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By extending the TGLMM to account for missing data, potential bias in disease prevalence estimate can be adjusted and thus, bias in PPV and NPV estimates can be avoided.
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On the other hand, administrative databases usually provide larger samples and thus increased statistical power, decrease the probability of selection and recall bias, but often have missing data on potential confounders.
The problem is the immense amount of work needed, first to evaluate the need for further testing, and then to conduct tests to provide any missing data on potential hazards.
Participants with missing data on potential confounding variables were categorised in a 'missing' category.
Multiple imputation techniques [ 30, 31] were used to handle missing data on potential confounders.
** Sample without patients who was lost to follow-up (n=81) and/or had missing data on potential prognostic factors (n=47).
Within our eligible study sample of 1268, there were some missing data on potential confounders and outcome measurements (data on exposure had to be observed to be eligible).
This led to missing data, the potential effects of which we have attempted to minimise through the use of multiple imputation methods.
Interviews were conducted from 20 37 years after the births which likely contributed to errors in recall and missing data for potential risk factors and water consumption habits.
In estimation of prediction models and following post-estimation calculations, we further excluded 732 patients with missing data for potential predictors in model estimation.
Follow-up limited to 2 years, selection bias due to nonparticipation in study, missing data for potential covariates, and single noncalibrated measurements from multiple laboratories.
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