
Define clinical data management as the process of collecting, validating, and ensuring regulatory-compliant, high-quality data from trial start to reporting for analysis.
Explore six clinical data types, including electronic health records, administrative data, claims, registries, health service data, and clinical trial data. Learn how these sources support medical research and data management.
Explore essential clinical data management activities in SAS projects, from data collection via CRFs and eCRFs to data entry, validation, and discrepancy management.
Explore the SAS programming process by mastering libraries and data sets, including work and permanent libraries and the libname statement. Practice data steps and key procs like print and contents.
Create permanent and temporary SAS libraries, import a clinical Excel dataset with proc import, and begin exploring patient demographic data in a SAS clinical data management session.
Filter rows in the CDM disease dataset to display only observations where disease equals multiple sclerosis, using subset archetypes and SAS clinical data management procedures.
Format columns in SAS by creating and applying formats, displaying data such as gender, and organizing results through formatting procedures, including grouping data and removing duplicate values.
Sort the disease data by variables in ascending or descending order, group by marital status or gender, and remove duplicates with proc sort, illustrating how to produce clean grouped results.
Apply conditional processing in SAS using if conditions and the end operator to filter observations in the disease dataset, combining average commute over 20 with female gender and hypertension.
Create an accumulating variable for number of laboratory procedures in the diabetes dataset using SAS clinical, applying the sum statement to obtain a cumulative total.
Learn how to convert variables in SAS by performing automatic and explicit data type conversions, using put and input functions, formats, and dataset management to handle numeric and character data.
Learn how to merge and concatenate SAS datasets using common variables, sorting by key identifiers, and compare merged versus concatenated results with practical demonstrations.
Learn to enhance SAS reports with titles, footnotes, and labels. Explore labeling concepts, the label statement, and persistent labeling in data management.
Use SAS proc freq to generate frequency reports and distribution tables for disease data, including frequency, percent, and cumulative frequency, with cross-tabulations by variables such as gender.
What is Clinical Data?
Clinical data is a staple resource for most health and medical research. Clinical data is either collected during the course of ongoing patient care or as part of a formal clinical trial program. Clinical data falls into six major types:
•Electronic health records.
•Administrative Data
•Claims Data
•Patient/Disease Registries
•Health Surveys
•Clinical trials Data
Electronic Health Record
The purest type of electronic clinical data which is obtained at the point of care at a
medical facility, hospital, clinic or practice.
Often referred to as the electronic medical record (EMR), the EMR is generally not available to outside researchers. The data collected includes administrative and demographic information, diagnosis, treatment, prescription drugs, laboratory tests, physiologic monitoring data, hospitalization, patient insurance, etc.
Administrative Data
These types of data are associated with electronic health records, which are primarily hospital discharge data reported to a government agency like AHRQ.
Claims Data
Claims data describe the billable interactions (insurance claims) between insured patients and the healthcare delivery system. Claims data falls into four general categories: inpatient, outpatient, pharmacy, and enrolment. The sources of claims data can be obtained from the government (e.g., Medicare) and/or commercial health firms (e.g., United HealthCare).
Patient / Disease Registries
Disease registries are clinical information systems that track a narrow range of key data for
certain chronic conditions such as Alzheimer's Disease, cancer, diabetes, heart disease,
and asthma. Registries often provide critical information for managing patient conditions.
Health Surveys
In order to provide an accurate evaluation of the population health, national surveys of the
most common chronic conditions are generally conducted to provide prevalence estimates.
National surveys are one of the few types of data collected specifically for research purposes,
thus making it more widely accessible.
Clinical Trials Registries and Databases
What is Clinical Data Management?
Clinical Data Management is the process of handling data from clinical trials. The inherent goal of any clinical data management system is to produce and maintain quality data. Clinical data management is a critical process in clinical research, which leads to the generation of high-quality, reliable, and statistically sound data from clinical trials. Clinical data management ensures the collection, integration, and availability of data at appropriate quality and cost.