As covered in
vignette('metadata'),Tplyr can produce
metadata for any result that it calculates. But what about data that
Tplyr can’t produce, such as a efficacy results or some
sort of custom analysis? You may still want that drill down capability
either on your own or paired with an existing Tplyr
table.
Take for instance Table 14-3.01 from the CDISC Pilot. Skipping the actual construction of the table, here’s the output data from Tplyr and some manual calculation:
kable(full_data)| row_id | row_label1 | row_label2 | var1_Placebo | var1_Xanomeline Low Dose | var1_Xanomeline High Dose |
|---|---|---|---|---|---|
| d1_1 | Baseline | n | 79 | 81 | 74 |
| d2_1 | Mean (SD) | 24.1 (12.19) | 24.4 (12.92) | 21.3 (11.74) | |
| d3_1 | Median (Range) | 21.0 ( 5;61) | 21.0 ( 5;57) | 18.0 ( 3;57) | |
| d1_2 | Week 24 | n | 79 | 81 | 74 |
| d2_2 | Mean (SD) | 26.7 (13.79) | 26.4 (13.18) | 22.8 (12.48) | |
| d3_2 | Median (Range) | 24.0 ( 5;62) | 25.0 ( 6;62) | 20.0 ( 3;62) | |
| d1_3 | Change from Baseline | n | 79 | 81 | 74 |
| d2_3 | Mean (SD) | 2.5 ( 5.80) | 2.0 ( 5.55) | 1.5 ( 4.26) | |
| d3_3 | Median (Range) | 2.0 (-11;16) | 2.0 (-11;17) | 1.0 ( -7;13) | |
| x4_1 | p-value(Dose Response) [1][2] | 0.245 | |||
| x4_2 | |||||
| x4_3 | p-value(Xan - Placebo) [1][3] | 0.569 | 0.233 | ||
| x4_4 | Diff of LS Means (SE) | -0.5 (0.82) | -1.0 (0.84) | ||
| x4_5 | 95% CI | (-2.1;1.1) | (-2.7;0.7) | ||
| x4_6 | |||||
| x4_7 | p-value(Xan High - Xan Low) [1][3] | 0.520 | |||
| x4_8 | Diff of LS Means (SE) | -0.5 (0.84) | |||
| x4_9 | 95% CI | (-2.2;1.1) |
This is the primary efficacy table from the trial. The top portion of
this table is fairly straightforward with Tplyr and can
be done using descriptive statistic layers. Once you hit the p-values on
the lower house, this becomes beyond Tplyr’s remit. To produce the
table, you can combine Tplyr output with a separate
data frame analyzed and formatted yourself (but note you can still use
some help from Tplyr tools like
apply_formats()).
But what about the metadata? How do you get the drill down capabilities for that lower half of the table? We’ve provided a couple additional tools in Tplyr to allow you to construct your own metadata and append existing metadata present in a Tplyr table.
Build a tplyr_meta object
As covered in vignette('metadata'), a
tplyr_meta object consists of two different fields: A list
of variable names, and a list of filter conditions. You provide both of
these fields as a list of quosures:
m <- tplyr_meta(
names = quos(a, b, c),
filters = quos(a==1, b==2, c==3)
)
m
#> tplyr_meta: 3 names, 3 filters
#> Names:
#> a, b, c
#> Filters:
#> a == 1, b == 2, c == 3The tplyr_meta() function can take these fields
immediately upon creation. If you need to dynamically create a
tplyr_meta object such as how Tplyr
constructs the objects internally), the functions
add_variables() and add_filters() are
available to extend an existing tplyr_meta object:
m <- m %>%
add_variables(quos(x)) %>%
add_filters(quos(x == 'a'))
m
#> tplyr_meta: 4 names, 4 filters
#> Names:
#> a, b, c, x
#> Filters:
#> a == 1, b == 2, c == 3, x == "a"Building your own metadata table
Now that we can create our own tplyr_meta objects, let’s
assemble the metadata for the bottom portion of Table 14-3.01:
# Overall model subset of data
meta <- tplyr_meta(
names = quos(TRTP, EFFFL, ITTFL, ANL01FL, SITEGR1, AVISIT, AVISITN, PARAMCD, AVAL, BASE, CHG),
filters = quos(EFFFL == "Y", ITTFL == "Y", PARAMCD == "ACTOT", ANL01FL == "Y", AVISITN == 24)
)
# Xan High / Placebo contrast
meta_xhp <- meta %>%
add_filters(quos(TRTP %in% c("Xanomeline High Dose", "Placebo")))
# Xan Low / Placbo Contrast
meta_xlp <- meta %>%
add_filters(quos(TRTP %in% c("Xanomeline Low Dose", "Placebo")))
# Xan High / Xan Low Contrast
meta_xlh <- meta %>%
add_filters(quos(TRTP %in% c("Xanomeline High Dose", "Xanomeline Low Dose")))
eff_meta <- tibble::tribble(
~"row_id", ~"row_label1", ~"var1_Xanomeline Low Dose", ~"var1_Xanomeline High Dose",
"x4_1", "p-value(Dose Response) [1][2]", NULL, meta,
"x4_3", "p-value(Xan - Placebo) [1][3]", meta_xlp, meta_xhp,
"x4_4", " Diff of LS Means (SE)", meta_xlp, meta_xhp,
"x4_5", " 95% CI", meta_xlp, meta_xhp,
"x4_7", "p-value(Xan High - Xan Low) [1][3]", NULL, meta_xlh,
"x4_8", " Diff of LS Means (SE)", NULL, meta_xlh,
"x4_9", " 95% CI", NULL, meta_xlh
)Let’s break down what happened here:
- First, we assemble the the overarching metadata object for the
model. A lot of this metadata is shared across each of the different
result cells for all of the efficacy data, so we can start by collecting
this information into a
tplyr_metaobject. - Next, we can use that starting point to build
tplyr_metaobjects for the other result cells. The model data contains contrasts of each of the different treatment group comparisons. By usingadd_filters(), we can create those additional threetplyr_metaobjects using the starting point and attaching an additional filter condition. - Lastly, to extend the metadata in the original
tplyr_tableobject that created the summary portion of this table, we need a data frame. There’s a lot of ways to do this, but I like the display and explicitness oftibble::tribble().
When building a data frame for use with tplyr_table
metadata, there are really only two rules:
- You need a column in the data frame called
row_id - The
row_idvalues cannot be duplicates of any other value within the existing metadata.
The row_id values built by Tplyr will
always follow the format “row_id of “c2_3”. In this example, I chose “x4_n” as the
format for the “x” to symbolize custom, and these data can be thought of
as the fourth layer. That said, these values would typically be masked
by the viewer of the table so they really just need to be unique - so
you can choose whatever you want.
Anti-joins
If the custom metadata you’re constructing requires references to
data outside your target dataset, this is also possible with a
tplyr_meta object. If you’re looking for non-overlap with
the target dataset, you can use an anti-join. Anti-joins can be added to
a tplyr_meta object using the add_anti_join()
function.
meta %>%
add_anti_join(
join_meta = tplyr_meta(
names = quos(TRT01P, EFFFL, ITTFL, SITEGR1),
filters = quos(EFFFL == "Y", ITTFL == "Y")
),
on = quos(USUBJID)
)
#> tplyr_meta: 11 names, 5 filters
#> Names:
#> TRTP, EFFFL, ITTFL, ANL01FL, SITEGR1, AVISIT, AVISITN, PARAMCD, AVAL, BASE, CHG
#> Filters:
#> EFFFL == "Y", ITTFL == "Y", PARAMCD == "ACTOT", ANL01FL == "Y", AVISITN == 24
#> Anti-join:
#> Join Meta:
#> tplyr_meta: 4 names, 2 filters
#> Names:
#> TRT01P, EFFFL, ITTFL, SITEGR1
#> Filters:
#> EFFFL == "Y", ITTFL == "Y"
#> On:
#> USUBJIDAppending Existing Tplyr Metadata
Now that we’ve created our custom extension of the
Tplyr metadata, let’s extend the existing data frame.
To do this, Tplyr has the function
append_metadata():
t <- append_metadata(t, eff_meta)Behind the scenes, this function simply binds the new metadata with
the old in the proper section of the tplyr_table object.
You can view the the tplyr_table metadata with the function
get_metadata():
get_metadata(t)
#> # A tibble: 16 × 6
#> row_id row_label1 row_label2 var1_Placebo var1_Xanomeline High…¹
#> <chr> <chr> <chr> <list> <list>
#> 1 d1_1 "Baseline" n <tplyr_mt> <tplyr_mt>
#> 2 d2_1 "Baseline" Mean (SD) <tplyr_mt> <tplyr_mt>
#> 3 d3_1 "Baseline" Median (R… <tplyr_mt> <tplyr_mt>
#> 4 d1_2 "Week 24" n <tplyr_mt> <tplyr_mt>
#> 5 d2_2 "Week 24" Mean (SD) <tplyr_mt> <tplyr_mt>
#> 6 d3_2 "Week 24" Median (R… <tplyr_mt> <tplyr_mt>
#> 7 d1_3 "Change from Baseline" n <tplyr_mt> <tplyr_mt>
#> 8 d2_3 "Change from Baseline" Mean (SD) <tplyr_mt> <tplyr_mt>
#> 9 d3_3 "Change from Baseline" Median (R… <tplyr_mt> <tplyr_mt>
#> 10 x4_1 "p-value(Dose Response… NA <NULL> <tplyr_mt>
#> 11 x4_3 "p-value(Xan - Placebo… NA <NULL> <tplyr_mt>
#> 12 x4_4 " Diff of LS Means (… NA <NULL> <tplyr_mt>
#> 13 x4_5 " 95% CI" NA <NULL> <tplyr_mt>
#> 14 x4_7 "p-value(Xan High - Xa… NA <NULL> <tplyr_mt>
#> 15 x4_8 " Diff of LS Means (… NA <NULL> <tplyr_mt>
#> 16 x4_9 " 95% CI" NA <NULL> <tplyr_mt>
#> # ℹ abbreviated name: ¹`var1_Xanomeline High Dose`
#> # ℹ 1 more variable: `var1_Xanomeline Low Dose` <list>Finally, as with the automatically created metadata from Tplyr, we can query these result cells just the same:
get_meta_subset(t, 'x4_1', "var1_Xanomeline High Dose") %>%
head() %>%
kable()| USUBJID | TRTP | EFFFL | ITTFL | ANL01FL | SITEGR1 | AVISIT | AVISITN | PARAMCD | AVAL | BASE | CHG |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 01-701-1015 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 8 | 13 | -5 |
| 01-701-1023 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 12 | 13 | -1 |
| 01-701-1028 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 3 | 3 | 0 |
| 01-701-1033 | Xanomeline Low Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 7 | 7 | 0 |
| 01-701-1034 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 11 | 11 | 0 |
| 01-701-1047 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 19 | 10 | 9 |
Metadata Without Tplyr
You very well may have a scenario where you want to use these metadata functions outside of Tplyr in general. As such, there are S3 methods available to query metadata from a dataframe instead of a Tplyr table, and parameters to provide your own target data frame:
get_meta_subset(eff_meta, 'x4_1', "var1_Xanomeline High Dose", target=tplyr_adas) %>%
head() %>%
kable()| USUBJID | TRTP | EFFFL | ITTFL | ANL01FL | SITEGR1 | AVISIT | AVISITN | PARAMCD | AVAL | BASE | CHG |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 01-701-1015 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 8 | 13 | -5 |
| 01-701-1023 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 12 | 13 | -1 |
| 01-701-1028 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 3 | 3 | 0 |
| 01-701-1033 | Xanomeline Low Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 7 | 7 | 0 |
| 01-701-1034 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 11 | 11 | 0 |
| 01-701-1047 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 19 | 10 | 9 |
As with the Tplyr metadata, the only strict criteria
here is that your custom metadata have a row_id column.
Tying it Together
The vignette wouldn’t be complete without the final contextual example - so here we go. Ultimately these pieces an all fit together in the context of a Shiny application and give you the desired click-through experience.
Source code available here