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This demonstrated that the imputed data was consistent with the measured data across all tests and the missing values derived through imputation were valid.
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Another particularity of the data is the large number of missing values, deriving from either improper filling, hospital structure, or missing values as such.
We used an imputation process to derive missing values in the PSI data.
In contrast to the Heckman and other approaches described thus far, multiple imputation methods derive missing values from existing data in the dataset, thereby creating a 'complete' dataset and eliminating the need to drop patients from analysis.
Because not all of the estimates of vaccine effectiveness required for the Jit cost-effectiveness model were explicitly evaluated and reported in the pivotal trials, Jit and colleagues employed available data from these trials to derive the missing values.
In brief, we applied an expectation-maximization procedure to fill in missing values in the time series and derived the trends from a singular value decomposition.
Socioeconomic position exhibited more missing values than other variables since it was derived from ten other variables.
Note that questionnaire derived data may have missing values; a Physical Activity Scale for the Elderly (range 0 (non active) - 400 (very active)); * p for association < 0.05.
The variable with the greatest proportion of missing values was the index of multiple deprivation, which is derived from the patient's post code and was missing in 6.9% of cases.
Table 2 shows that data derived from hospital records (97,809) had low percentage of missing values for the key variables.
We assumed that missing values occurred at random given the other covariates and used multiple imputation to derive predictions for missing values.[22] The extent of completeness of data for various predictor variables is represented in Table 1.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com