This webinar will feature presentations from 3 speakers on the topic of Using Visualisations to Help Make Decisions:
• Caroline Caudan - Interactive statistical monitoring to optimize review of potential study issue with R-Shiny
• Paolo Eusebi - Effective visualization of uncertainty – Where we are and where to go
• Michael O’Kelly - Subgroup analysis: a look at the SEAMOS approach (Standardised Effects Adjusted for Multiple Overlapping Subgroups)
These presentations were originally planned as part of the 2020 PSI conference in Barcelona, and have been reorganized as a webinar.
You can now register for this event. Registration will close at 12:00 on 2nd September 2020.
To register your place, please click here
Interactive statistical monitoring to optimize review of potential study issue with R-Shin
Background: Statistical Monitoring involves the review of prospective study data collected in participating site to detect inconsistencies between patients and between sites in term of trends.
Method: A Phase IV study (PRO-MSACTIVE) is currently evaluating ocrelizumab in active relapsing Multiple Sclerosis patients in France. Specific statistical methods (volcano plots, mahalanobis distance, funnel plot …), described in a statistical monitoring plan, have been applied to SDTM database to detect potential issues (duplicate records, under-reporting of AEs or PROs, outliers, patients with similar characteristics…). An application has been developed using R-Shiny to generate an interactive web app to ease the identification of site and/or patient during the statistical data review meeting.
Results: The PRO-MSACTIVE study enrolled 422 patients by 46 sites between July 2018 and August 2019. The 3rd data review meeting was held on the 17th of october 2019 and 18 standard and planned tests were run on baseline characteristics and follow-up data, with a total of 15 (32.6%) sites identified as needing review/investigation.
Conclusion: Statistical monitoring is useful to identify unusual or clustered data patterns that might be revealing issues that could impact the data integrity and/or may potentially impact patient’s safety. With appropriate interactive data visualization, the important findings can easily be identified/reviewed by study team and appropriate actions be set up and assigned to the most appropriate function for a close follow-up. Interactive statistical monitoring is time consuming to initiate using R-Shiny, but time saving from the 1st data review as long as analysis performed at each meeting remains similar.
Effective visualization of uncertainty – Where we are and where to go
Statistics is all about uncertainty and uncertainty shows up in different ways in our research. If we want to answer questions about the development of the symptoms of an individual patient, we explore the overall distribution of the patients. For questions about the differences between e.g. treatment groups in studies, we are interested on the precision of the treatment effect. And as statisticians we face uncertainty by applying models with limited knowledge about the data generating processes.
The communication about uncertainty in its different forms plays a central role in statistics, yet it’s not well done in high profile medical journals. A random sample (n=50) was obtained from 777 RCTs papers published in BMJ, JAMA, Lancet and NEJM from November 2017 to October 2019.
Overall, uncertainty was not even considered in most of the plots. Those displaying uncertainty predominantly used whiskers or bands for confidence intervals. However, confidence intervals only poorly give a perception of a distribution.
The presentation will showcase different ways to better display uncertainty as inspired from other fields, including politics and weather forecast. These examples will cover also interactive and dynamic plots, which can enrich data visualization experience in electronic media.
Applications of these techniques for clinical trials and other medical data sources will be shown in addition to demonstrate how communication of uncertainty can be improved in medical statistics. These applications will make use of both frequentist and Bayesian approaches.
Subgroup analysis: a look at the SEAMOS approach (Standardised Effects Adjusted for Multiple Overlapping Subgroups)
In 2018, the PSI/EFSPI Working Group on Subgroup Analysis issued a White Paper in Pharmaceutical Statistics, which noted the usefulness of SEAMOS, a forest-plot based approach, as well as a number of other approaches. SEAMOS resamples from the data, using as its criterion the most extreme estimate of treatment effect, compared to the overall estimate of treatment effect. This presentation explores the SEAMOS approach further, using an idea due to PSI/EFSPI Working Group member Tom Parke, where the candidate subgroups of the forest plot are assessed collectively, using as a criterion the overlap of the multiple confidence intervals of standardised treatment effect estimates, where the measure of difference in subgroups is the confidence level required to preserve overlap of confidence intervals within a set of categories (e.g. preserve overlap between male and female; preserve overlap across regions). SEAMOS and its variants give rise to plots that may help in understanding the true significance of subgroup differences; furthermore, some multivariate parametric assessment of the “extremeness” of the subgroups in a forest plot is possible, as well as the resampling-based approach described in the source paper. This presentation looks at what can work best in practice, and when.