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. 2021 Mar 29;38(2):200-203.
doi: 10.1093/fampra/cmaa134.

Missing data in primary care research: importance, implications and approaches

Miguel Marino  1   2 , Jennifer Lucas  1 , Emile Latour  3 , John D Heintzman  1   4
Affiliations

Missing data in primary care research: importance, implications and approaches

Miguel Marino et al. Fam Pract. .
No abstract available

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Figures

Figure 1.
Figure 1.
Stages of multiple imputation with an illustrative example where the mean haemoglobin A1c (HbA1c) among patients is the parameter of interest and m = 3 imputations were performed. Note: Multiple imputation begins with an incomplete data set. Multiple imputed data sets are created by replacing missing values with plausible values based on a random draw from a distribution that is specifically modelled for the missing values, typically using regression models. Analysis is done on each imputed data set to estimate a parameter of interest. These estimates are pooled into one single estimate of the parameter and its variance.
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