This tutorial will walk through how to leverage the tidyCDISC app’s Table Generator tab. Let’s walk through creating simple summary statistics, among other things. If you haven’t already, import your studies data. For this tutorial, we’ll be using the five CDISC Pilot data files: An ADSL, ADVS, ADLBC, ADAE, and the ADTTE.

## Building an example table from scratch

The Table Generator tab is divided in two: the area to the left is the drag-and-drop interface used to build tables, and on the right is the real-time table output.

Any uploaded data sets will appear on the left-hand side as “bins”. “Bins” are just containers, showcasing the usable contents from a data source, when expanded. The ADSL & ADAE bins include the names of all their variables, while any BDS data sets, like the ADVS & ADLBC bins include a list of all the PARAMCD values. TTE class data sources are currently not supported in the table generator.

On the right-hand side, there are a list of “Stats”, such as ANOVA, CHG, MEAN, and FREQ to name a few. We call these “STAT Blocks”. And finally, in the middle of it all is the “drop zone”.

In order to build a table, we need only drag a variable block to the “Variable” drop zone and match it up with an applicable stat block by dragging it in the “Stats” drop zone, demonstrated in the following example.

### Age Summary Statistics

Below, we drag the AGE block from the ADSL and drag the MEAN block from the list of Stats to calculate summary statistics on patient AGE within the trial.

### PARAMCD Summary Statistics

Similarly, we can drag in DIABP from the ADVS and use the MEAN block to calculate summary statistics on parameters. However, because DIABP came from a BDS class data source, we need to select the desired AVISIT to calculate summary statistics for the desired time point.

### Add FREQ stat to categorical variable block, re-shuffle, and delete output

Now let’s assume we forgot to add some frequency statistics for our studies age groups. In fact, we really prefer it over the quantitative statistics previously calculated on the AGE variable. It’s no problem! We simply go back to our ADSL bin, select & move the AGEGRP1 variable to the “drop zone” and pair it with a FREQ stat block. Recently added blocks are always added to the bottom of the list, but that’s no worry, once added, you can shuffle the blocks around to present the output in the most logical manner. To remove an undesired output, just click the X on the right-hand side of each variable & corresponding stat block.

### Group by another Variable

We can use the ‘Group By’ drop-down list to calculate our table’s statistics across levels of a categorical variable. For example, When we select TRT01P, the a single column of statistics is broken down by all the planned treatment groups.

Here is the resulting table:

Now that we’ve created a simple table, it’s time to name and download it for saving/ sharing. Below, we insert our table title and download as CSV. Currently, the app supports RTF, CSV, and HTML output formats, but PDF is coming soon!

That’s it for our example workflow when building tables from scratch. However, the table generator offers many more features, including filtering, other statistical blocks, recipes for standard analysis (STAN) tables, more export file types for output, exported R code to reproduce the table, and in-app guided help. Those topics are expanded on below!

## Other Stat blocks

Please use this resource as a guide when determining how to summarize your data. It’s important to note that new statistical blocks are in development, so if they are not listed below, you can hover over any of the stat blocks in the app and text will appear for a “quick reference” description. The stat blocks we showcased in the demo above include the following:

• MEAN: Eight summary statistics are displayed. These include n, mean, standard deviation, median, Q1, Q3, Min, and Max.

• FREQ: Counts and percentages are displaced for all variable values that exist. Note this does not count subjects like the frequencies described below. If you’d like to count subjects, see NESTED FREQ DSC OR NESTED FREQ ABC.

The other statistical blocks not showcased in the demo are:

• ANOVA: this block can be used in conjunction with the grouping variable drop down selection to determine if means differ across groups.

• CHG: like the mean block, this also calculates basic summary statistics but uses the change from baseline value rather than AVAL

• Y FREQ: Only applies to flag variables (ending in ‘FL’, like DTHFL) that contain ‘Y’ values. The output will include a subject count and percentage for only the ‘Y’ values.

• MAX FREQ: This statistic only applies to variables (VAR) that have a “N” (VARN) counterpart. It’s useful for variables like AESEV when subjects should only be counted one time at their max VARN value. So, when paired with AESEV or AESEVN, the frequencies will reflect maximum severity of an adverse event during the trial.

• NON MISSING: A handy statistic that counts subjects who have non-missing values for a given variable. As such, it can help provide a high level measure of how many subjects had some value, as seen below with AEDECOD

• NESTED FREQ DSC: Notice when using this stat block, you’ll need to choose a variable to “nest” inside the values / levels of another. Without the nested variable selected (ie NONE is selected), the subject counts and percentages are displayed in descending order. Place the high level variable (like AEBODSYS) on the left hand side and select the more granular variable to be “nested” inside of it, like AEDECOD. Then the output will show all the AEDECOD values that exist within each AEBODSYS, all sorted descending.

• NESTED FREQ ABC: provides the same functionality as NESTED FREQ DSC, but organizes the variables and it’s values in alphabetical order!

## Filtering data in the table generator

There is an entire guide called “04 Filtering” dedicated to filtering in the tidyCDISC application. Please reference that article as the ultimate filtering resource, but just note that when filtering occurs on the Table Generator tab, those filters permeate all data sets on the tab and will be reflected in the table output as seen below. Also note that some mandatory filters will be applied automatically when ‘standard analysis’ tables are selected so be aware of the filtering caption displayed beneath the table title (red brackets added for attention). Ultimately, it is the users responsibility to make sure the correct data filters are applied for their table output!

## Standard Analysis Tables

There are a set of tables / listings that users need to build frequently, due to their common inclusion in submission filings to regulatory authorities. As such, the Table Generator (at the time this guide was authored) contains the following ‘recipes’ for commonly used tables:

When data needed to produce certain outputs aren’t present, the list of available tables in this drop down will change. For example, if an ADAE is not uploaded, then all the AE tables will disappear from the above list.

When one of these tables is selected, the table generator simply compiles the needed variable and stat blocks in the correct order to generate the desired output. This can be particularly useful for tables like #41 “Blood Chemistry actual values by visit” since it would be cumbersome to manually drag in a MEAN block for each week and each blood chemistry parameter, as there can be many. See snapshot of this automation below:

## Export output file types

As displayed in an earlier demo, you can export table output to an RTF, CSV, or HTML file. In a future release of tidyCDISC, you’ll be able to prepare a PDF version of your output. In the meantime, you can create a PDF using a temporary work-around: first export as HTML, then download page as a PDF.

## Export R code to reproduce table outside of app

As previously mentioned, one can reproduce any table created inside the app in an R script by downloading code using this button:

Upon doing so, the R script will download to your browser and when opened in R Studio, should look something like the below image. The R script will help you install any packages needed for the analysis, including the tidyCDISC package. If you used the CDISC Pilot data, no code modification is needed to reproduce the output since that data is pre-loaded into the tidyCDISC package. However, if you uploaded your data from a different source, you’ll need to enter the file path to your study directory’s data as a string, as seen on line 17. Note: 'path/to/study/directory/' is just a placeholder until you enter your own string. If you are unsure of how to format your string, just run choose.dir() in your R console if running on Windows.

After reading in the data, the R script will recall the variable and stat blocks previously dropped into the “drop zone” to recreate the output using the exact same process as performed in the app. From there, an R data.frame and html rendering of your table is prepared for saving or further analysis.

## Need help?

Throughout all the tabs in the application, there are these little buttons (usually in the top right-hand corners) with question marks on them:

When clicked, they launch a real-time guide that walks the user through the major components of the currently visible screen/ tab, providing context and suggested workflow. However, if the in-app guide doesn’t address your needs, consult the online documentation (that you’re reading now) for the most thorough user guide. If all else fails, send the developers a message with your question or request!