Grant Number:	4R01CA085295-11 Interpret this number
Primary Investigator:	Daniels, Michael
Organization:	University Of Florida
Project Title:	Bayesian Methods for (INCOMPLETE) Longitudinal Cancer Data
Fiscal Year:	2011

DESCRIPTION (provided by applicant): We continue work from our previous proposal in developing new Bayesian methodology for longitudinal cancer data with missingness. In the presence of missing data that is related to observed or unobserved responses, it is known that mis-specifying the dependence will most often result in biased estimates of mean parameters. In addition, in such settings, flexible, parsimonious dependence models are often necessary. Such models are not currently available for correlation matrices (which form an integral part of many longitudinal models). The first aim of this proposal will introduce a new parameterization for a correlation matrix for longitudinal responses that offers considerable benefits with respect to prior specification and modeling. We will explore several models and priors and their associated properties, computational issues and strategies both with respect to automated parsimonious modeling, posterior sampling, and high-dimensional problems, and their implementation in a wide array of longitudinal models with applications. The second aim will explore the extension of these models to multivariate longitudinal data. In particular, we will explore the 'ordering' of the multivariate longitudinal response vector with regards to parsimonious models and prior specification and correlation/covariance structures for which this ordering is not an issue. In the third aim, we will develop new Bayesian approaches for causal inference in longitudinal cancer studies in which repeatedly measured outcomes may be informatively missing due to loss to follow-up or protocol-defined events (progression or death). In seeking to draw inference about causal estimands, non-identifiable assumptions are required. We will introduce low-dimensional, interpretable parameterizations of these assumptions and elicit priors for these parameters from scientific experts. These methods will be used to answer questions of interest from several recent cancer clinical trials including assessing potential surrogate markers (Specific Aim 1), exploring the relationship between patient reported (quality of life) and physician reported (toxicity) outcomes (Specific Aim 2), and making inference at the end of quality of life studies when subjects have dropped out due to cancer progression or death (Specific Aim 3). PUBLIC HEALTH RELEVANCE: The new methods proposed in this application will have important public health benefits. They will facilitate drawing correct inferences from quality of life studies for late stage cancers, understanding the relationship between physician reported and patient reported outcomes, and making earlier determinations of treatment effects.




ARMA Cholesky Factor Models for the Covariance Matrix of Linear Models.
Authors: Lee K. , Baek C. , Daniels M.J. .
Source: Computational Statistics & Data Analysis, 2017 Nov; 115, p. 267-280.
EPub date: 2017-05-18 00:00:00.0.
PMID: 29109594
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A note on posterior predictive checks to assess model fit for incomplete data.
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PMID: 27426216
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Inference In Randomized Trials With Death And Missingness
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Source: Biometrics, 2016-10-17 00:00:00.0; , .
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A Framework For Bayesian Nonparametric Inference For Causal Effects Of Mediation
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Source: Biometrics, 2016-08-01 00:00:00.0; , .
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Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data.
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Source: Journal Of Computational And Graphical Statistics : A Joint Publication Of American Statistical Association, Institute Of Mathematical Statistics, Interface Foundation Of North America, 2016-01-02 00:00:00.0; 25(1), p. 167-186.
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PMID: 27175055
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Quantile regression in the presence of monotone missingness with sensitivity analysis.
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Source: Biostatistics (oxford, England), 2016 Jan; 17(1), p. 108-21.
PMID: 26041008
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Causal inference with longitudinal outcomes and non-ignorable drop-out: Estimating the effect of living alone on cognitive decline.
Authors: Josefsson M. , de Luna X. , Daniels M.J. , Nyberg L. .
Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2016-01-01 00:00:00.0; 65(1), p. 131-144.
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Bayesian methods for nonignorable dropout in joint models in smoking cessation studies.
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Source: Journal Of The American Statistical Association, 2016; 111(516), p. 1454-1465.
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Pattern mixture models for the analysis of repeated attempt designs.
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Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function.
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Source: Journal Of The American Statistical Association, 2015 Mar; 110(509), p. 45-55.
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Computationally Efficient Banding Of Large Covariance Matrices For Ordered Data And Connections To Banding The Inverse Cholesky Factor
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Source: Journal Of Multivariate Analysis, 2014-09-01 00:00:00.0; 130, p. 21-26.
PMID: 25147413
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A Semiparametric Approach To Simultaneous Covariance Estimation For Bivariate Sparse Longitudinal Data
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Source: Biometrics, 2014 Mar; 70(1), p. 33-43.
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Fully Bayesian Inference Under Ignorable Missingness In The Presence Of Auxiliary Covariates
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Sparsity Inducing Prior Distributions For Correlation Matrices Of Longitudinal Data
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Causal Inference For Bivariate Longitudinal Quality Of Life Data In Presence Of Death By Using Global Odds Ratios
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Source: Statistics In Medicine, 2013-10-30 00:00:00.0; 32(24), p. 4275-84.
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Source: Biometrical Journal. Biometrische Zeitschrift, 2013 Jan; 55(1), p. 17-37.
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Source: Biometrics, 2012 Dec; 68(4), p. 1028-36.
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Source: Biometrics, 2012 Jun; 68(2), p. 361-70.
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Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2011-08-01 00:00:00.0; 60(4), p. 475-496.
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