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In recent years, domestic clinical trial registration has grown rapidly, with clinical data gaining prominence as a key translational outcome in research. Database construction and validation are crucial for ensuring data completeness and accuracy in clinical studies.
Driven by innovative drug development needs and the pursuit of time and cost efficiency, complex clinical trial designs are increasingly common. Examples include integrated early-phase SAD/MAD studies with Food Effect (FE), Drug-Drug Interaction (DDI), and Mass Balance (MB) components; oncology trials combining dose escalation and expansion; Decentralized Clinical Trials (DCT); and the merging of real-world and post-marketing data. These advanced designs demand robust database construction and integration, making efficient clinical database development a critical factor in improving research quality.
The clinical database architecture primarily relies on an EDC system, with core components being page design, logical validation, and access control (particularly blinding). Page layouts are structured around the Case Report Form (CRF), a critical trial element that ensures data completeness and study credibility. Effective CRF design aligns with trial objectives to collect research-relevant data, directly supporting clinical trial success.
CRF designs vary across clinical trials due to multiple factors: the absence of standardized templates adaptable to all study designs, conflicting priorities among team members or departments, and limitations of database platforms requiring system-specific adjustments. To optimize resource reuse, existing templates and standards should be prioritized where possible：
1. Variable Uniformity: Align variable names/coding with controlled terminology.
2. Option Consistency: Standardize option order (e.g., "Yes" before "No") and expressions (e.g., "Unknown/Not Done/Not Applicable").
3. Format Harmonization: Define formats for dates, decimals, fixed units.
4. Guideline Systematization: Optimize eCRF instructions via online tools for end-to-end consistency.
Standardized design improves data entry efficiency, quality, and reduces training and analysis complexity. Leverage global standards like CDISC/CDASH to streamline data collection and enhance interoperability.
CDASH Methodology for Data Acquisition System Design
CDASH establishes clinical data standards and CRF content guidelines for new drug trials, including common variable pools, with a recommended design process:
As can be seen from the above figure, CRF design is a collaborative and strictly controlled process requiring input from all clinical research stakeholders to review, refine, and approve the forms. The drafting phase begins only after finalizing the study protocol, with special attention to key requirements during this stage.
1. Data streamlining principle: Collect only essential variables for analysis to avoid redundancy. Excessive data increases costs/time while compromising quality, and raises ethical concerns (e.g., unnecessary PK sampling may impact subjects).
2. Analysis-driven principle: Align data collection with study protocol and statistical plans. Collect all required variables using statistically sound methods based on predefined analytical objectives.
3. Standardization: Adhere to CDASH/CDISC standards for data fields, minimizing free text requiring coding/extraction for analysis.
4. User-centered design: Implement intuitive field labeling and guidance to eliminate CRF ambiguities.
Leverage CDASH template libraries for CRF optimization
CDASH delivers prebuilt templates for core clinical data points, such as: Adverse Events(AE)、Comments(CO)、Prior and Concomitant Medications(CM)、Demographics(DM)、ECG Test Results(EG)、Exposure(EX)、Inclusion/Exclusion Criteria Not Met(IE)、Laboratory Test Results(LB)、Medical History(MH)、Physical Examination(PE)、Procedures(PR)、Subject Characteristics(SC)、Substance Use(SU)、Vital Signs(VS), etc. Here are two examples, though the study does not cover all listed fields.
1. The Adverse Events page lists routinely collected variables, such as: HR=Highly Recommended，R/C=Required/Conditional，O=Optional。
- AETERM-Adverse Event Name (HR)
- AESTDAT-Adverse event start date (HR)
- AESTTIM-Adverse event start time (R/C)
- AEENDAT-Adverse event end date (HR)
- AEENTIM-Adverse event end time (R/C)
- AESEV-Severity level (R/C)/AETOXGR-Toxicity level (R/C)
- AEREL-relationship to test drug (HR)
- AEACN-Actions taken with the test drug (R/C)
- AEOUT-Adverse event outcome (R/C)
- AEDIS-whether to withdraw from the trial because of this AE (O)
- AESER-Serious adverse event (R/C)
- AESDTH-Deadly (R/C)
- AESLIFE-Life-threatening (R/C)
- ASSHOSP-Hospitalization or prolonged hospitalization (R/C)
- AESDISAB-Causes permanent or significant disability/loss of function (R/C)
- AESCONG-Congenital anomaly or birth defect (R/C)
- AESMIE-Other medically significant event (R/C)
2. The Hypoglycemic Events page (Endocrine Program) lists potential variables to collect, such as:
（CDISC, Tools, Knowledge Base, eCRF Portal）
When designing eCRFs from CRFs, it is critical to balance EDC system characteristics: While high reusability of fields/forms accelerates database setup, tightly linked modules make later modifications error-prone and laborious (like "pulling one hair affects the whole body"); conversely, low reusability isolates changes to individual forms but increases initial build time and duplicates fields. Thus, optimizing reusability versus efficiency requires aligning with project-specific needs.
The EDC system's logical validation ensures data accuracy, reduces data cleaning time, and simplifies statistical analysis. Key considerations for eCRF design and validation setup include:
1. Use standardized field names in eCRFs to streamline logical verification and minimize errors.
2. Modular design: Decouple collectable data elements (e.g., separate date/time fields) to optimize system logic and maintenance workflows.
3. Implement logical verification by avoiding wildcards and precisely referencing fields with specific identifiers (e.g., visit, form, field, record number).
4. Multi-record log verification is often more complex and error-prone than direct field configuration; always check system capabilities (e.g., dynamic row support) beforehand.
Standardizing CRF design and EDC setup—including logical verification—streamlines research processes. While adaptable designs lack universal solutions, prioritizing standardization, practical application of knowledge, and continuous learning transforms theory into action. These foundational ‘root system’ principles simplify complexity, enabling extraction of valuable data from information overload to ultimately advance human benefit.
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