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These authors called high-quality SNPs displaying only a small percentage of missing data, whereas many applications of GBS [ 16] have tolerated SNPs with very high frequencies of missing data, sometimes up to 80%.
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Problems caused by missing data can sometimes be mitigated with statistical techniques [ 12].
Although these analyses identified that missing data was sometimes associated with covariates included in the corresponding "reduced model," there was little evidence that this was independently associated with the most recent previous outcome value (P values ranged from 0.14 to 0.98).
In particular, problems with compliance with the numerous repeated assessments common to many EMA protocols raises concerns about the preponderance of missing data that these methods sometimes produce.
Missing data in statistical analysis can sometimes be problematic, particularly if they are associated with the outcome of interest.
The category "sometimes" was handled as missing data.
"One of those challenges is the timeliness of the report, in the sense that sometimes there are partly missing data.
Follow-up phone calls are sometimes needed to obtain missing data or to clarify ambiguous responses in the high-quality research data applications.
Routine data sources will also include missing data and measurements taken with varying periodicity, sometimes involving key variables that may be important as covariates.
Sometimes observed-cluster inference may be of interest even when missing data are potentially observable.
The presence of missing data is a common issue in medical research, leading to reduced precision and sometimes bias in parameter estimates.
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