ArticleEpidemiology (Sunnyvale, Calif.)2015
The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model.
Article in Epidemiology (Sunnyvale, Calif.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Guide to Decomposition of Causal Effects Into Mediation, Interaction, and Direct Effects: Case Study on Aerobic Exercise and Parkinson Disease.Neurology · 2024Article
- Pseudo-value regression trees.Lifetime data analysis · 2024Article
- High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.Scientific reports · 2024Article
- Linked shrinkage to improve estimation of interaction effects in regression models.Epidemiologic methods · 2024Article
- Comparative analysis of all-terrain vehicles, motorcycle and automobile-related trauma in a rural border community of the USA.BMJ open · 2022Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies. STUDY DESIGN AND
settingBased on different scenarios that involved known values for coefficient of the interaction term in Cox regression models we generated 1000 samples of size 600 each. The simulated samples and a real life data set from the Cameron County Hispanic Cohort were used to evaluate the effect of ignoring statistical interactions in these models.
resultsCompared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated regression coefficients. Whereas when data were generated from a perfect additive Cox proportional hazards regression model the inclusion of the interaction between the two covariates resulted in only 2% estimated bias in main effect regression coefficients estimates, but did not alter the main findings of no significant interactions.
conclusionsWhen the effects are synergic, the failure to account for an interaction effect could lead to bias and misinterpretation of the results, and in some instances to incorrect policy decisions. Best practices in regression analysis must include identification of interactions, including for analysis of data from epidemiologic studies.
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