In statistical modeling the covariance structure is often considered a nuisance, or at least of secondary importance to the mean. However, when the goals of an analysis include estimation of subject-specific effects or prediction, estimation of the covariance structure is very important. This project will develop methodology for improving estimation of covariance structure in longitudinal cancer studies. This will result in increased efficiency in estimation of fixed and random effects, such as subject-specific trajectories, and predictions. Specifically, this project will develop estimators of covariance matrices that are robust to a variety of eigenstructures and/or structural assumptions, develop methods to compute these estimators in hierarchical models, and develop classes of models to account for heterogeneity, both explained and unexplained by covariates, in covariance structures across subjects in longitudinal trials. In sum, this will provide greater flexibility in modeling, and efficiency in making inferences from, longitudinal cancer data. The methods include development of sensible prior distribution from which estimators can be derived that have good properties in small samples and/or in high dimensions and construction of hierarchical models to account for heterogeneous covariance structures across subjects through covariates and/or prior distributions. The common theme will be prior distributions which sh6nk the cova6ance matrix or function toward some parametric form or to some 'average' matrix or function, with the amount of shrinkage determined by the data. Computationally efficient ways to compute the estimators and fit the models will be explored, some of which will involve only minor modifications to standard software.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-SNEM-5 (01))
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Erickson, Burdette (BUD) W
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University of Florida
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Wang, Chenguang; Scharfstein, Daniel O; Colantuoni, Elizabeth et al. (2017) Inference in randomized trials with death and missingness. Biometrics 73:431-440
Kim, Chanmin; Daniels, Michael J; Marcus, Bess H et al. (2017) A framework for Bayesian nonparametric inference for causal effects of mediation. Biometrics 73:401-409
Lee, Keunbaik; Baek, Changryong; Daniels, Michael J (2017) ARMA Cholesky Factor Models for the Covariance Matrix of Linear Models. Comput Stat Data Anal 115:267-280
Liu, Minzhao; Daniels, Michael J; Perri, Michael G (2016) Quantile regression in the presence of monotone missingness with sensitivity analysis. Biostatistics 17:108-21
Gaskins, J T; Daniels, M J (2016) Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data. J Comput Graph Stat 25:167-186
Gaskins, J T; Daniels, M J; Marcus, B H (2016) Bayesian methods for nonignorable dropout in joint models in smoking cessation studies. J Am Stat Assoc 111:1454-1465
Xu, Dandan; Chatterjee, Arkendu; Daniels, Michael (2016) A note on posterior predictive checks to assess model fit for incomplete data. Stat Med 35:5029-5039
Josefsson, Maria; de Luna, Xavier; Daniels, Michael J et al. (2016) Causal inference with longitudinal outcomes and non-ignorable drop-out: Estimating the effect of living alone on cognitive decline. J R Stat Soc Ser C Appl Stat 65:131-144
Daniels, Michael J; Jackson, Dan; Feng, Wei et al. (2015) Pattern mixture models for the analysis of repeated attempt designs. Biometrics 71:1160-7
Su, Li; Daniels, Michael J (2015) Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function. Stat Med 34:2004-18

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