Visualizing the target estimand in comparative effectiveness studies with multiple treatments Article Swipe
Gabrielle Simoneau
,
Marian Mitroiu
,
Thomas P. A. Debray
,
Wei Wei
,
Stan R. W. Wijn
,
Joana Caldas Magalhães
,
Justin Bohn
,
Changyu Shen
,
Fabio Pellegrini
,
Carl de Moor
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.57264/cer-2023-0089
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.57264/cer-2023-0089
Aim: Comparative effectiveness research using real-world data often involves pairwise propensity score matching to adjust for confounding bias. We show that corresponding treatment effect estimates may have limited external validity, and propose two visualization tools to clarify the target estimand. Materials & methods: We conduct a simulation study to demonstrate, with bivariate ellipses and joy plots, that differences in covariate distributions across treatment groups may affect the external validity of treatment effect estimates. We showcase how these visualization tools can facilitate the interpretation of target estimands in a case study comparing the effectiveness of teriflunomide (TERI), dimethyl fumarate (DMF) and natalizumab (NAT) on manual dexterity in patients with multiple sclerosis. Results: In the simulation study, estimates of the treatment effect greatly differed depending on the target population. For example, when comparing treatment B with C, the estimated treatment effect (and respective standard error) varied from -0.27 (0.03) to -0.37 (0.04) in the type of patients initially receiving treatment B and C, respectively. Visualization of the matched samples revealed that covariate distributions vary for each comparison and cannot be used to target one common treatment effect for the three treatment comparisons. In the case study, the bivariate distribution of age and disease duration varied across the population of patients receiving TERI, DMF or NAT. Although results suggest that DMF and NAT improve manual dexterity at 1 year compared with TERI, the effectiveness of DMF versus NAT differs depending on which target estimand is used. Conclusion: Visualization tools may help to clarify the target population in comparative effectiveness studies and resolve ambiguity about the interpretation of estimated treatment effects.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.57264/cer-2023-0089
- OA Status
- diamond
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391134143
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391134143Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.57264/cer-2023-0089Digital Object Identifier
- Title
-
Visualizing the target estimand in comparative effectiveness studies with multiple treatmentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-23Full publication date if available
- Authors
-
Gabrielle Simoneau, Marian Mitroiu, Thomas P. A. Debray, Wei Wei, Stan R. W. Wijn, Joana Caldas Magalhães, Justin Bohn, Changyu Shen, Fabio Pellegrini, Carl de MoorList of authors in order
- Landing page
-
https://doi.org/10.57264/cer-2023-0089Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.57264/cer-2023-0089Direct OA link when available
- Concepts
-
Covariate, Statistics, Propensity score matching, Matching (statistics), Pairwise comparison, Population, Confounding, Econometrics, Medicine, Computer science, Psychology, Mathematics, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.clarify | 36, 249 |
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| abstract_inverted_index.matched | 165 |
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| abstract_inverted_index.samples | 166 |
| abstract_inverted_index.studies | 256 |
| abstract_inverted_index.suggest | 215 |
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| abstract_inverted_index.compared | 226 |
| abstract_inverted_index.differed | 121 |
| abstract_inverted_index.dimethyl | 96 |
| abstract_inverted_index.duration | 201 |
| abstract_inverted_index.effects. | 266 |
| abstract_inverted_index.ellipses | 52 |
| abstract_inverted_index.estimand | 240 |
| abstract_inverted_index.example, | 128 |
| abstract_inverted_index.external | 28, 67 |
| abstract_inverted_index.fumarate | 97 |
| abstract_inverted_index.involves | 8 |
| abstract_inverted_index.matching | 12 |
| abstract_inverted_index.methods: | 42 |
| abstract_inverted_index.multiple | 108 |
| abstract_inverted_index.pairwise | 9 |
| abstract_inverted_index.patients | 106, 154, 207 |
| abstract_inverted_index.research | 3 |
| abstract_inverted_index.revealed | 167 |
| abstract_inverted_index.showcase | 74 |
| abstract_inverted_index.standard | 141 |
| abstract_inverted_index.validity | 68 |
| abstract_inverted_index.Materials | 40 |
| abstract_inverted_index.ambiguity | 259 |
| abstract_inverted_index.bivariate | 51, 195 |
| abstract_inverted_index.comparing | 90, 130 |
| abstract_inverted_index.covariate | 59, 169 |
| abstract_inverted_index.depending | 122, 236 |
| abstract_inverted_index.dexterity | 104, 222 |
| abstract_inverted_index.estimand. | 39 |
| abstract_inverted_index.estimands | 85 |
| abstract_inverted_index.estimated | 136, 264 |
| abstract_inverted_index.estimates | 24, 115 |
| abstract_inverted_index.initially | 155 |
| abstract_inverted_index.receiving | 156, 208 |
| abstract_inverted_index.treatment | 22, 62, 70, 118, 131, 137, 157, 183, 188, 265 |
| abstract_inverted_index.validity, | 29 |
| abstract_inverted_index.comparison | 174 |
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| abstract_inverted_index.facilitate | 80 |
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| abstract_inverted_index.comparisons. | 189 |
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| abstract_inverted_index.respectively. | 161 |
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| abstract_inverted_index.interpretation | 82, 262 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.69821159 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |