This paper is concerned with the analysis of mixed data with ordinal and continuous outcomes with the possibility of non - ignorable missing outcomes . A copula-based regression model is proposed that accounts for associations between ordinal and continuous outcomes . Our approach entails specifying underlying latent variables for the mixed outcomes to indicate the latent mechanisms which generate the ordinal and continuous variables . Maximum likelihood estimation of our model parameters is implemented using standard software such as function nlminb in R . Results of simulations concern the relative biases of parameter estimates of joint and marginal models using data with non-ignorable outcomes . The proposed methodology is illustrated using a medical data obtained from an observational study on women with three correlated responses , an ordinal response of osteoporosis of the spine and two continuous responses of body mass index and waistline . The effect of the amount of total body calcium (Ca) , job status (Job) , type of dwelling (Ta) and age on all responses are investigated simultaneously .
Jafari, N.; Tabrizi, E.; and Samani, E. B.
Gaussian Copula Mixed Models with Non-Ignorable Missing Outcomes,
Applications and Applied Mathematics: An International Journal (AAM), Vol. 10,
1, Article 6.
Available at: https://digitalcommons.pvamu.edu/aam/vol10/iss1/6