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However, this MI approach still produced biased regression coefficient estimates with 75% missingness.
CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness.
However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism.
In this simulation study, performing a CC analysis with any multivariate missingness produced less biased regression coefficient estimates with better coverage rates than using SI or MI.
For the highly skewed continuous covariates X2, X3 and X4, the least biased regression coefficient estimates were produced when MI was performed using MICE-PMM without transformations.
Previous evidence [ 2, 3] suggested that imposing univariate MAR missingness associated with outcome would result in biased regression coefficient estimates when using CC analysis.
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This approach could have resulted in heavily biased regression coefficients (Skrondal and Laake 2001).
Given the presence of spatial autocorrelation, which could have biased regression coefficients and overall model fit, we implemented the SAR technique to account for such phenomenon.
Note that our method gives biased regression coefficients.
To account for non-response bias, regression coefficients were estimated using the maximum likelihood method.
The appearance of the Deeks' funnel plot for ROMA on EOC detection was symmetrical (Additional file 2: Figure S1), and the funnel plot asymmetry test showed little sign of publication bias (regression coefficients was −3.73; p = 0.617).
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