TY - JOUR
T1 - Regressive models for risk prediction of repeated multinomial outcomes: An illustration using Health and Retirement Study data
JF - Biometrical Journal
Y1 - 2020
A1 - Rafiqul I Chowdhury
A1 - M. Ataharul Islam
KW - multinomial outcomes
KW - regressive model
KW - repeated measures
KW - risk prediction
KW - sequence of events
AB - Abstract Life expectancy is increasing in many countries and this may lead to a higher frequency of adverse health outcomes. Therefore, there is a growing demand for predicting the risk of a sequence of events based on specified factors from repeated outcomes. We proposed regressive models and a framework to predict the joint probabilities of a sequence of events for multinomial outcomes from longitudinal studies. The Markov chain is used to link marginal and sequence of conditional probabilities to predict the joint probability. Marginal and sequence of conditional probabilities are estimated using marginal and regressive models. An application is shown using the Health and Retirement Study data. The bias of parameter estimates for all models from all bootstrap simulation is less than 1% in most of the cases. The estimated mean squared error is also very low. Results from the simulation study show negligible bias and the usefulness of the proposed model. The proposed model and framework would be useful to solve real-life problems from various fields and big data analysis.
VL - n/a
UR - https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201800101
ER -