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Lab Data management for CDM

 3 Important points in efficient Lab Data Management for Clinical Data Managers

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One activity that always haunts Data Manages throughout the study is "Lab Data Management "
Lab Data Management is a critical activity in the study as clinically significant outliers in lab results can lead to Adverse events. Also in most cases lab data results also directly contributes to primary and secondary end points of the study
Lab Data Management also gets complicated with therapeutic areas (ex: oncology) & when your study has large number of sites (> 30) in multiple countries, then lab results will run in thousands.
So Here are the 3 important points for efficient lab data Management

1. Understanding the Type of lab data collected & type of lab used in the study

Once the protocol is finalized, Data Managers (DM) should thoroughly review the protocol to see what types of lab data is collected in the study.
Types of Lab data includes
a. Safety Data ( Ex: Hematology, Chemistry, Urinalysis) - this constitutes the over 60% of lab data in the study
b. Efficacy Data - determine whether an intervention produces the expected result under ideal circumstances.
c. Special Data including PK / PD data, Genomic data, Biomarker data
Next important step to determine is to see what type of labs are used the study. This will help in database setup.
a. Central Labs - Processes samples from multiple clinical sites at one central place

b. Local Labs - lab that’s closer to the clinical site

c. Specialty Labs -that process non-traditional tests like genetic testing, biomarkers etc

2. Correct EDC - lab module setup in study startup phase.
This is the most critical step as it can solve most of the lab reconciliation issues in the study. Ensuring what to be collected & what not to be collected on eCRF ( say we collect results on eCRF for local lab tests & not for central labs), clearly defining the analytes that are tested, normal ranges, units, their SI units and most importantly conversions ( working with Statistician, SAS & SDTM programmer becomes critical here - to avoid lab result related issues in future.
3. Clearly defining the DTA, FFA & Data Cleaning Plan
Data Transfer agreement (DTA) & File Format specification (FFA) - plays important role for central labs & Ensuring all the data aspects are clearly defined including format of incoming files, frequency of transfers, encryptions, cumulative / incremental transfers, contact details of senders, list of variables to be sent, type and length of each variable, data & time formats, etc are critical for effective lab data management.
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