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However, we systematically excluded from the analysis patients with unreliable or missing data.
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Removal of SPPs in duplicated regions with inconsistent data or missing data (see Methods) was a reasonable method of removing unreliable data as these data may be from poorly performing probes in one or all replicates, heterozygous loci, paralogous genes or deleted genes.
This approach suffers from activity gaps or missing data.
Invalid or missing data were excluded.
Roughly 10% of the full sample was excluded from the analyses due to unreliable or missing information.
Exclusion criteria were: 1) health maintenance organization (HMO) enrollment during the 12 months prior to and including the month of diagnosis since HMO claims can be unreliable due to missing data; 2) history of other cancers within 5 years prior to PCa diagnosis.
2) The loci which have 30% (user defined parameter) or more missing data from the hybrids were filtered out as they represent data from unreliable regions such as repeats and 3) The loci where hybrids have more than 2 alleles (user defined parameter), also called multi-allelic loci were filtered out as they represent sequencing errors or population based artifacts.
In smaller cohorts and in case control studies, risk estimates based on classic frequentist modeling are likely to produce unreliable estimates in small strata, and any likelihood-based analysis for small data, or even worse, missing data, usually involves computationally intensive methods or ad hoc adjustments.
This increases the chance for missing data or unreliable data.
At the end of the regression and k-means processes, each of the filter bank outputs is divided into one of the two classes, indicating the reliable and unreliable components similar to the missing data method as mentioned in Section 1.
A number of different types of artifact were observed in our data, such as brief periods where estimates in one dimension become unreliable, and occasional periods of missing data preceded by egregious artifact.
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