On the logic of collapsibility for causal effect measures
Liu et al. (2020) discuss the relation between efficacy measures within subgroups and efficacy measures on the population level, which can be obtained by merging the subgroups. They come to the conclusion that neither odds ratios (for binary endpoints) nor hazard ratios (for time-to-event endpoints)...
Published in: | Biometrical Journal |
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2021
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Online Access: | https://repository.publisso.de/resource/frl:6433311 https://doi.org/10.1002/bimj.202000305 |
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ftleibnizopen:oai:oai.leibnizopen.de:PSaKVYsBBwLIz6xGU1c2 2023-11-12T04:17:40+01:00 On the logic of collapsibility for causal effect measures Didelez, Vanessa Stensrud, Mats Julius 2021 https://repository.publisso.de/resource/frl:6433311 https://doi.org/10.1002/bimj.202000305 eng eng CC BY 4.0 Biometrical journal, 64(2):235-242 Causal inference Survival analysis Collapsibility Confounding 2021 ftleibnizopen https://doi.org/10.1002/bimj.202000305 2023-10-22T23:10:27Z Liu et al. (2020) discuss the relation between efficacy measures within subgroups and efficacy measures on the population level, which can be obtained by merging the subgroups. They come to the conclusion that neither odds ratios (for binary endpoints) nor hazard ratios (for time-to-event endpoints) are suitable measures of efficacy in this context. This insight is not new, and more general settings have been considered previously (Daniel, Zhang, & Farewell, 2020; Greenland & Pearl, 2011; Greenland, Robins, & Pearl, 1999; Huitfeldt, Stensrud, & Suzuki, 2019; Martinussen & Vansteelandt, 2013; Pang, Kaufman, & Platt, 2013; Sjölander, Dahlqwist, & Zetterqvist, 2016). While we largely agree with their conclusion, we do so for different reasons and would like to point out a number of important subtleties that have perhaps not been appreciated by Liu et al. (2020). These should be carefully understood to avoid any further misleading interpretations. In particular, we want to emphasise, like many before, that confounding and non-collapsibility are separate issues (Didelez et al., 2010; Greenland, 1996; Greenland & Pearl, 2011; Greenland et al., 1999; Pand, Kaufman, & Platt, 2013; Pang et al., 2013; Shrier & Pang, 2015); to cite Greenland (2011): ‘confounding may occur with or without non-collapsibility, and non-collapsibility may occur with or without confounding’. Moreover, in view of patients and investigators preferring contrasts in terms of absolute risks (Murray, Caniglia, Swanson, Hernández-Díaz, & Hernán, 2018), we are sceptical about the emphasis on relative median survival time proposed in Liu et al. (2020). Other/Unknown Material Greenland Unknown Greenland Hernán ENVELOPE(-62.867,-62.867,-64.900,-64.900) Huitfeldt ENVELOPE(-64.754,-64.754,-65.993,-65.993) Biometrical Journal 64 2 235 242 |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
Causal inference Survival analysis Collapsibility Confounding |
spellingShingle |
Causal inference Survival analysis Collapsibility Confounding Didelez, Vanessa Stensrud, Mats Julius On the logic of collapsibility for causal effect measures |
topic_facet |
Causal inference Survival analysis Collapsibility Confounding |
description |
Liu et al. (2020) discuss the relation between efficacy measures within subgroups and efficacy measures on the population level, which can be obtained by merging the subgroups. They come to the conclusion that neither odds ratios (for binary endpoints) nor hazard ratios (for time-to-event endpoints) are suitable measures of efficacy in this context. This insight is not new, and more general settings have been considered previously (Daniel, Zhang, & Farewell, 2020; Greenland & Pearl, 2011; Greenland, Robins, & Pearl, 1999; Huitfeldt, Stensrud, & Suzuki, 2019; Martinussen & Vansteelandt, 2013; Pang, Kaufman, & Platt, 2013; Sjölander, Dahlqwist, & Zetterqvist, 2016). While we largely agree with their conclusion, we do so for different reasons and would like to point out a number of important subtleties that have perhaps not been appreciated by Liu et al. (2020). These should be carefully understood to avoid any further misleading interpretations. In particular, we want to emphasise, like many before, that confounding and non-collapsibility are separate issues (Didelez et al., 2010; Greenland, 1996; Greenland & Pearl, 2011; Greenland et al., 1999; Pand, Kaufman, & Platt, 2013; Pang et al., 2013; Shrier & Pang, 2015); to cite Greenland (2011): ‘confounding may occur with or without non-collapsibility, and non-collapsibility may occur with or without confounding’. Moreover, in view of patients and investigators preferring contrasts in terms of absolute risks (Murray, Caniglia, Swanson, Hernández-Díaz, & Hernán, 2018), we are sceptical about the emphasis on relative median survival time proposed in Liu et al. (2020). |
author |
Didelez, Vanessa Stensrud, Mats Julius |
author_facet |
Didelez, Vanessa Stensrud, Mats Julius |
author_sort |
Didelez, Vanessa |
title |
On the logic of collapsibility for causal effect measures |
title_short |
On the logic of collapsibility for causal effect measures |
title_full |
On the logic of collapsibility for causal effect measures |
title_fullStr |
On the logic of collapsibility for causal effect measures |
title_full_unstemmed |
On the logic of collapsibility for causal effect measures |
title_sort |
on the logic of collapsibility for causal effect measures |
publishDate |
2021 |
url |
https://repository.publisso.de/resource/frl:6433311 https://doi.org/10.1002/bimj.202000305 |
long_lat |
ENVELOPE(-62.867,-62.867,-64.900,-64.900) ENVELOPE(-64.754,-64.754,-65.993,-65.993) |
geographic |
Greenland Hernán Huitfeldt |
geographic_facet |
Greenland Hernán Huitfeldt |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
Biometrical journal, 64(2):235-242 |
op_rights |
CC BY 4.0 |
op_doi |
https://doi.org/10.1002/bimj.202000305 |
container_title |
Biometrical Journal |
container_volume |
64 |
container_issue |
2 |
container_start_page |
235 |
op_container_end_page |
242 |
_version_ |
1782334469594677248 |