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However, the kinship analysis for missing persons identifications showed variable results among the participants.
Sensitivity analysis for missing outcome data did not change this effect estimate (Additional file 1: Table S6d), and no evidence of heterogeneity was observed (I 2 = 0%).
Sensitivity analysis for missing outcome data did not change this effect estimate or substantially reduce the degree of heterogeneity (Additional file 1: Table S6e).
We also carried out a sensitivity analysis for missing data.
Similar computations for trials that report sensitivity analysis for missing data will also be performed.
Our conclusions were unaffected by the sensitivity analysis for missing data.
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This analysis allows for missing data at different time points and is considered as the optimal way for evaluation of longitudinal data.
Extreme sensitivity analysis, accounting for missing primary outcome data, identified consistently that the PACT intervention protected against the occurrence of any error (table 4).
Moustakas, A. and Evans, M. R. (2015) Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values.
Extreme sensitivity analysis accounted for missing primary outcome data and the findings were consistent at each extreme, demonstrating the positive effect of PACT to prevent error.
We performed per-protocol analysis, analysis with baseline values for missing data and analysis with multiple imputation for missing data.
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