Fixed effect model
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Fixed effect(s) model is a term often used in hierarchical linear modeling. It is an opposite of a random effect model. Fixed effect model assumes that the data come from normal populations which differ in their means. It answers the question whether the studies included in the meta-analysis show that the treatment or exposure produced the effect on average.
In a fixed effect model the unit of analysis are the ones of interest, and thus constitute the entire population of units. Only within-study variation is taken to influence the uncertainty of results (as reflected in the confidence interval) of a meta-analysis using a fixed effect model. Variation between the estimates of effect from each study (heterogeneity) does not effect the confidence interval in a fixed effect model.
Methods often used with fixed effect model:
- Mantel Haenszel method
- Peto's method
- General variance-based method
[edit] References
- Fixed effect model at Bandolier (Oxford EBM website)
- Fixed and random effects models
- Distinguishing Between Random and Fixed: Variables, Effects, and Coefficients
- How to Conduct a Meta-Analysis: Fixed and Random Effect Models
- Fixed Effects (in ANOVA)
[edit] See also
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