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Multiple imputation of dental caries data using a zero‐inflated Poisson regression model
Author(s) -
Pahel Bhavna T.,
Preisser John S.,
Stearns Sally C.,
Rozier R. Gary
Publication year - 2010
Publication title -
journal of public health dentistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 63
eISSN - 1752-7325
pISSN - 0022-4006
DOI - 10.1111/j.1752-7325.2010.00197.x
Subject(s) - imputation (statistics) , poisson regression , missing data , statistics , poisson distribution , regression , mathematics , regression analysis , zero inflated model , count data , linear regression , dentistry , medicine , population , environmental health
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 nonmissing caries data in their distribution, especially with respect to the excess zeros in the nonmissing caries data. Conclusion: This technique can be easily applied to impute missing dental caries data.