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Longitudinal Binary Data (longitudinal + binary_data)

Distribution by Scientific Domains
Mathematics and Statistics100%

Selected Abstracts

A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros

BIOMETRICS, Issue 2 2008
Dimitris Rizopoulos
Summary Many longitudinal studies generate both the time to some event of interest and repeated measures data. This article is motivated by a study on patients with a renal allograft, in which interest lies in the association between longitudinal proteinuria (a dichotomous variable) measurements and the time to renal graft failure. An interesting feature of the sample at hand is that nearly half of the patients were never tested positive for proteinuria (,1g/day) during follow-up, which introduces a degenerate part in the random-effects density for the longitudinal process. In this article we propose a two-part shared parameter model framework that effectively takes this feature into account, and we investigate sensitivity to the various dependence structures used to describe the association between the longitudinal measurements of proteinuria and the time to renal graft failure. [source]

Marginal Analysis of Incomplete Longitudinal Binary Data: A Cautionary Note on LOCF Imputation

BIOMETRICS, Issue 3 2004
Richard J. Cook
Summary In recent years there has been considerable research devoted to the development of methods for the analysis of incomplete data in longitudinal studies. Despite these advances, the methods used in practice have changed relatively little, particularly in the reporting of pharmaceutical trials. In this setting, perhaps the most widely adopted strategy for dealing with incomplete longitudinal data is imputation by the "last observation carried forward" (LOCF) approach, in which values for missing responses are imputed using observations from the most recently completed assessment. We examine the asymptotic and empirical bias, the empirical type I error rate, and the empirical coverage probability associated with estimators and tests of treatment effect based on the LOCF imputation strategy. We consider a setting involving longitudinal binary data with longitudinal analyses based on generalized estimating equations, and an analysis based simply on the response at the end of the scheduled follow-up. We find that for both of these approaches, imputation by LOCF can lead to substantial biases in estimators of treatment effects, the type I error rates of associated tests can be greatly inflated, and the coverage probability can be far from the nominal level. Alternative analyses based on all available data lead to estimators with comparatively small bias, and inverse probability weighted analyses yield consistent estimators subject to correct specification of the missing data process. We illustrate the differences between various methods of dealing with drop-outs using data from a study of smoking behavior. [source]

Assessment of short-term association between health outcomes and ozone concentrations using a Markov regression model

ENVIRONMETRICS, Issue 3 2003
Abdelkrim Zeghnoun
Abstract Longitudinal binary data are often used in panel studies where short-term associations between air pollutants and respiratory health outcomes are investigated. A Markov regression model in which the transition probabilities depend on the covariates, as well as the past responses, was used to study the short-term association between daily ozone (O3) concentrations and respiratory health outcomes in a panel of schoolchildren in Armentières, Northern France. The results suggest that there was a small but statistically significant association between O3 and children's cough episodes. A 10,,g/m3 increase in O3 concentrations was associated with a 13.9,% increase in cough symptoms (CI,95%,=,1.2,28.1%). The use of a Markov regression model can be useful as it permits one to address easily both the regression objective and the stochastic dependence between successive observations. However, it is important to verify the sensitivity of the Markov regression parameters to the time-dependence structure. In this study, it was found that, although what happened on the previous day was a strong predictor of what happened on the current day, this did not contradict the O3 -respiratory symptom associations. Compared to the Markov regression model, the signs of the parameter estimates of marginal and random-intercept models remain the same. The magnitudes of the O3 effects were also essentially the same in the three models, whose confidence intervals overlapped. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Marginal Analysis of Incomplete Longitudinal Binary Data: A Cautionary Note on LOCF Imputation

BIOMETRICS, Issue 3 2004
Richard J. Cook
Summary In recent years there has been considerable research devoted to the development of methods for the analysis of incomplete data in longitudinal studies. Despite these advances, the methods used in practice have changed relatively little, particularly in the reporting of pharmaceutical trials. In this setting, perhaps the most widely adopted strategy for dealing with incomplete longitudinal data is imputation by the "last observation carried forward" (LOCF) approach, in which values for missing responses are imputed using observations from the most recently completed assessment. We examine the asymptotic and empirical bias, the empirical type I error rate, and the empirical coverage probability associated with estimators and tests of treatment effect based on the LOCF imputation strategy. We consider a setting involving longitudinal binary data with longitudinal analyses based on generalized estimating equations, and an analysis based simply on the response at the end of the scheduled follow-up. We find that for both of these approaches, imputation by LOCF can lead to substantial biases in estimators of treatment effects, the type I error rates of associated tests can be greatly inflated, and the coverage probability can be far from the nominal level. Alternative analyses based on all available data lead to estimators with comparatively small bias, and inverse probability weighted analyses yield consistent estimators subject to correct specification of the missing data process. We illustrate the differences between various methods of dealing with drop-outs using data from a study of smoking behavior. [source]