Noncollapsibility, confounding, and sparse-data bias. Part 2 : What should researchers make of persistent controversies about the odds ratio?
Copyright © 2021 Elsevier Inc. All rights reserved..
A previous note illustrated how the odds of an outcome have an undesirable property for risk summarization and communication: Noncollapsibility, defined as a failure of a group measure to represent a simple average of the measure over individuals or subgroups. The present sequel discusses how odds ratios amplify odds noncollapsibility and provides a basic numeric illustration of how noncollapsibility differs from confounding of effects (with which it is often confused). It also draws a connection of noncollapsibility to sparse-data bias in logistic, log-linear, and proportional-hazards regression.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:139 |
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| Enthalten in: |
Journal of clinical epidemiology - 139(2021) vom: 01. Nov., Seite 264-268 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Greenland, Sander [VerfasserIn] |
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| Links: |
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| Themen: |
Causality |
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| Anmerkungen: |
Date Completed 20.12.2021 Date Revised 20.12.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jclinepi.2021.06.004 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2021 Elsevier Inc. All rights reserved. | ||
| 520 | |a A previous note illustrated how the odds of an outcome have an undesirable property for risk summarization and communication: Noncollapsibility, defined as a failure of a group measure to represent a simple average of the measure over individuals or subgroups. The present sequel discusses how odds ratios amplify odds noncollapsibility and provides a basic numeric illustration of how noncollapsibility differs from confounding of effects (with which it is often confused). It also draws a connection of noncollapsibility to sparse-data bias in logistic, log-linear, and proportional-hazards regression | ||
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| 650 | 4 | |a Noncollapsibility | |
| 650 | 4 | |a Odds Ratio | |
| 650 | 4 | |a Rate Ratio | |
| 650 | 4 | |a Simpson's paradox | |
| 650 | 4 | |a Sparse-data bias | |
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