Consequently, the conditional effect of marital status on the probability of disability, as obtained from the fixed-effects approach, becomes statistically significant at each of the six time points, thereby yielding incorrect conclusions in the significance tests on the conditional effect. As indicated earlier, such tremendous bias in the dispersion statistic originates from neglect of variability in the predicted probability of disability. The fixed-effects logit model underestimates the absolute values of the conditional effects and overestimates the Wald statistics to an exceptional extent. The empirical BLUP generates close conditional effects to those of the first two approaches, but in early time points, the Wald statistic is notably underestimated. Therefore, those two approaches result in the same conclusion concerning the effect of marital status on the probability of disability and its statistical significance. The retransformation method, after removal of variable EDUC_MEAN, produces the closest set of the conditional effects to those of the full random intercept logit model, particularly the Wald chi-square statistics. The conditional effects of marital status on the probability of disability, however, are not statistically significant except at the second (TIME = 1) and the third (TIME = 2) time points, evaluated by the Wald statistics for the full random intercept logit model.
![time point measure time point measure](https://www.playableguitar.com/images/small-length-hand.png)
Table 10.2 displays that those currently married are less likely to be disabled than their unmarried counterparts.
![time point measure time point measure](https://pbs.twimg.com/media/DkI696gXoAI2FjW.jpg)
Corresponding to severe underestimation of the standard errors, all the fixed-effects predictions on the probability of disability are very strongly statistically significant, which is obviously incorrect and yielding misleading test results.Ĭonditional Effect of Marital Status Generated From Full ModelĬonditional Effect of Marital Status Generated From Retransformation ApproachĬonditional Effect of Marital Status Generated From Empirical BLUPĬonditional Effect of Gender Generated From the Fixed-Effects Approach More significantly, the fixed-effects approach severely underestimates the standard errors, much more so than the empirical BLUP. The fixed-effects logit model generates the least reliable predictions as compared to those from the random-intercept logit perspectives. Furthermore, the BLUP method, only retransforming a portion of variability, results in strong underestimation of the standard errors for nonlinear predictions. The empirical BLUP provides fairly close predictions at the early time points in the last four time points, the predicted probability of disability deviates considerably from those of the first two approaches. At the next four time points, the predicted probability of disability is associated with low standard errors. In the first two panels, the predicted probability of disability at the first two time points, the 19 follow-ups, are not statistically significant at α = 0.05, with the high standard errors relative to the point estimates. These results highlight high efficiency and coverage of the retransformation method in handling retransformation bias in nonlinear predictions. The retransformation method derives the closest set of predictions to those from the full random intercept logit model, both the point estimates and the standard error approximates. In Table 10.2, the probability of disability is shown to increase consistently over time at the early and the middle stages, and then the increase slows down near the end of the observation period, reflecting a selection of the fittest process.
![time point measure time point measure](https://venturebeat.com/wp-content/uploads/2020/04/a1.png)
Predicted Probability Generated From the Fixed-Effects Approach Predicted Probability Generated From BLUP Predicted Probability Generated From the Retransformation Approach Predicted Probability Generated From the Full Model