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Given the strong positive effect of ST on treatment retention found in earlier studies [ 37- 39], dropout is analyzed with both multilevel logistic regression and survival analysis (to account for development over time).
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We compared the SE/Coefficient ratios from both single-level and multilevel logistic regression to check for possible bias in those from the single-level model (erroneously small SE).
Method: We will use multilevel logistic regression models to check our hypotheses.
Associations of these POS measures with walking and depression were examined using adjusted multilevel logistic regression models.
We applied multilevel logistic regression techniques to obtain odds ratios (OR), variance partition coefficients (VPC) and 95% credible intervals (CI).
Multilevel logistic regression models used SAS GLIMMIX.
Multilevel logistic regression models were fit using SAS Proc Glimmix to account for clustering of individual observations by county.
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Multilevel logistic regression models were used to analyze the data from 723 households from wave 2 of the Los Angeles Family and Neighborhood Survey.
Using a multilevel logistic regression model, we evaluated patient and hospital characteristics, as well as geographic availability, in relation to discharge to either IRF or SNF.
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