How can clinical trial data be improved?

Improving Clinical Trial Data: Best Practices and Recommendations

The most effective way to improve clinical trial data is through streamlined data collection that focuses on critical information while eliminating unnecessary data points, combined with standardized reporting formats and robust statistical methodologies. 1

Key Strategies for Improving Clinical Trial Data

Implementing Quality by Design Approach

• Apply a Quality by Design approach to prospectively examine trial objectives and define factors critical to meeting these objectives 1
• Focus on collecting only data that is essential to evaluating the safety and efficacy of the investigational treatment 1
• Eliminate collection of large amounts of non-critical data that burdens investigators and may compromise data quality 1

Standardizing Data Collection and Reporting

• Develop standardized data reporting formats aligned with regulatory requirements to enable better analysis of existing data 1
• Implement electronic data capture systems to enhance efficiency, limit redundancies, and minimize data entry errors 1
• Use consistent ontologies and vocabularies to facilitate data mining and integration across different trials 1

Optimizing Safety Data Collection

• Predefine in the protocol which adverse events are expected as part of the natural course of the disease to streamline safety reporting 1
• Specify which physical assessments are critical to determining the safety profile of the drug rather than collecting all possible measurements 1
• Balance the need for comprehensive safety data with the burden of excessive data collection 1

Improving Trial Design and Planning

• Design trials with well-defined and reliable outcome measures that prioritize morbidity, mortality, and quality of life 1
• Ensure baseline comparability between study groups and minimize bias through proper randomization and stratification 1
• Prespecify analysis plans for primary and secondary endpoints, including how to handle missing data 1

Advanced Approaches to Data Collection and Analysis

Leveraging Technology for Enhanced Data Quality

• Utilize electronic medical records to study the natural process of care and include larger, more representative patient populations 1
• Implement wireless technologies to improve access to electronic medical records, reducing error rates and enabling rapid data collection 1
• Apply centralized monitoring with electronic capabilities to considerably reduce errors, especially as the quantity of data collected increases 1

Improving Statistical Methodologies

• Develop fit-for-purpose statistical methodologies appropriate for the specific trial context 1
• Conduct sensitivity analyses to evaluate the robustness of conclusions across different analytical approaches 1
• Prespecify how missing data will be handled to maintain the integrity of the analysis 1

Data Linkage and Integration

• Link multiple data sources to address representativeness of samples and assess generalizability of results 1
• Standardize data reporting to enable analysis of dormant but valuable data currently existing in different, incompatible formats 1
• Create cloud-based collections of data that can be interrogated for multiple purposes beyond the original trial 1

Addressing Common Pitfalls in Clinical Trial Data

Managing Missing Data

• Recognize that all methods to analyze missing data make usually unverifiable assumptions about the nature of the missing data 1
• Look for robustness across a number of sensitivity analyses and conduct trials in ways that minimize loss to follow-up 1
• Consider using multiple analytical methods for handling missing data and reporting results of more than one method if they differ 2

Balancing Comprehensive Data Collection with Efficiency

• Recognize that collecting non-critical but easily obtained data may not be justified solely on the basis that it might identify a safety signal 1
• Consider the trade-off between potentially missing some extraneous data versus the efficiencies gained through streamlined collection 1
• Focus on reducing logistical, procedural, and operational issues that complicate studies and burden investigators 1

Addressing International Variations in Requirements

• Account for regional variations in regulatory requirements when designing global trials 1
• Incorporate prespecifications in the trial protocol to manage minor but important differences in regulations 1
• Standardize processes globally despite differences in safety data requirements across countries 1

By implementing these recommendations, clinical trials can achieve higher quality data while reducing burden on investigators, improving patient recruitment and retention, and ultimately accelerating the development of new treatments with more reliable evidence of safety and efficacy.