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Longitudinal Data Analysis and Time Series Commons™
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Articles 1 - 4 of 4
Full-Text Articles in Longitudinal Data Analysis and Time Series
Set-Based Tests For Genetic Association In Longitudinal Studies, Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Ana V. Diez Roux, Bhramar Mukherjee
Set-Based Tests For Genetic Association In Longitudinal Studies, Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Ana V. Diez Roux, Bhramar Mukherjee
Jennifer McMahon
Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint eff#11;ect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set …
Surrogate Markers For Time-Varying Treatments And Outcomes, Jesse Hsu, Edward Kennedy, Jason Roy, Alisa Stephens-Shields, Dylan Small, Marshall Joffe
Surrogate Markers For Time-Varying Treatments And Outcomes, Jesse Hsu, Edward Kennedy, Jason Roy, Alisa Stephens-Shields, Dylan Small, Marshall Joffe
Edward H. Kennedy
A surrogate marker is a variable commonly used in clinical trials to guide treatment decisions when the outcome of ultimate interest is not available. A good surrogate marker is one where the treatment effect on the surrogate is a strong predictor of the effect of treatment on the outcome. We review the situation when there is one treatment delivered at baseline, one surrogate measured at one later time point, and one ultimate outcome of interest and discuss new issues arising when variables are time-varying. Most of the literature on surrogate markers has only considered simple settings with one treatment, one …
Optimal Restricted Estimation For More Efficient Longitudinal Causal Inference, Edward Kennedy, Marshall Joffe, Dylan Small
Optimal Restricted Estimation For More Efficient Longitudinal Causal Inference, Edward Kennedy, Marshall Joffe, Dylan Small
Edward H. Kennedy
Efficient semiparametric estimation of longitudinal causal effects is often analytically or computationally intractable. We propose a novel restricted estimation approach for increasing efficiency, which can be used with other techniques, is straightforward to implement, and requires no additional modeling assumptions.
Case Studies In Evaluating Time Series Prediction Models Using The Relative Mean Absolute Error, Nicholas G. Reich, Justin Lessler, Krzysztof Sakrejda, Stephen A. Lauer, Sopon Iamsirithaworn, Derek A T Cummings
Case Studies In Evaluating Time Series Prediction Models Using The Relative Mean Absolute Error, Nicholas G. Reich, Justin Lessler, Krzysztof Sakrejda, Stephen A. Lauer, Sopon Iamsirithaworn, Derek A T Cummings
Nicholas G Reich
Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this paper, we present a framework for evaluating time series predictions that emphasizes computational simplicity and an intuitive interpretation using the relative mean absolute error metric. For a single time series, this metric enables comparisons of candidate model predictions against naive reference models, a method that can provide useful and standardized performance benchmarks. Additionally, in applications with multiple time series, this …