Standardized study performance, quality assurance, and quality control in a cluster-randomized trial: the Pneumococcal Vaccine Schedules trial.

Abstract

Randomized controlled trials are considered the "gold standard" for evaluating the effectiveness of an intervention. However, large-scale, cluster-randomized trials are complex and costly to implement. The generation of accurate, reliable, and high-quality data is essential to ensure the validity and generalizability of findings. Robust quality assurance and quality control procedures are important to optimize and validate the quality, accuracy, and reliability of trial data. To date, few studies have reported on study procedures to assess and optimize data integrity during the implementation of large cluster-randomized trials. The dearth of literature on these methods of trial implementation may contribute to questions about the quality of data collected in clinical trials. Trial protocols should consider the inclusion of quality assurance indicators and targets for implementation. Publishing quality assurance and control measures implemented in clinical trials should increase public trust in the findings from such studies. In this manuscript, we describe the development and implementation of internal and external quality assurance and control procedures and metrics in the Pneumococcal Vaccine Schedules trial currently ongoing in rural Gambia. This manuscript focuses on procedures and metrics to optimize trial implementation and validate clinical, laboratory, and field data. We used a mixture of procedure repetition, supervisory visits, checklists, data cleaning and verification methods and used the metrics to drive process improvement in all domains.