Multicenter studies are widely used to meet accrual targets in clinical trials. Clinical data monitoring is required to ensure the quality and validity of the data gathered across centers. One approach to this end is central statistical monitoring, which aims at detecting atypical patterns in the data by means of statistical methods. In this context, we consider the simple case of a continuous variable, and we propose a detection procedure based on a linear mixed-effects model to detect location differences between each center and all other centers. We describe the performance of the procedure as a function of contamination rate and signal-to-noise ratio. We investigate the effect of center size and variance structure and illustrate the use of the procedure using data from two multicenter clinical trials.

As part of central statistical monitoring of multicenter clinical trial data, we propose a procedure based on the beta-binomial distribution for the detection of centers with atypical values for the probability of some event. The procedure makes no assumptions about the typical event proportion and uses the event counts from all centers to derive a reference model. The procedure is shown through simulations to have high sensitivity and high specificity if the contamination rate is small and the atypical event proportions are the result of some systematic shift in the underlying data generating mechanism.

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. Unsupervised statistical monitoring consists of performing as many statistical tests as possible on all trial data, in order to detect centers whose data are inconsistent with data from other centers. Methods We conducted simulations using data from a large multicenter trial conducted in Japan for patients with advanced gastric cancer. The actual trial data were contaminated in computer simulations for varying percentages of centers, percentages of patients modified within each center and numbers and types of modified variables. The unsupervised statistical monitoring software was run by a blinded team on the contaminated data sets, with the purpose of detecting the centers with contaminated data. The operating characteristics (sensitivity, specificity and Youden’s J-index) were calculated for three detection methods: one using the p-values of individual statistical tests after adjustment for multiplicity, one using a summary of all p-values for a given center, called the Data Inconsistency Score, and one using both of these methods. Results The operating characteristics of the three methods were satisfactory in situations of data contamination likely to occur in practice, specifically when a single or a few centers were contaminated. As expected, the sensitivity increased for increasing proportions of patients and increasing numbers of variables contaminated. The three methods showed a specificity better than 93% in all scenarios of contamination. The method based on the Data Inconsistency Score and individual p-values adjusted for multiplicity generally had slightly higher sensitivity at the expense of a slightly lower specificity. Conclusions The use of brute force (a computer-intensive approach that generates large numbers of statistical tests) is an effective way to check data quality in multicenter clinical trials. It can provide a cost-effective complement to other data-management and monitoring techniques.

Objective Glucagon is secreted by pancreatic α-cells in response to hypoglycemia and its hyperglycemic effect helps to restore normal blood glucose. Insulin and somatostatin (SST) secretions from β- and δ-cells, respectively, are stimulated by glucose by mechanisms involving an inhibition of their ATP-sensitive K + (K ATP ) channels, leading to an increase in [Ca 2+ ] c that triggers exocytosis. Drugs that close K ATP channels, such as sulfonylureas, are used to stimulate insulin release in type 2 diabetic patients. α-cells also express K ATP channels. However, the mechanisms by which sulfonylureas control glucagon secretion are still largely debated and were addressed in the present study. In particular, we studied the effects of K ATP channel blockers on α-cell [Ca 2+ ] c and glucagon secretion in the presence of a low (1 mM) or a high (15 mM) glucose concentration and evaluated the role of SST in these effects. Methods Using a transgenic mouse model expressing the Ca 2+ -sensitive fluorescent protein, GCaMP6f, specifically in α-cells, we measured [Ca 2+ ] c in α-cells either dispersed or within whole islets (by confocal microscopy). By measuring [Ca 2+ ] c in α-cells within islets and glucagon secretion using the same perifusion protocols, we tested whether glucagon secretion correlated with changes in [Ca 2+ ] c in response to sulfonylureas. We studied the role of SST in the effects of sulfonylureas using multiple approaches including genetic ablation of SST, or application of SST-14 and SST receptor antagonists. Results Application of the sulfonylureas, tolbutamide, or gliclazide, to a medium containing 1 mM or 15 mM glucose increased [Ca 2+ ] c in α-cells by a direct effect as in β-cells. At low glucose, sulfonylureas inhibited glucagon secretion of islets despite the rise in α-cell [Ca 2+ ] c that they triggered. This glucagonostatic effect was indirect and attributed to SST because, in the islets of SST-knockout mice, sulfonylureas induced a stimulation of glucagon secretion which correlated with an increase in α-cell [Ca 2+ ] c . Experiments with exogenous SST-14 and SST receptor antagonists indicated that the glucagonostatic effect of sulfonylureas mainly resulted from an inhibition of the efficacy of cytosolic Ca 2+ on exocytosis. Although SST-14 was also able to inhibit glucagon secretion by decreasing α-cell [Ca ...

CSM can be used to provide a check of the quality of the data from completed multicenter clinical trials before analysis, publication, and submission of the results to regulatory agencies. It can also form the basis of a risk-based monitoring strategy in ongoing multicenter trials. CSM aims at improving data quality in clinical trials while also reducing monitoring costs.