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The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20 %, 30 %, 40 %, 50 %, 60 %, 70 %, 8090%and90%ssing data cofpared to observed ROmissinges, with 50 replicates.
This approach minimized the possibility of missing data, compared with the using of cDNA/EST database or the assembled genomic database.
For these variables, the case fatality rate was the same or lower in patients with missing data compared to patients with documented results, supporting our assumption that missing variables were likely within the normal range [data not shown].
The data contain a higher fraction of missing data compared to the small data set.
Pinus assemblies averaged just less than 120 kb (6% estimated missing data, compared to P. thunbergii reference).
Outgroup assemblies averaged just over 119 kb (10.4% average estimated missing data compared to P. thunbergii reference).
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The validity was assessed by measuring levels of missing data, comparing symptom scores with objective prolapse stages and between affected and asymptomatic women.
Logistic regression analysis of missing data, comparing those who were included in the final sample with those who were excluded because of missing data in one or more variables.
This approach was taken to ease the computational burden (compared with using all 48 individual ordinal responses) and to permit the use of maximum likelihood (ML) estimation, with its more realistic missing data assumptions compared to a least-squares estimator.
Participants with missing data were compared with the rest of the cohort.
All cases with missing data were compared to cases with complete data to evaluate whether missing data biased our estimations.
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