Cargando…

Hybrid approaches to clinical trial monitoring: Practical alternatives to 100% source data verification

For years, a vast majority of clinical trial industry has followed the tenet of 100% source data verification (SDV). This has been driven partly by the overcautious approach to linking quality of data to the extent of monitoring and SDV and partly by being on the safer side of regulations. The regul...

Descripción completa

Detalles Bibliográficos
Autor principal: De, Sourabh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications Pvt Ltd 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3159208/
https://www.ncbi.nlm.nih.gov/pubmed/21897885
http://dx.doi.org/10.4103/2229-3485.83226
Descripción
Sumario:For years, a vast majority of clinical trial industry has followed the tenet of 100% source data verification (SDV). This has been driven partly by the overcautious approach to linking quality of data to the extent of monitoring and SDV and partly by being on the safer side of regulations. The regulations however, do not state any upper or lower limits of SDV. What it expects from researchers and the sponsors is methodologies which ensure data quality. How the industry does it is open to innovation and application of statistical methods, targeted and remote monitoring, real time reporting, adaptive monitoring schedules, etc. In short, hybrid approaches to monitoring. Coupled with concepts of optimum monitoring and SDV at site and off-site monitoring techniques, it should be possible to save time required to conduct SDV leading to more available time for other productive activities. Organizations stand to gain directly or indirectly from such savings, whether by diverting the funds back to the R&D pipeline; investing more in technology infrastructure to support large trials; or simply increasing sample size of trials. Whether it also affects the work-life balance of monitors who may then need to travel with a less hectic schedule for the same level of quality and productivity can be predicted only when there is more evidence from field.