Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring.

Background/aims: A risk-based approach to clinical research may include a central statistical assessment of data quality. We investigated the operating characteristics of unsupervised statistical monitoring aimed at detecting atypical data in multicenter experiments. The approach is premised on the assumption that, save for random fluctuations and natural variations, data coming from all centers should be comparable and statistically consistent.

Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial.

Introduction: Data quality may impact the outcome of clinical trials; hence, there is a need to implement quality control strategies for the data collected. Traditional approaches to quality control have primarily used source data verification during on-site monitoring visits, but these approaches are hugely expensive as well as ineffective. There is growing interest in central statistical monitoring (CSM) as an effective way to ensure data quality and consistency in multicenter clinical trials.

Liver transplantation for hepatocellular cancer: UCL experience in 137 adult cirrhotic patients. Alpha-foetoprotein level and locoregional treatment as refined selection criteria.

Liver transplantation (LT) is a validated treatment for selected cirrhotics with hepatocellular cancer (HCC). A retrospective single center study including 137 recipients having proven HCC was done to refine inclusion criteria for LT as well as to look at impact of locoregional treatment (LRT) on outcome. At pre-LT imaging, 42 (30.

A statistical approach to central monitoring of data quality in clinical trials.

Background: Classical monitoring approaches rely on extensive on-site visits and source data verification. These activities are associated with high cost and a limited contribution to data quality. Central statistical monitoring is of particular interest to address these shortcomings.