Performance of bias adjustments was evaluated by calculating the percent root mean-squared bias (RMSB), which is analogous to the root mean-squared error except that our analysis focuses on bias and does not include a term for precision.
The hope is that the total mean squared error (bias squared + variance) is lower than uncorrected estimate.
The paper compares six smoothers, in terms of mean squared error and bias, when there are multiple predictors and the sample size is relatively small.
Operating characteristics collected included type I error rate, coverage, median bias, mean squared error, and median width.
Through simulation, we compare the proposed models to standard logistic models in terms of bias, mean squared error, coverage probability, and power.
Through simulation, we have shown that the proposed models outperform standard logistic models in terms of bias, mean squared error, coverage probability, and power under a range of treatment effect and visit process scenarios designed to reflect a PMTCT setting.
We compared the effects of treatment as obtained from the regression models to the true effects of treatment according to which the data were generated, determining bias, mean squared error (MSE), 95percentt coverage probability (CP), and power for each estimator.
For a specific example of a three-stage drop-the-losers trial, we compare their performance against several alternative estimators in terms of bias, mean squared error, confidence interval width and coverage.
For continuous outcomes, using LMEM is recommended in comparison to either ANOVA or GEE, taking into consideration the following operating characteristics: median bias, mean squared error, coverage, and median width.
Authors of previous studies examining the influence of caliper width have based the choice of caliper solely on mean squared error, which combines bias and precision in a single number (10, 11).
For continuous outcomes, while the coverage never fell below 0.93, and the type I error rate never exceeded 0.07 for any method, overall linear mixed-effects models performed well with respect to median bias, mean squared error, coverage, and median width.
