One should understand that the interpretation of LOR estimate from a marginal model is of a population-average, while that of a mixed model is a conditional-average. Therefore there is a slight difference in their meaning. Expanding the proportional odds ratio model One may use the frameworks ...
5. SAS code for performing likelihood ratio test in a 2 × 2 × 2 contingency tables data binomial; input center treat y n; * y successes out of n trials; cards; 1 1 30 100 1 2 50 100 2 1 45 100 2 2 75 100 ; run; ...
These 're-samples' are fed back into the logistic regression and bootstrap' estimates of confidence intervals for the model parameters are made by examining the model parameters calculated at each cycle of the process. The bias statistic shows how much each mean model parameter from the boot...
These 're-samples' are fed back into the logistic regression and bootstrap' estimates of confidence intervals for the model parameters are made by examining the model parameters calculated at each cycle of the process. The bias statistic shows how much each mean model parameter from the boot...
5. SAS code for performing likelihood ratio test in a 2 × 2 × 2 contingency tables data binomial; input center treat y n; * y successes out of n trials; cards; 1 1 30 100 1 2 50 100 2 1 45 100 2 2 75 100 ; run; ...
One should understand that the interpretation of LOR estimate from a marginal model is of a population-average, while that of a mixed model is a conditional-average. Therefore there is a slight difference in their meaning. Expanding the proportional odds ratio model One may use the frameworks ...