Random effects modelling versus logistic regression for the inclusion of cluster-level covariates in propensity score estimation: a monte carlo simulation and registry cohort analysis

HIGHLIGHTS

  • who: . et al. from the Montreal University, Canada have published the research: Random effects modelling versus logistic regression for the inclusion of cluster-level covariates in propensity score estimation: A Monte Carlo simulation and registry cohort analysis, in the Journal: (JOURNAL)
  • how: The authors studied six different PS estimation strategies for clustered data using random effects modelling (REM) compared with logistic regression. The following PS estimation strategies were compared i) logistic regression omitting cluster-level confounders ii) logistic regression including cluster-level confounders iii) the same as ii) but including cross-level interactions iv) v . . .

     

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