The Consequences of Neglected Confounding and Interactions in Mixed-Effects Meta-Regression: An Illustrative Example
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| Title: | The Consequences of Neglected Confounding and Interactions in Mixed-Effects Meta-Regression: An Illustrative Example |
|---|---|
| Language: | English |
| Authors: | Knop, Eric S. (ORCID |
| Source: | Research Synthesis Methods. Jul 2023 14(4):647-651. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 5 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Regression (Statistics), Meta Analysis, Death, Heart Disorders, Recruitment, Age, Bias |
| DOI: | 10.1002/jrsm.1643 |
| ISSN: | 1759-2879 1759-2887 |
| Abstract: | Analysts seldom include interaction terms in their meta-regression model, which can introduce bias if an interaction is present. We illustrate this by reanalysing a meta-regression study in acute heart failure. Based on a total of 285 studies, the 1-year mortality rate related to acute heart failure is considered and the connection to the study-level covariates year of recruitment and average age of study participants are of interest. We show that neglecting a possibly confounding variable and an interaction term might lead to erroneous inference and conclusions. Based on our results and accompanying simulations, we recommend to include possible confounders and interaction terms, whenever they are plausible, in mixed-effects meta-regression models. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1383565 |
| Database: | ERIC |
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| Abstract: | Analysts seldom include interaction terms in their meta-regression model, which can introduce bias if an interaction is present. We illustrate this by reanalysing a meta-regression study in acute heart failure. Based on a total of 285 studies, the 1-year mortality rate related to acute heart failure is considered and the connection to the study-level covariates year of recruitment and average age of study participants are of interest. We show that neglecting a possibly confounding variable and an interaction term might lead to erroneous inference and conclusions. Based on our results and accompanying simulations, we recommend to include possible confounders and interaction terms, whenever they are plausible, in mixed-effects meta-regression models. |
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| ISSN: | 1759-2879 1759-2887 |
| DOI: | 10.1002/jrsm.1643 |
