fixed-effect model

fixed-effect model

A statistical model that stipulates that the units being analysed—e.g. people in a trial or studies in a meta-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 affect the confidence interval in a fixed-effect model (Cochrane definition).
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The sensitivity analysis was performed by changing the effect model (random-effect to fixed-effect model) and comparing the results of different effect model.
Next, Columns (7) through (9) report results from the application of fixed-effect model, Columns (10) through (12) provides a similar report from the estimation of a random-effect model, and, finally, the last three columns do the same job for a random-coefficient model, where coefficient randomness is assumed to apply only to the coefficient of our concern, the level of corruption (cp i).
We assessed potential heterogeneity of effect estimates within each group using I [sup]2 test, where I [sup]2 (%) >50% and P < 0.10 was considered significantly heterogeneous, then random effect model was applied to combine results from different trials, otherwise, fixed-effect model was used.
When [I.sup.2] is less than 50% and p>0.10, the results were considered homogeneous and the fixed-effect model was used; when [I.sup.2] is greater than 50% and less than 75%, results were considered heterogeneous and the random-effect model was used.
The fixed-effect model eliminates all cross-county variations, including any unobserved ones.
The fixed-effect model is preferred under such circumstance (Hsiao, 2003).
A fixed-effect model was used first, the Q test and I2 statistic was performed to assess the heterogeneity, and P < 0.1 or I2 > 50% was considered as heterogeneity between studies.
This result supports the preference to fixed-effect model rather than pool model.
Both tables show the results of (i) the pooled panel model regression, (ii) the fixed-effect model, and (iii) the random- effect model.
Data was analyzed with a fixed-effect model. These results were input into a simulation model, the Evidence Based Medicine Integrator, in order to estimate their long-term implications in a real-world population from Kaiser Permanente (CA, USA).

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