Impact of Misdiagnosis in Case-Control Studies of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome 2023 Malato et al

Abstract:
Misdiagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) can occur when different case definitions are used by clinicians (relative misdiagnosis) or when failing the genuine diagnosis of another disease (misdiagnosis in a strict sense). This problem translates to a recurrent difficulty in reproducing research findings. To tackle this problem, we simulated data from case-control studies under misdiagnosis in a strict sense. We then estimated the power to detect a genuine association between a potential causal factor and ME/CFS. A minimum power of 80% was obtained for studies with more than 500 individuals per study group.

When the simulation study was extended to the situation where the potential causal factor could not be determined perfectly (e.g., seropositive/seronegative in serological association studies), the minimum power of 80% could only be achieved in studies with more than 1000 individuals per group.

In conclusion, current ME/CFS studies have suboptimal power under the assumption of misdiagnosis. This power can be improved by increasing the overall sample size using multi-centric studies, reporting the excluded illnesses and their exclusion criteria, or focusing on a homogeneous cohort of ME/CFS patients with a specific pathological mechanism where the chance of misdiagnosis is reduced.

https://www.mdpi.com/2075-4418/13/3/531

 

Depressing but not unexpected. We have long known that the tiny cohorts common in ME research are a problem. But I didn't realise they needed to be that much larger for one of the most important of study questions, i.e. what is the causal factor. And their calculations don't even take into account the possibility of even well characterised ME cohorts potentially containing several subgroups (the authors mention this as a limitation themselves).

I'm not sure I understand this. Why do they think that their mathematical model covers the possibility of two ME subgroups? The argument seems to be that if you mathematically treat one subgroup as a "misdiagnosis" then their mathematical model for actual misdiagnosis applies. But wouldn't the two things have a cumulative effect? In any real life cohort you will have a certain number of true misdiagnoses diluting any signal from the data - this they've modelled for - but if the rest of the cohort potentially consists of type 1 and type 2 ME, then wouldn't that mean you need even larger cohorts than predicted by their modelling to pick up on any signal?