Introduction
In the realm of observational research, where data heterogeneity and
complexity are common, assessing and diagnosing the characteristics of
cohorts is fundamental to ensuring the reliability and credibility of
research findings. This is also an essential step in phenotype
development. The OHDSI community has developed an R package,
CohortDiagnostics, which provides researchers with a systematic approach
to examine various facets of cohorts, enabling them to identify
potential biases, assess data completeness, and validate the suitability
of cohorts for analysis. This tool is crucial for researchers working
within the Observational Health Data Sciences and Informatics (OHDSI)
ecosystem, enabling them to ensure the accuracy and reliability of
cohort definitions through a detailed examination of incidence rates,
cohort characteristics, and the specific codes triggering cohort
inclusion criteria. CohortDiagnostics streamlines the process of cohort
evaluation by:
- Generating a broad spectrum of diagnostics against a CDM database -
see more details here: Features
and Functionalities
- Providing an interactive R Shiny application within the package for
an intuitive exploration and visualization of these diagnostics. For
more information on R Shiny, see here.
Features and Functionalities
CohortDiagnostics offers a suite of features designed to deepen the
understanding of cohort dynamics and the intricacies of cohort
definitions, including:
-
Cohort Definition: Facilitates the examination and
validation of the logic behind cohort definitions, ensuring they
accurately capture the intended population.
-
Concepts in Data Source: Identifies the specific
concepts present within the data source that are relevant to the cohort
definitions, enabling a deeper understanding of data coverage and
content.
-
Orphan Concepts: Highlights concepts that, despite
their relevance, are not captured within a cohort’s definition. This
helps in refining concept sets and cohort criteria to ensure
comprehensiveness and relevance.
-
Cohort Counts: Provides counts of individuals and
records within cohorts, offering a basic measure of cohort size and
scope.
-
Incidence Rate: Calculates the incidence rate of
cohorts, stratified by various demographic and temporal factors such as
age, sex, and calendar year, to assess the frequency of patients/records
in the cohort and potential patterns over these strata.
-
Time Distributions: Examines the distribution of
time-related variables within cohorts, such as observation time before
and after cohort index date as well as cohort duration, offering
insights into cohort dynamics over time and available observation
time.
-
Index Event Breakdown: Breaks down the specific
events that qualify individuals for cohort inclusion, providing clarity
on how inclusion criteria are met.
-
Visit Context: Analyzes the healthcare context
(e.g., inpatient, outpatient) of the index events, offering insights
into where and how cohort members are identified within the healthcare
system.
-
Cohort Overlap: Assesses the degree of overlap
between cohorts, which can inform on potential biases, errors, or
redundancies in cohort construction, as well as shared characteristics
between cohorts of patients.
-
Cohort Characterization: Characterizes cohorts by
detailing prevalent conditions, medication use, procedures, and more, to
understand the clinical profile of cohort members over various time
periods relative to index.
-
Compare Cohort Characterization: Enables the direct
comparison of characteristics between cohorts, facilitating the
identification of unique or shared features across different cohorts and
across time points.
-
Meta Data: Provides meta-information about the data
and analyses conducted, ensuring transparency and reproducibility of the
cohort diagnostics process.
Together, these features equip researchers with the tools necessary
for a thorough examination of cohort definitions, enhancing the quality
and reliability of observational health research.
Utility and Application
CohortDiagnostics significantly contributes to the field of
observational health research by providing a robust framework for the
evaluation and validation of cohort definitions. Its utility spans
several critical areas:
-
Enhancing Cohort Definition Confidence: By offering
detailed diagnostics, CohortDiagnostics helps researchers refine their
cohort definitions, ensuring they accurately capture the intended
population. This is a critical step in phenotype development, which is a
cornerstone of modern observational health data research.
-
Identifying Data Quality Issues: Through the
identification of orphan concepts and the detailed breakdown of index
events, researchers can pinpoint data quality issues or gaps in cohort
definitions. Iterating over multiple potential cohort definitions after
analyzing these diagnostics is an encouraged and common practice.
-
Facilitating the Ideas Behind Comparative Analyses:
The package’s capabilities to characterize and compare cohorts, as well
as to analyze cohort overlaps, are invaluable for researchers looking to
understand the nuances and dynamics of their study populations. These
diagnostics can help inform comparative studies in the future, after the
cohorts and phenotypes are refined and finalized.
-
Supporting Transparent Research: By enabling the
listing of source codes, data source information, and providing a
platform for detailed diagnostics exploration, CohortDiagnostics fosters
a culture of transparency and reproducibility in observational
research.