-bind_rows(dat,big_ns)tail(dat)#># A tibble: 6 × 5#> grp lbl col param val#><chr><chr><chr><chr><dbl>#>1 B b Placebo mean 7.64#>2 B b Treatment mean 2.31#>3 ANA Placebo bigN 34#>4 BNA Placebo bigN 36#>5 ANA Treatment bigN 42#>6 BNA Treatment bigN 39Now that we have all big Ns represented in the ARD, we can make sureof theby_page argument in thebig_n_structureto ensure the various big Ns are assigned to the relevant pages definedby thepage_plan.
gts<-tfrmt( group=grp, label=lbl, column=col, param=param, value=val, body_plan=body_plan(frmt_structure(group_val=".default", label_val=".default",frmt("x.x"))), page_plan=page_plan(page_structure(group_val=".default")), big_n=big_n_structure(param_val="bigN", n_frmt=frmt("\nN = xx"), by_page=TRUE))%>%print_to_gt(dat)| PlaceboN = 34 | TreatmentN = 42 | |
|---|---|---|
| A | ||
| a | 1.3 | 3.5 |
| b | 5.1 | 4.2 |
| PlaceboN = 36 | TreatmentN = 39 | |
|---|---|---|
| B | ||
| a | 4.4 | 8.4 |
| b | 7.6 | 2.3 |
## Final noteAs a final note, column names for the big N's should match the ARD column names, rather than any future names. Regardless of any renaming that happens in the `col_plan`, the column name for the big N's should be consistent with column values in the ARD. The big N's will still be added if names do get updated in the `col_plan`. ``` rn_span_tfrmt %>% tfrmt(col_plan = col_plan( group, label, starts_with("col"), new_col_3 = mycol3, -mycol5 )) %>% print_to_gt(.data = with_more_big_n_data)column colsN = 18 | ||||
|---|---|---|---|---|
cols 1,2N = 12 | col 4 | new_col_3N = 6 | ||
| col1 | col2 | col4N = 6 | ||
| g1 | ||||
| rowlabel1 | 1 | 1 | 1 | 1 |
| rowlabel2 | 2 | 2 | 2 | 2 |
| g2 | ||||
| rowlabel3 | 3 | 3 | 3 | 3 |
big_n_structures provide a straightforward way of addingbig N’s from the ARD into the column labels. The functionality allowsfor big N’s at any level of column spanning depending on how it isparameterized in the ARD.