Comments on: Missing data methods in longitudinal studies: a review

Incomplete data are quite common in biomedical and other types of research, especially in longitudinal studies. During the last three decades, a vast amount of work has been done in the area. This has led, on the one hand, to a rich taxonomy of missing-data concepts, issues, and methods and, on the other hand, to a variety of data-analytic tools. Elements of taxonomy include: missing data patterns, mechanisms, and modeling frameworks; inferential paradigms; and sensitivity analysis frameworks. These are described in detail. A variety of concrete modeling devices is presented. To make matters concrete, two case studies are considered. The first one concerns quality of life among breast cancer patients, while the second one examines data from the Muscatine children’s obesity study

Sensitivity Analysis in Correlated Bivariate Continuous and Binary Responses

Factorization models for correlated binary and continuous responses are proposed. Full likelihood-based approach that yields maximum likelihood estimates of the model parameters is used. A common way to investigate if perturbations of model components influence key results of the analysis is to compare the results derived from the original and perturbed models using an influence graph. So small perturbation influence of the correlation parameters of the models on likelihood displacement and a general index of sensitivity (ISNI) are also studied. The model is illustrated using data from arthritis and body mass index data. The effect of systolic blood pressure, gender and age on arthritis and body mass index are investigated

Regression Models for Mixed Over-Dispersed Poisson and Continuous Clustered Data: Modeling BMI and Number of Cigarettes Smoked Per Day

Clustered data, multiple observations collected on the same experimental unit, is common in epidemiological studies. Bivariate outcome data is often the result of interest in two correlated response variables. An efficient method is presented for dealing with bivariate outcomes when one outcome is continuous and the other is a count using a simple transformation to handle over-dispersed Poisson data. A multilevel analysis was performed on data from the National Health Interview Survey (NHIS) with body mass index (BMI) and the number of cigarettes smoked per day (NCS) as responses. Results show that these random effects models yield misleading results in cases where the data is not transformed

A Bayesian model for longitudinal count data with non-ignorable dropout

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73907/1/j.1467-9876.2008.00628.x.pd

Gaussian Copula Mixed Models with Non-Ignorable Missing Outcomes

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

Estimating healthcare demand for an aging population: a flexible and robust bayesian joint model

In this paper, we analyse two frequently used measures of the demand for health care, namely hospital visits and out-of-pocket health care expenditure, which have been analysed separately in the existing literature. Given that these two measures of healthcare demand are highly likely to be closely correlated, we propose a framework to jointly model hospital visits and out-of-pocket medical expenditure. Furthermore, the joint framework allows for the presence of non-linear effects of covariates using splines to capture the effects of aging on healthcare demand. Sample heterogeneity is modelled robustly with the random effects following Dirichlet process priors with explicit cross-part correlation. The findings of our empirical analysis of the U.S. Health and Retirement Survey indicate that the demand for healthcare varies with age and gender and exhibits significant cross-part correlation that provides a rich understanding of how aging affects health care demand, which is of particular policy relevance in the context of an aging population