Title: EMcorrProbit R package
Authors: Denitsa Grigorova - Sofia University (Bulgaria) [presenting]
Nina Daskalova - Sofia University (Bulgaria)
Abstract: Correlated probit models (CPMs) are widely used for modeling of ordinal data or joint analyses of ordinal and continuous data which are common outcomes in medical studies. When we have clustered or longitudinal data CPMs with random effects are used to take into account the dependence between clustered measurements. When the dimension of the random effects is large, finding of the maximum likelihood estimates (MLEs) of the model parameters via standard numerical approximations is computationally cumbersome or in some cases impossible. EM algorithms for one ordinal longitudinal variable and for one ordinal and one continuous longitudinal variable are recently developed. The methods developed set the foundations of the EMcorrProbit R package (https://github.com/ninard/EMcorrProbit) which is going to offer also MLEs of CPM for two longitudinal ordinal variables via recently developed ECM algorithm. An application of the algorithm is presented to CPM for the longitudinal ordinal outcomes self-rated health and categorized body mass index from the Health and Retirement Study. We report results from fitting the model and also some simulation studies.