Discussion on "Correct and logical causal inference for binary and time-to-event outcomes in randomized controlled trials" by Yi Liu, Bushi Wang, Miao Yang, Jianan Hui, Heng Xu, Siyoen Kil, and Jason C. Hsu.
causal inference
companion diagnostic
confounding
estimand
prognostic and predictive biomarkers
subgroup analysis
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
revised:
28
11
2020
received:
26
10
2020
accepted:
28
11
2020
pubmed:
31
12
2020
medline:
26
4
2022
entrez:
30
12
2020
Statut:
ppublish
Résumé
In their paper, Liu et al. (2020) pointed out illogical discrepancies between subgroup and overall causal effects for some efficacy measures, in particular the odds and hazard ratios. As the authors show, the culprit is subgroups having prognostic effects within treatment arms. In response to their provocative findings, we found that the odds and hazard ratios are logic respecting when the subgroups are purely predictive, that is, the distribution of the potential outcome for the control treatment is homogeneous across subgroups. We also found that when we redefined the odds and hazards ratio causal estimands in terms of the joint distribution of the potential outcomes, the discrepancies are resolved under specific models in which the potential outcomes are conditionally independent. In response to other discussion points in the paper, we also provide remarks on association versus causation, confounding, statistical computing software, and dichotomania.
Identifiants
pubmed: 33377537
doi: 10.1002/bimj.202000320
doi:
Substances chimiques
Plant Extracts
0
bushi
0
Types de publication
Journal Article
Comment
Langues
eng
Sous-ensembles de citation
IM
Pagination
225-234Commentaires et corrections
Type : CommentOn
Informations de copyright
© 2020 Wiley-VCH GmbH.
Références
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