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Home › Research › Treatments

Treatment Effects of Cholinesterase Inhibitors in Alzheimer’s Disease: a Causal Machine Learning Approach

Dedebant E, Even M, Törnqvist M, Hauw F, Fauvel T, Geoffroy C.. medRxiv, 2026-02-12

This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Treatments

Topic: Drug trials: efficacy and safety

A bstract INTRODUCTION Treatment response in Alzheimer’s disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heterogeneous treatment effects (HTE) from observational data while controlling for confounding bias. METHODS We developed a causal machine learning framework integrating expert-guided causal graphs, complementary HTE estimators, sensitivity analyses, and policy learning. We applied it to cholinesterase inhibitors (ChEIs) in MCI due to AD to patients from the NACC and ADNI cohorts. RESULTS Analysing 4,049 patients with 12-month and 2,223 with 36-month follow-up, all estimators indicated null or negative long-term ChEI effects on cognitive and functional outcomes, notably on functional measures. ChEIs showed slightly more deleterious effects among men than women. DISCUSSION This framework provides a methodology for estimating HTE from observational data. It revealed no beneficial responder subgroups, highlighting the challenge of detecting treatment heterogeneity in moderately sized cohorts. This approach can inform treatment selection for other AD therapies including memantine, anti-amyloid agents, and emerging treatments.

Key findings

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Study details

How this study scored 16/100
How this study's score is builtJournal tier 2 of 10; Journal's citation record 0 of 4; Study design 0 of 12; Strength of evidence 1 of 5; Peer-review validation 4 of 10; How recent 5 of 5; Sample size 3 of 4; Effect reported 0 of 3; Studied in people 3 of 3; Randomised / blinded 0 of 2; Times cited left out, no data; Citations per year left out, no data; Impact for its field and year left out, no data; Cited by studies here left out, no data; Still being cited left out, no data; Senior author's track record left out, no data; Institution's track record left out, no data; Multi-centre collaboration left out, no data; Indexed in PubMed 1 of 2; Free to read 0 of 2. 19 of the 62 points it could be judged on, which is 31 of 100. Raw total 31 of 100, shown as 16 because this is not a peer-reviewed research article.raw total 31 of 100capped at 16 — hatched points do not count

Preprint — not yet peer-reviewed. It scored 31 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 19 of the 62 points we could judge it on, which is 31 out of 100.

ComponentPointsWhy
Journal tier 2 / 10 Tier 4 — not a peer-reviewed journal (medRxiv)
Journal's citation record 0 / 4 Not a journal article, so the journal's record does not count
Study design 0 / 12 No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence 1 / 5 Rated “low” in our summary
Peer-review validation 4 / 10 Preprint — not yet peer-reviewed. Hosted on a preprint server.
How recent 5 / 5 2026 — within 1 year(s) of the newest study here
Sample size 3 / 4 4,049 participants
Effect reported 0 / 3 No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people 3 / 3 Studied in people (participant count stated)
Randomised / blinded 0 / 2 No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited — / 7 No citation count (not found in OpenAlex) — left out of this score
Citations per year — / 5 No citation count or no year — left out of this score
Impact for its field and year — / 6 No field-weighted citation impact (OpenAlex has none yet) — left out of this score
Cited by studies here — / 6 Not yet checked against this library's citation graph — left out of this score
Still being cited — / 4 No yearly citation counts — left out of this score
Senior author's track record — / 4 Senior author's record not available — left out of this score
Institution's track record — / 3 No institution listed in OpenAlex — left out of this score
Multi-centre collaboration — / 3 Institutions not listed — left out of this score
Indexed in PubMed 1 / 2 Not in PubMed, but has a DOI
Free to read 0 / 2 Behind a paywall — scores zero
Points 19 / 62 Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 19 of the 62 points we could judge it on is 31 out of 100.
Total 31 / 100 Capped to 16 because this is not a peer-reviewed article.

What each of these 20 components measures, and what this score is not →

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Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Dedebant, E., Even, M., Törnqvist, M., Hauw, F., Fauvel, T., & Geoffroy, C. (2026). Treatment Effects of Cholinesterase Inhibitors in Alzheimer’s Disease: a Causal Machine Learning Approach. medRxiv. https://doi.org/10.64898/2026.02.11.26346078

Vancouver

Dedebant E, Even M, Törnqvist M, Hauw F, Fauvel T, Geoffroy C. Treatment Effects of Cholinesterase Inhibitors in Alzheimer’s Disease: a Causal Machine Learning Approach. medRxiv. 2026. doi:10.64898/2026.02.11.26346078.

BibTeX

@article{dedebant2026-f682c193,
  author  = {Dedebant, E. and Even, M. and Törnqvist, M. and Hauw, F. and Fauvel, T. and Geoffroy, C.},
  title   = {{Treatment Effects of Cholinesterase Inhibitors in Alzheimer’s Disease: a Causal Machine Learning Approach}},
  journal = {medRxiv},
  year    = {2026},
  doi     = {10.64898/2026.02.11.26346078},
  url     = {https://doi.org/10.64898/2026.02.11.26346078}
}

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