A nonparametric multiple imputation approach for missing categorical data.

ABSTRACT:

Background

Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities.

Methods

We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for pred

SUBMITTER: Zhou M 

PROVIDER: S-EPMC5461637 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature