Semi-parametric Bayesian analysis of binary responses with a continuous covariate subject to non-random missingness

Semi-parametric Bayesian analysis of binary responses with a continuous covariate subject to non-random missingness

Missingness in explanatory variables requires a model for the covariates even if the interest lies only in a conditional model for the outcomes given the covariates. An incorrect specification of the models for the covariates or for the missingness mechanism may lead to biased inferences for the parameters of interest. Previously published articles either use semi-/non-parametric flexible distr...

Estimation Methods with Ordered Covariate Subject to Measurement Error and Missingness in Semi-Ecological Design

Bayesian Determination of Sample Size in Longitudinal Studies with Binary Responses Using Random Effects Models

Sample size determination is important in all statistical studies including longitudinal studies. This is usually done by considering a target power to reduce the costs of sampling. Choosing the right sample size using efficient methods, ensures that the researcher achieve goal of the study, by spending the least amount of energy, time and money. In this article, using a method based on simulat...

Semi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses

Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...

Pseudo-likelihood Methods for the Analysis of Longitudinal Binary Data Subject to Nonignorable Non-monotone Missingness

For longitudinal binary data with non-monotone non-ignorable missing outcomes over time, a full likelihood approach is complicated algebraically, and maximum likelihood estimation can be computationally prohibitive with many times of follow-up. We propose pseudo-likelihoods to estimate the covariate effects on the marginal probabilities of the outcomes, in addition to the association parameters...