Using these three matrices, we evaluated how different approaches reducing missing data influence our resulting topology and if the selected taxa and genes based on these different approaches have an influence on the inferred phylogeny.
For example, Molitor et al. (2006) documented the effect of missing exposure data at the individual level and determined that the methodology chosen to account for this missing data influences the conclusions regarding the observed health effects.
However, it is not likely that the missing data have influenced the major conclusions.
The presence of missing data could influence the interpretation of the results.
Our previous report on the changes of muscle strength in the same population suggested that differential follow-up rate or nonrandom missing data might influence the results biased to the null (6).
The way we handled missing data might have influenced the estimates or inflated the effects for variables with only few data points and long periods of interpolation (Supplementary Appendix pp. 16 18).
In our population, we did not observe any association with the explored parameters; however, the relevant proportion of missing data could have influenced such finding masking the real effects in terms of increased/reduced hazard of adverse events occurrence.
Although analyses were conducted using pairwise complete data, results from analyses repeated on a subset of the study population with complete data indicate that missing data did not influence our results.
Fourth, we have missing data, which might have influenced the results.
Job satisfaction and the perception of management had α values of 0.62 and 0.64, respectively, which are slightly below the recommended acceptable α value limit of 0.70 because of missing data that may have influenced the result.
