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Longitudinal Data Analysis and Time Series Commons™
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- and allow for time vary- (1)
- And easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals (1)
- As comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them (1)
- Asthma; Cumuluative Residuals; Repeated Measured; Spatial Cluster Detection; Wheeze (1)
- Bayesian; multi-state; recurrent event; competing risk; hierarchical; stratified; survival analysis This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible (1)
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- Body mass index; Cumulative residuals; Generalized estimating equations; Socioeconomic status; Spatial cluster detection; Weighted linear regression (1)
- Exploratory data analysis; History matrix; History matrix visualization; Longitudinal categorical data; Multi-state survival analysis (1)
- Scalable to larger studies (1)
Articles 1 - 6 of 6
Full-Text Articles in Longitudinal Data Analysis and Time Series
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart, Brian Caffo, Ciprian Crainiceanu, Naresh M. Punjabi
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart, Brian Caffo, Ciprian Crainiceanu, Naresh M. Punjabi
Johns Hopkins University, Dept. of Biostatistics Working Papers
This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified …
Lasagna Plots: A Saucy Alternative To Spaghetti Plots, Bruce Swihart, Brian Caffo, Bryan D. James, Matthew Strand, Brian S. Schwartz, Naresh M. Punjabi
Lasagna Plots: A Saucy Alternative To Spaghetti Plots, Bruce Swihart, Brian Caffo, Bryan D. James, Matthew Strand, Brian S. Schwartz, Naresh M. Punjabi
Johns Hopkins University, Dept. of Biostatistics Working Papers
Longitudinal repeated measures data has often been visualized with spaghetti plots for continuous out- comes. For large datasets, this often leads to over-plotting and consequential obscuring of trends in the data. This is primarily due to overlapping of trajectories. Here, we suggest a framework called lasagna plot ting that constrains the subject-specific trajectories to prevent overlapping and utilizes gradients of color to depict the outcome. Dynamic sorting and visualization is demonstrated as an exploratory data analysis tool. Supplemental material in the form of sample R code additional illustrated examples are available online.
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart
Modeling Multilevel Sleep Transitional Data Via Poisson Log-Linear Multilevel Models, Bruce J. Swihart
COBRA Preprint Series
This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified …
Reliability Of The Model For Clustering Of Longitudinal Datasets Of Infant Mortality Rate In India, Ajay Kumar Bansal, S D. Sharma
Reliability Of The Model For Clustering Of Longitudinal Datasets Of Infant Mortality Rate In India, Ajay Kumar Bansal, S D. Sharma
COBRA Preprint Series
Because of the natural tendency of human beings and heavenly bodies to form groups, the technique of cluster analysis or segmentation analysis find its importance and applications in many fields of study. A model for clustering of time trends was proposed by authors whose beauty is that 2-way dimensions that is the horizontal flow of the trend and vertical distance of the trend from a common base are considered to obtain the natural clusters. In the present paper, the reliability of this model is studied in two steps namely (i) by repeating the analysis but using different interval distance measures …
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari
Harvard University Biostatistics Working Paper Series
No abstract provided.