Multiple imputation of dental caries data using a zero-inflated Poisson regression model.
Excess zeros exhibited by dental caries data require special attention when multiple imputation is applied to such data.


OBJECTIVE
The objective of this study was to demonstrate a simple technique using a zero-inflated Poisson (ZIP) regression model, to perform multiple imputation for missing caries data.


METHODS
The technique is demonstrated using data (n = 24,403) from a medical office-based preventive dental program in North Carolina, where 27.2 percent of children (n = 6,637) were missing information on physician-identified count of carious teeth. We first estimate a ZIP regression model using the nonmissing caries data (n = 17,766). The coefficients from the ZIP model are then used to predict the missing caries data.


RESULTS
This technique results in imputed caries counts that are similar to the non-missing caries data in their distribution, especially with respect to the excess zeros in the nonmissing caries data.


CONCLUSIONS
This technique can be easily applied to impute missing dental caries data.
Pahel
Bhavna T
BT
School of Dentistry, The University of North Carolina at Chapel Hill, 27599-7450, USA. bhavna_pahel@unc.edu
Preisser
John S
JS
Stearns
Sally C
SC
Rozier
R Gary
RG
D000367:Age Factors; D000465:Algorithms; D015982:Bias; D002648:Child; D003627:Data Interpretation, Statistical; D003731:Dental Caries / Q000453:epidemiology*; D005544:Forecasting; D006801:Humans; D008507:Medically Underserved Area; D015233:Models, Statistical; D009657:North Carolina / Q000453:epidemiology; D016012:Poisson Distribution; D012044:Regression Analysis