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Hierarchical mixture models for longitudinal immunologic data with heterogeneity, non-normality, and missingness.

Abstract：

:It is a common practice to analyze longitudinal data frequently arisen in medical studies using various mixed-effects models in the literature. However, the following issues may standout in longitudinal data analysis: (i) In clinical practice, the profile of each subject's response from a longitudinal study may follow a "broken stick" like trajectory, indicating multiple phases of increase, decline and/or stable in response. Such multiple phases (with changepoints) may be an important indicator to help quantify treatment effect and improve management of patient care. To estimate changepoints, the various mixed-effects models become a challenge due to complicated structures of model formulations; (ii) an assumption of homogeneous population for models may be unrealistically obscuring important features of between-subject and within-subject variations; (iii) normality assumption for model errors may not always give robust and reliable results, in particular, if the data exhibit non-normality; and (iv) the response may be missing and the missingness may be non-ignorable. In the literature, there has been considerable interest in accommodating heterogeneity, non-normality or missingness in such models. However, there has been relatively little work concerning all of these features simultaneously. There is a need to fill up this gap as longitudinal data do often have these characteristics. In this article, our objectives are to study simultaneous impact of these data features by developing a Bayesian mixture modeling approach-based Finite Mixture of Changepoint (piecewise) Mixed-Effects (FMCME) models with skew distributions, allowing estimates of both model parameters and class membership probabilities at population and individual levels. Simulation studies are conducted to assess the performance of the proposed method, and an AIDS clinical data example is analyzed to demonstrate the proposed methodologies and to compare modeling results of potential mixture models under different scenarios.

journal_name

Stat Methods Med Res

journal_title

Statistical methods in medical research

authors

Huang Y,Chen J,Yin P

doi

10.1177/0962280214544207

subject

Has Abstract

pub_date

2017-02-01 00:00:00

pages

223-247

issue

eissn

0962-2802

issn

1477-0334

pii

0962280214544207

journal_volume

pub_type

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Hierarchical mixture models for longitudinal immunologic data with heterogeneity, non-normality, and missingness.