The Cohorts page serves as your base for creating and understanding your specific patient groups or cohorts, a key step towards deriving in-depth insights from your data.

In clinical research, comprehending the demographics, characteristics, and various attributes of your patients is vital. Our Cohorts page enables this understanding, providing you with valuable insights about your study population.

Here are some questions that you can answer by defining a cohort:

A cohort is a subset of patients who share certain characteristics or have had certain experiences. To create a cohort, you'll utilize two types of filters: patient property filters, focusing on inherent patient attributes, and reported event filters, focusing on patient findings, interventions, and events during the study.

Patient property filters allow you to set criteria based on inherent patient attributes such as Arm, Sex, and Status. To add a patient property filter, click "+ Patient Property Filter", select the relevant property, and define the condition it should satisfy.

For instance, here's how to set a filter for patients from the active arm:

Add as many filters as needed. When using more than one, you can decide how they should interact: select AND if patients must satisfy all conditions, or OR if they must fulfill at least one condition.

In this example, we're setting a filter for patients who are in the active arm AND are 40 years old or older:

Reported event filters help you focus on patient findings during the study, such as the number of Exposures or Adverse Events. To add a reported event filter, click "+ Reported Event Filter" and select the domain and value for your filter.

Add filters for reported events themselves by clicking on the "Filter" button, located next to the trash can icon. In this example we filtered for serious adverse events:

Just like with patient property filters, you can add multiple reported event filters and specify how they should be combined.

This example represents a cohort of patients from the active treatment arm, aged at least 40, who have experienced at least one serious adverse event, and were exposed at least once to the drug.

After creating your cohort, you can visualize it in two ways: as a tree for a hierarchical perspective, or as a list for a simpler, linear view.

The tree shows how patients distribute within the cohort based on key properties like status and arm. Please note that the tree's structure will depend on your specific data, particularly factors like the number of arms.

Here's a tree derived from the cohort from the "Reported Event Properties" section. Out of 17 patients who completed the study, 11 experienced at least one serious adverse event.

Clicking a node in the tree opens a drawer with detailed information about the patients in that node.

The table presents a comprehensive list of all the patients included in the cohort. By clicking on the Patient ID of a patient, a new page opens with the profile of the patient.

Defining and understanding cohorts is crucial to derive insights from your data. Cohorts can be used across our platform, including dashboard functionalities like graphs and pivot tables. By predefining cohorts, you can ensure your visualizations and analyses are relevant and focused, leading to more precise interpretations.