期刊
DIAGNOSTICS
卷 13, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/diagnostics13030531
关键词
misdiagnosis; misclassification; association studies; simulation; statistical power; ME; CFS
Misdiagnosis of ME/CFS can be caused by different case definitions or failure to diagnose other diseases, leading to difficulties in reproducing research findings. Simulated data showed that studies with over 500 individuals per group have a minimum power of 80% to detect a genuine association. However, in studies where the potential causal factor is not perfectly determined, such as serological association studies, a minimum power of 80% can only be achieved with over 1000 individuals per group.
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.
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