Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on an...Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on analyzing such data is well documented. Dependence between observations from the same study subject in correlated data renders invalid the usual chi-square tests of independence and inflates the variance ofparameter estimates. Disaggregated approaches such as hierarchical linear models which are able to adjust for individual level covariate：s are favoured in the analysis of such data, thereby gaining power over aggregated and individual-level analyses. In this article the authors, therefore, address the issue of analyzing correlated data with dichotomous end-points by using hierarchical logistic regression, a generalization of the standard logistic regression model for independent outcomes.展开更多
文摘Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on analyzing such data is well documented. Dependence between observations from the same study subject in correlated data renders invalid the usual chi-square tests of independence and inflates the variance ofparameter estimates. Disaggregated approaches such as hierarchical linear models which are able to adjust for individual level covariate：s are favoured in the analysis of such data, thereby gaining power over aggregated and individual-level analyses. In this article the authors, therefore, address the issue of analyzing correlated data with dichotomous end-points by using hierarchical logistic regression, a generalization of the standard logistic regression model for independent outcomes.
