Clinical Data Management (CDM) is a critical phase in any Veristat clinical trial design — whether it’s a new drug test or an oncology trial design. This is because the success of your clinical research relies on accurate data collection and analysis. With accurate data you will be able to generate high-quality and statistically correct results. With that in mind, how can you streamline clinical data management to drive success in your clinical trials?
This step-by-step guide will walk you through how to implement CDM in your clinical study effectively. The trial design you use doesn’t matter — whether it is oncology clinical trial design or any other — this post has got you covered. Let’s dive in.
Develop a Data Management Plan: Database Designing
Before starting the clinical research, draft a plan showing how you’ll safely collect and store data. Define who will do what to ensure a smooth operation from the trial’s beginning to the end. On top of that, design the database you’ll use in the study, which is usually a clinical software application that comes with built-in compliance and regulatory requirements, making it easy to use. When setting up the software, evaluate system specifications, regulatory and compliance, and user requirements to ensure data security. After that, define study details like objectives, patients, sites, intervals, and investigators inside the software. Next, design Case Report Forms (CRF) for data entry.
Create and Track Case Report Forms (CRF)
In the first step, you designed the CRF, here, you’ll create one. A CRF is simply a document for data collection. It records all the data responses from patients and protocol-required information about the subjects in the clinical trial. A CRF can be in the form of a paper or electronic version. Use the electronic version to reduce the chances of errors and for fast detection of discrepancies. This way, data collection will be fast, which will reduce the trial’s time. After gathering data, CRF tracking or monitoring is what follows to ensure completeness. This could mean checking to identify missing pages or illegible data to eliminate data gaps in your clinical trial.
Data Validation and Discrepancy Management
Before launching the trial, you drafted a protocol specification to guide the experiment and define what to expect. Now, data validation is an edit check that tests whether what you’ve gathered in your database aligns with the protocol specification. This way, you’ll shed light on discrepancies in the trial, which may result from missing and inconsistent data or deviations from the protocol. When you identify a discrepancy in your database, investigate the reason and resolve them with documentary proof, which will be helpful during trial audits.
Note: Discrepancy review and management is a critical step because it’ll determine the authenticity of the results from your clinical trial. Therefore, be thorough when handling them. One way to do that is by setting regular intervals for discrepancy reviews.
You launched your clinical trial with high hopes of success. So follow best CDM practices to ensure nothing stops you from introducing that drug under test to the market.