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We investigated capability for dealing with missing data under different circumstances by various methods.
The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce.
We felt that there were too many cases with missing data about the exact circumstances to create meaningful estimates of the prevalence of many behaviors (e.g., playing quick draw; thought the gun was unloaded).
Experimental results have demonstrated that XHSD is a reliable method for xor-haplotyping under all circumstances including missing data and typing error cases.
A corollary is that MI would not perform well in circumstances where the pattern of missing data is not MAR or MCAR (a common situation in applied research).
9 10 In these circumstances, specialist methods to address missing data may lessen the loss of precision and power resulting from exclusion of individuals with incomplete predictor variables but are not required in order to avoid bias.
Similarly, the ability to look into the important role of locums [ 9] was hampered by large amounts of missing data where survey questions did not apply to the respondents' circumstances.
The occurrence of missing data in the other 9 patients was less than 5%, and in these circumstances the last value was carried forward [ 21].
Under these circumstances, we might expect MI+ with the model used to impute missing data, including the outcome of interest to provide the least biased parameter estimates, with a potential for bias for estimates based on CCA, MS and MI−.
Days with missing data of several hours (for example when the participant forgot the Actiwatch or for unexpected circumstances such as admission to hospital for a surgical procedure) were excluded from further analysis.
In circumstances where a peptide was identified in some LC-MS analyses, but not others, the missing data were coded as 'NaN'.
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