DOING STATISTICS IN DARKNESS: WHAT CAN A BIOESTATICIAN ADVISE TO ACELERATE THE UNDERSTANDING OF MYALGIC ENCEPHALOMYELITIS/CHRONIC FATIGUE SYNDROME

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating disease with unknown cause characterized by non-specific symptoms, such as fatigue, post exertion malaise, abnormal sleep patterns and frequent viral infections. The respective diagnostic is cumbersome due to the inexistence of a disease-specific biomarker. As a consequence, problems related to the definition of sampling population, patient’s classification, selective bias and confounding often emerge in clinical data from this disease. In addition, the respective statistical analyses involve a large number of tests exploring different hypotheses for the cause of ME/CFS. As such, false positive results might emerge if multiple testing is properly not accounted for in the analysis. In this talk, I will discuss all these statistical problems using data from the UK ME/CFS biobank as a case study. I will also introduce the new European network on ME/CFS (EUROMENE) and how it can be used to tackle some of the above problems.