Multiple imputation for gamma outcome variable using generalized linear model

ABSTRACT

We used a proper multiple imputation (MI) through Gibbs sampling approach to impute missing values of a gamma distributed outcome variable which were missing at random, using generalized linear model (GLM) with identity link function. The missing values of the outcome variable were multiply imputed using GLM and then the complete data sets obtained after MI were analysed through GLM again for the estimation purpose. We examined the performance of the proposed technique through a simulation study with the data sets having four moderate and large proportions of missing values, 10%, 20%, 30% and 50%. We also applied this technique on a real life data and compared the results with those obtained by applying GLM only on observed cases. The results showed that the proposed technique gave better results for moderate proportions of missing values.

KEYWORDS:

• Multiple imputation
• generalized linear model
• gamma distribution
• missing data
• identity link function

Acknowledgements

We are thankful to Mr Anand Kumar, a freelance data analyst for the useful discussion on the program code used for multiple imputation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by grant to the second author under research and development programof University of Delhi, India.