|Grant Number:||7R01CA085295-13 Interpret this number|
|Primary Investigator:||Daniels, Michael|
|Organization:||University Of Texas, Austin|
|Project Title:||Bayesian Methods for (INCOMPLETE) Longitudinal Cancer Data|
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.
Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function.
Authors: Su L, Daniels MJ
Source: Stat Med, 2015 May 30;34(12), p. 2004-18.
EPub date: 2015 Mar 12.
Fully Bayesian inference under ignorable missingness in the presence of auxiliary covariates.
Authors: Daniels MJ, Wang C, Marcus BH
Source: Biometrics, 2014 Mar;70(1), p. 62-72.
EPub date: 2013 Dec 10.
A semiparametric approach to simultaneous covariance estimation for bivariate sparse longitudinal data.
Authors: Das K, Daniels MJ
Source: Biometrics, 2014 Mar;70(1), p. 33-43.
EPub date: 2014 Jan 8.
Causal inference for bivariate longitudinal quality of life data in presence of death by using global odds ratios.
Authors: Lee K, Daniels MJ
Source: Stat Med, 2013 Oct 30;32(24), p. 4275-84.
EPub date: 2013 May 30.
Flexible marginalized models for bivariate longitudinal ordinal data.
Authors: Lee K, Daniels MJ, Joo Y
Source: Biostatistics, 2013 Jul;14(3), p. 462-76.
EPub date: 2013 Jan 29.
An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout.
Authors: Li N, Daniels MJ, Li G, Elashoff RM
Source: Biom J, 2013 Jan;55(1), p. 17-37.
EPub date: 2012 Nov 2.
Bayesian inference for the causal effect of mediation.
Authors: Daniels MJ, Roy JA, Kim C, Hogan JW, Perri MG
Source: Biometrics, 2012 Dec;68(4), p. 1028-36.
EPub date: 2012 Sep 24.
Bayesian model selection for incomplete data using the posterior predictive distribution.
Authors: Daniels MJ, Chatterjee AS, Wang C
Source: Biometrics, 2012 Dec;68(4), p. 1055-63.
EPub date: 2012 May 2.
Multiple imputation of missing phenotype data for QTL mapping.
Authors: Bobb JF, Scharfstein DO, Daniels MJ, Collins FS, Kelada S
Source: Stat Appl Genet Mol Biol, 2011;10(1), p. Article 29.
Marginalized models for longitudinal ordinal data with application to quality of life studies.
Authors: Lee K, Daniels MJ
Source: Stat Med, 2008 Sep 20;27(21), p. 4359-80.
A class of markov models for longitudinal ordinal data.
Authors: Lee K, Daniels MJ
Source: Biometrics, 2007 Dec;63(4), p. 1060-7.
On estimation of vaccine efficacy using validation samples with selection bias.
Authors: Scharfstein DO, Halloran ME, Chu H, Daniels MJ
Source: Biostatistics, 2006 Oct;7(4), p. 615-29.
EPub date: 2006 Mar 23.
Longitudinal profiling of health care units based on continuous and discrete patient outcomes.
Authors: Daniels MJ, Normand SL
Source: Biostatistics, 2006 Jan;7(1), p. 1-15.
EPub date: 2005 May 25.
Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes.
Authors: Scharfstein DO, Daniels MJ, Robins JM
Source: Biostatistics, 2003 Oct;4(4), p. 495-512.
Modelling the random effects covariance matrix in longitudinal data.
Authors: Daniels MJ, Zhao YD
Source: Stat Med, 2003 May 30;22(10), p. 1631-47.
Dynamic conditionally linear mixed models for longitudinal data.
Authors: Pourahmadi M, Daniels MJ
Source: Biometrics, 2002 Mar;58(1), p. 225-31.