Will the method of handling missing data lead to different conclusions?
Thus, including markers with larger amounts of missing data led to improved GS accuracies.
Like data noise, missing data led to increased inaccuracy of the parameter estimates, regardless of the estimation methods.
For some mRNA targets, the pattern of missing data led to exclusion of subject pairs from the statistical analyses, resulting in exclusion of the following number of subject pairs; IL1β = 4; IGF-IEc = 1; IL1R = 1; cmet = 1.
The following missing data led to exclusions: ever having been pregnant was missing for 183 women, information regarding marital status, a key determinant of childbearing, was missing for 1,102 women; an additional 4,727 women had missing information regarding BMI at age 20.
No one diet variable (i.e., fruit and veg, breakfast, fizzy drink consumption) at either time point was missing from more than 15% of the sample, however, the overall pattern of missing data led to a substantial reduction in sample size.
3 4 Missing data lead to a reduction of power, compromise the benefits of randomisation and are a potential source of bias.
The full information maximum likelihood (FIML) procedure included in Mplus was used to deal with missing data, which leads to a more reliable and less biased estimation than other methods (Schafer and Graham 2002).
Intensity threshold based algorithms utilize simple analysis along the depth scan's intensity profiles or find borders by investigating intensity gradients and are frequently confused by missing data, which leads to non-physiologic results [ 8– 15].
Lemmon et al. (2009) found that missing data could lead to biases for both maximum likelihood and Bayesian inference.
