U1246 SPHERE
SPHERE is a multidisciplinary unit that seeks to develop and validate methods that can be used in clinical or epidemiological studies.
The philosophy that drives the SPHERE unit is that the patient must be considered as a whole, i.e. taking into account his/her environment, and integrating his/her perceptions, experience, and wishes.
4 axes of research
Our latest news
Two pages on SPHERE in the latest issue of Inserm, Le magazine!
Public Health: Consider the entire patient sphere
pages 12-13
Methodological review showed that time-to-event outcomes are often inadequately handled in cluster randomized trials
Objectives: To estimate the prevalence of time-to-event (TTE) outcomes in cluster randomized trials (CRTs) and to examine their
statistical management.
Study design and setting: We searched PubMed to identify primary reports of CRTs published in six major general medical journals
(2013–2018). Nature of outcomes and, for TTE outcomes, statistical methods for sample size, analysis, and measures of intracluster
correlation were extracted.
Results: A TTE analysis was used in 17% of the CRTs (32/184) either as a primary or secondary outcome analysis, or in a sensitivity
analysis. Among the five CRTs with a TTE primary outcome, two accounted for both intracluster correlation and the TTE nature of the
outcome in sample size calculation; one reported a measure of intracluster correlation in the analysis. Among the 32 CRTs with a least
one TTE analysis, 44% (14/32) accounted for clustering in all TTE analyses. We identified 12 additional CRTs in which there was at
least one outcome not analyzed as TTE for which a TTE analysis might have been preferred.
Conclusion: TTE outcomes are not uncommon in CRTs but appropriate statistical methods are infrequently used. Our results suggest
that further methodological development and explicit recommendations for TTE outcomes in CRTs are needed.
Using pooling for RT-qPCR tests
One of the challenges of the current COVID-19 pandemic is the need to test populations as widely as possible to better detect spread and evolution. However, technical problems were highlighted, such as the tension on the availability of reagents. To limit this problem, pooling methods (mixing multiple samples before testing) are regularly considered in RT-qPCR (see, for example, Gollier et Gossner (2020)).
In this talk, we will begin by explaining the principle of an RT-qPCR test and recall the definition of the false positives and negatives. We will then look at the pooling principle and how this procedure influences the results on the rate of false negatives. In particular, we will highlight the importance of knowing the distribution of viral load concentration. Therefore, we will continue on the difficulty of estimating this concentration and conclude with a few procedures that could be applied to help in this time of crisis.
