Dataset Information

Development and application of a longitudinal electrocardiogram repository: the Framingham Heart Study.

ABSTRACT: The electrocardiogram (ECG) has widespread use in clinical care and research. Despite its extensive use and study, important gaps remain in examining prospective, repeated longitudinal ECG measures, and their association with cardiovascular outcomes. The Framingham Heart Study (FHS) is a community-based study designed to examine risk factors and outcomes associated with cardiovascular disease. Here, we describe a novel effort in the FHS to develop a unique resource: serial ECGs conducted on 3 generations of study participants spanning multiple decades (1986 to the present). We describe the FHS and the role the ECG has had in conducting cardiovascular epidemiology in the FHS. We then describe potential applications for a longitudinal ECG repository. We expect the Framingham ECG repository to enhance cardiovascular research and epidemiologic study. Such a resource will complement the phenotypic and genotypic characterization of FHS, facilitating novel investigations of cardiovascular epidemiology.

SUBMITTER: Magnani JW

PROVIDER: S-EPMC3483375 | biostudies-literature | 2012 Nov-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and application of a longitudinal electrocardiogram repository: the Framingham Heart Study.

Magnani Jared W JW Newton-Cheh Christopher C O'Donnell Christopher J CJ Levy Daniel D

Journal of electrocardiology 20120724 6

The electrocardiogram (ECG) has widespread use in clinical care and research. Despite its extensive use and study, important gaps remain in examining prospective, repeated longitudinal ECG measures, and their association with cardiovascular outcomes. The Framingham Heart Study (FHS) is a community-based study designed to examine risk factors and outcomes associated with cardiovascular disease. Here, we describe a novel effort in the FHS to develop a unique resource: serial ECGs conducted on 3 ge ...[more]

PMID: 22832152

Similar Datasets

Project description:BackgroundStatistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome.MethodsIn this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times.ResultsSimulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.ConclusionsTraditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Dataset Information

Development and application of a longitudinal electrocardiogram repository: the Framingham Heart Study.

Publications

Development and application of a longitudinal electrocardiogram repository: the Framingham Heart Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets