Dataset Information

Varying-coefficient models for longitudinal processes with continuous-time informative dropout.

ABSTRACT: Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administrative censoring. Specifically, we assume that the longitudinal outcome process depends on the dropout process through its model parameters. The unconditional distribution of the repeated measures is a mixture over the dropout (administrative censoring) time distribution, and the continuous dropout time distribution with administrative censoring is left completely unspecified. We use Markov chain Monte Carlo to sample from the posterior distribution of the repeated measures given the dropout (administrative censoring) times; Bayesian bootstrapping on the observed dropout (administrative censoring) times is carried out to obtain marginal covariate effects. We illustrate the proposed framework using data from a longitudinal study of depression in HIV-infected women; the strategy for sensitivity analysis on unverifiable assumption is also demonstrated.

SUBMITTER: Su L

PROVIDER: S-EPMC2800163 | biostudies-other | 2010 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Varying-coefficient models for longitudinal processes with continuous-time informative dropout.

Su Li L Hogan Joseph W JW

Biostatistics (Oxford, England) 20091015 1

Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administrative censoring. Specifically, we assume that the longitudinal outcome process depends on the dropout p ...[more]

PMID: 19837655

Similar Datasets

Project description:BackgroundDropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account for dropout and introduces a Bayesian semi-parametric varying coefficient model for exponential family longitudinal data with non-ignorable dropout.MethodsTo demonstrate these methods, we present results from a simulation study and estimate the impact of drug use on longitudinal CD4 + T cell count and viral load suppression in the Women's Interagency HIV Study. Sensitivity analyses are performed to consider the impact of model assumptions on inference. We compare results between our semi-parametric method and parametric models to account for dropout, including the conditional linear model and a parametric frailty model. We also compare results to analyses that fail to account for dropout.ResultsIn simulation studies, we show that semi-parametric methods reduce bias and mean squared error when parametric model assumptions are violated. In analyses of the Women's Interagency HIV Study data, we find important differences in estimates of changes in CD4 + T cell count over time in untreated subjects that report drug use between different models used to account for dropout. We find steeper declines over time using our semi-parametric model, which makes fewer assumptions, compared to parametric models. Failing to account for dropout or to meet parametric assumptions of models to account for dropout could lead to underestimation of the impact of hard drug use on CD4 + cell count decline in untreated subjects. In analyses of subjects that initiated highly active anti-retroviral treatment, we find that the estimated probability of viral load suppression is lower in models that account for dropout.ConclusionsNon-ignorable dropout is an important consideration when analyzing data from longitudinal clinical trials and cohort studies. While methods that account for non-ignorable dropout must make some unavoidable assumptions that cannot be verified from the observed data, many methods make additional parametric assumptions. If these assumptions are not met, inferences can be biased, making more flexible methods with minimal assumptions important.

Dataset Information

Varying-coefficient models for longitudinal processes with continuous-time informative dropout.

Publications

Varying-coefficient models for longitudinal processes with continuous-time informative dropout.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure