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All outcomes will be analyzed using random effects models accounting for clustering of subjects within physicians' practices.
*Intraclass correlation within physicians: physician-level variance/total variance.
An exchangeable covariance structure was used to account for clustering of patients within physicians.
* p values linear models with a random effect to adjust for patient clustering within physicians.
*p values from logistic models with a random effect to adjust for clustering of patients within physicians.
First, because of the cross-classified structure of the data, patients may not be completely nested within physicians.
Similar(25)
Within-physician variation in prescribing decisions and the effect of physician preference have infrequently been studied.
Random (within-physician) variability will contribute to the residual variance at the patient level.
Generalized estimating equations (GEE) were employed to account for within-physician correlation (26).
The random coefficient analysis, deals with within-physician correlations by allowing different coefficients to be random.
We demonstrated two types of variations of physician decisions to discontinue treatment with TNF antagonists: between-physician and within-physician.
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