Reference‐Based Multiple Imputation for Longitudinal Binary Data

This paper formulates and describes two algorithms for implementing reference-based multiple imputation for longitudinal binary outcome data using: (i) joint modeling with the multivariate normal distribution and an adaptive rounding algorithm and (ii) joint modeling with a latent multivariate normal model. A simulation study was performed to compare the properties of the two methods.