The contribution of the non-missing covariates to the distribution of missing values is based on modelling the non-missing co-variates in complete cases to predict the likely value of the variable in records where it is missing.
The missing data pattern showed an equal distribution of missing values.
This work also considers three different mechanisms governing the distribution of missing values in a dataset, and examines the impact of noise on the imputation process.
The empirical results show that using incomplete cases often increases the effectiveness of nearest neighbor imputation (especially at higher missingness levels), regardless of the type of missingness (i.e., the distribution of missing values in the data).
The impacts of various designing factors are appropriately accounted for in the sample size formula, including intervention effect, baseline response rate, within-subject correlation, and distribution of missing values in the before- and after-intervention periods.
Given the probable non-random distribution of missing data, imputation was not performed.
It is typically difficult to calculate the distribution of missing data conditioned on the observed ones.
Although there are remaining issues that need further study, for instance, the upper error bound by the number and distribution of missing samples, the proposed method shows very promising results, that is, capable of signal recovery from randomly spaced subsamples and continuous lost samples.
The distribution of missing values was balanced across treatment groups.
Patients with missing data on time on ART did not differ significantly in terms of gender, age or ethnicity compared to other patients in the study, indicating a random distribution of missing values.
Due to the scarcity of data on megafaunal fruits and the irregular distribution of missing values we resorted to these binary contrasts to partially control the patterns of phylogenetic relatedness (see [6], [121] for a similar approach).
