Rahmadi, Ridho, Groot, Perry, van Rijn, Marieke Hc et al. · Statistical methods in medical research · 2018 · DOI
This study developed a new statistical method for finding cause-and-effect relationships in long-term health data, even when sample sizes are small. The researchers tested their method on data from people with chronic fatigue syndrome, Alzheimer's disease, and chronic kidney disease, and found it could identify stable patterns of how different health factors influence each other over time.
Understanding causal relationships in ME/CFS is challenging because the disease involves multiple interconnected physiological systems that change over time. This methodological advance provides researchers with a more stable statistical tool to identify which factors actually cause changes in ME/CFS symptoms and disease progression, potentially leading to better targeted interventions.
This is a methodological paper, not a clinical outcomes study; it does not prove specific causal relationships exist in ME/CFS, only that the statistical method can identify stable causal patterns when applied to real disease data. The stability of findings across subsamples does not confirm biological causation—it only indicates which relationships are most robust in the data. Findings still require validation through independent studies and mechanistic investigation.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
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Primary citation
Rahmadi, Ridho, Groot, Perry, van Rijn, Marieke Hc, van den Brand, Jan Ajg, Heins, Marianne, Knoop, Hans, et al. (2018). Causality on longitudinal data: Stable specification search in constrained structural equation modeling.. Statistical methods in medical research. https://doi.org/10.1177/0962280217713347
BibTeX
@article{mecfsatlas-rahmadi-2018-causality-longitudinal,
author = {Rahmadi, Ridho and Groot, Perry and van Rijn, Marieke Hc and van den Brand, Jan Ajg and Heins, Marianne and Knoop, Hans and Heskes, Tom and Alzheimer’s Disease Neuroimaging Initiative and MASTERPLAN Study Group and OPTIMISTIC consortium},
title = {Causality on longitudinal data: Stable specification search in constrained structural equation modeling.},
journal = {Statistical methods in medical research},
year = {2018},
doi = {10.1177/0962280217713347},
note = {PubMed: 28657454},
url = {https://www.mecfsatlas.com/evidence/rahmadi-2018-causality-longitudinal},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-29. https://www.mecfsatlas.com/evidence/rahmadi-2018-causality-longitudinal
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