Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials.

BACKGROUND: Central monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials. MATERIAL AND METHODS: Quality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10 P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal. RESULTS: The SDM quality metrics showed improvement in 83% (95% CI, 80-85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41-70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct. CONCLUSION: The results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.

Does Central Monitoring Lead to Higher Quality? An Analysis of Key Risk Indicator Outcomes.

BACKGROUND: Central monitoring, which typically includes the use of key risk indicators (KRIs), aims at improving the quality of clinical research by pro-actively identifying and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. However, there has to-date been a relative lack of direct quantitative evidence published supporting the claim that central monitoring actually leads to improved quality. MATERIAL AND METHODS: Nine commonly used KRIs were analyzed for evidence of quality improvement using data retrieved from a large central monitoring platform. A total of 212 studies comprising 1676 sites with KRI signals were used in the analysis, representing central monitoring activity from 23 different sponsor organizations. Two quality improvement metrics were assessed for each KRI, one based on a statistical score (p-value) and the other based on a KRI's observed value. RESULTS: Both KRI quality metrics showed improvement in a vast majority of sites (82.9% for statistical score, 81.1% for observed KRI value). Additionally, the statistical score and the observed KRI values improved, respectively by 66.1% and 72.4% on average towards the study average for those sites showing improvement. CONCLUSION: The results of this analysis provide clear quantitative evidence supporting the hypothesis that use of KRIs in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.

Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring.

BACKGROUND: A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations. MATERIAL AND METHODS: The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud. RESULTS: Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported. CONCLUSION: An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.

Genetic similarities between tobacco use disorder and related comorbidities: an exploratory study.

BACKGROUND: Tobacco use disorder (TUD), defined as the use of tobacco to the detriment of a person's health or social functioning, is associated with various disorders. We hypothesized that mutual variation in genes may partly explain this link. The aims of this study were to make a non-exhaustive inventory of the disorders using (partially) the same genetic pathways as TUD, and to describe the genetic similarities between TUD and the selected disorders. METHODS: We developed a 3 stage approach: (i) selection of genes influencing TUD using Gene2Mesh and Ingenuity Pathway Analysis (IPA), (ii) selection of disorders associated with the selected genes using IPA and (iii) genetic similarities between disorders associated with TUD using Jaccard distance and cluster analyses. RESULTS: Fourteen disorders and thirty-two genes met our inclusion criteria. The Jaccard distance between pairs of disorders ranged from 0.00 (e.g. oesophageal cancer and malignant hypertension) to 0.45 (e.g. bladder cancer and addiction). A lower number in the Jaccard distance indicates a higher similarity between the two disorders. Two main clusters of genetically similar disorders were observed, one including coexisting disorders (e.g. addiction and alcoholism) and the other one with the side-effects of smoking (e.g. gastric cancer and malignant hypertension). CONCLUSIONS: This exploratory study partly explains the potential genetic components linking TUD to other disorders. Two principle clusters of disorders were observed (i) coexisting disorders of TUD and (ii) side-effects of TUD disorders. A further deepening of this observation in a real life study should allow strengthening this hypothesis.

Communicating genetics and smoking through social media: are we there yet?

BACKGROUND: Social media is a recent source of health information that could disseminate new scientific research, such as the genetics of smoking. OBJECTIVE: The objectives were (1) to evaluate the availability of genetic information about smoking on different social media platforms (ie, YouTube, Facebook, and Twitter) and (2) to assess the type and the content of the information displayed on the social media as well as the profile of people publishing this information. METHODS: We screened posts on YouTube, Facebook, and Twitter with the terms "smoking" and "genetic" at two time points (September 18, 2012, and May 7, 2013). The first 100 posts were reviewed for each media for the time points. Google was searched during Time 2 as an indicator of available information on the Web and the other social media that discussed genetics and smoking. The source of information, the country of the publisher, characteristics of the posts, and content of the posts were extracted. RESULTS: On YouTube, Facebook, and Twitter, 31, 0, and 84 posts, respectively, were included. Posts were mostly based on smoking-related diseases, referred to scientific publications, and were largely from the United States. From the Google search, most results were scientific databases. Six scientific publications referred to within the Google search were also retrieved on either YouTube or Twitter. CONCLUSIONS: Despite the importance of public understanding of smoking and genetics, and the high use of social media, little information on this topic is actually present on social media. Therefore, there is a need to monitor the information that is there and to evaluate the population's understanding of the information related to genetics and smoking that is displayed on social media.

Impact of genetic notification on smoking cessation: systematic review and pooled-analysis.

OBJECTIVES: This study aimed to evaluate the impact of genetic notification of smoking-related disease risk on smoking cessation in the general population. Secondary objectives were to assess the impact of genetic notification on intention-to-quit smoking and on emotional outcomes as well as the understanding and the recall of this notification. METHODS: A systematic review of articles from inception to August 2011 without language restriction was realized using PubMed, Embase, Scopus, Web of Science, PsycINFO and Toxnet. Other publications were identified using hand search. The pooled-analysis included only randomized trials. Comparison groups were (i) high and low genetic risk versus control, and (ii) high versus low genetic risk. For the pooled-analysis random effect models were applied and sensitivity analyses were conducted. RESULTS: Eight papers from seven different studies met the inclusion criteria of the review. High genetic risk notification was associated with short-term increased depression and anxiety. Four randomized studies were included in the pooled-analysis, which revealed a significant impact of genetic notification on smoking cessation in comparison to controls (clinical risk notification or no intervention) in short term follow-up less than 6 months (RR = 1.55, 95% CI 1.09-2.21). CONCLUSIONS: In short term follow-up, genetic notification increased smoking cessation in comparison to control interventions. However, there is no evidence of long term effect (up to 12 month) on smoking cessation. Further research is needed to assess more in depth how genetic notification of smoking-related disease could contribute to smoking cessation.