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Pivot Tables

Combine, filter and export data in dynamic tables

Lauren Tucci avatar
Written by Lauren Tucci
Updated over 3 months ago

Overview

Pivot tables are a versatile tool that allows you to sift through and analyze your data with precision. They provide invaluable insights into patterns, trends, and relationships in your data. These tables allow you to dissect and filter data and compare different subsets of events.

Use Cases

Here are some examples of questions you can answer using pivot tables:

  • Clinical Operations

    1. Can I compare the efficacy outcomes between different treatment arms for specific patient populations?

    2. What are the trends in patient enrolment across different study sites and therapeutic areas?

  • Safety Monitoring:

    1. Can I analyse the distribution of adverse events by age group and gender?

    2. How does the safety profile vary based on the duration of drug exposure?

    3. Can I identify any correlations between specific lab results and treatment response?

  • Medical Monitoring:

    1. Is there a correlation between a specific biomarker and the treatment response?

    2. Can I assess the impact of my drug on various laboratory parameters?

Quick start

With just a few clicks, you can easily create a Pivot Table. Let's build a simple report together, to address the following question:

How does the investigational drug affect liver function and the distribution of serious adverse events in hypertensive patients as compared to the other treatment groups?

Step 1: Add an Event to Your Pivot Table

Start by clicking "Events +" in the panel definition menu. This allows you to add a new event for analysis. Each event will be represented by a new column in your table.

Initially, the table rows are segmented using the Patient ID. You can modify, add or delete these segments as required. For more on adding segments, refer to the section "Step 4: Adding a Segment to Your Pivot Table".

Step 2: Add a Cohort to Your Pivot Table

Cohorts define a subset of patients. For our example we only want to keep patients with hypertension. Click on Cohorts +, to add a cohorts to the panel. And select the cohort "Patient with Hypertension" from the list.

To learn more about cohort, you can refer to the section "Basic feature" or explore the "Cohort Page" article for a more in-depth understanding of how cohort creation.

Step 3: Add a Filter to Your Pivot Table

Adding filters to your data can aid in pinpointing specific subsets of data and highlighting trends or anomalies. To add a filter to your Panel, simply click on Filters +. In this case, we add a filter to keep only the serious adverse events.

Step 4: Add a Segment to Your Pivot Table

Click on Segments +, to create a new segment. By default, the table is initially divided based on the Patient ID of the patients. However, let's make a change and instead segment the rows using the treatment arm. Adding back the segment on the Patient ID will allow you to further explore the data and gain more detailed insights. This way, you can slice into the data and extract more specific information.

If your table spans multiple domains and you choose a property specific to one domain (other than the Patient), the columns that don't share this property will have their values grouped into a single row, in the category "undefined".

Export Your Pivot Table

Once your table is configured to your liking, you can export it as an Excel file. Open the file in your preferred spreadsheet software, and start editing and creating graphs based on your exported data.

With the flexibility of pivot tables and the integration of cohort definitions, you can delve deep into your data, revealing insights that would be otherwise hard to identify. Start exploring your data today with pivot tables!

Additional Features

Cohorts, Filters, and Segments can be added at two levels in pivot tables:

  1. At the Panel level, they apply to all selected events.

  2. At the Event level, they apply only to the specified event, leaving other events unaffected.

This feature provides you with a granular control over your specification, leading to greater flexibility in your data analysis.

Add A Cohort To an Event

Once you've created an event, click on the "⋮ More" button to add a cohort.

In the following example, the cohort "Exposure Count >= 1" applies only to the event "Adverse Event ► Events Count". The event "Laboratory ► ALT Average Result" remains unaffected.

Add a Filter to an Event

After creating an event, add a filter by clicking on the "⋮ More" button.

In the following example, the filter "Adverse Event ► Serious: Is Yes" applies exclusively to the event "Adverse Event ► Events Count". The event "Laboratory ► ALT Average Result" remains unaffected.

Add A Segment to an Event

Depending on where the segment is created, the behaviour will change. If applied at the panel level, the segment generates groups for the rows of the table. On the other hand, if the segment is added to an event, this results in the associated column being grouped into multiple sub-columns.

After creating an event, you can add a segment by clicking on the ⋮ More button.

In the example below, adding the segment Adverse Event ► Severity, will create a sub-column for each severity grade.

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