Grant Number:	1R01CA183854-01 Interpret this number
Primary Investigator:	Daniels, Michael
Organization:	University Of Texas At Austin
Project Title:	Bayesian Approaches for Missingness and Causality in Cancer and Behavior Studies
Fiscal Year:	2014

DESCRIPTION (provided by applicant): This proposal will develop novel Bayesian approaches to handle missingness and conduct causal inference for important problems in biomedical research with particular relevance to cancer and behavioral studies. Missing data is a major problem in clinical studies. Of late, more e ort is spent to try to minimize the amount of missingness, but it remains a problem. We will address several pressing complications in the analysis of incomplete data in clinical settings as documented in a recent National Academies of Science report, including assessing model t to the observed data, developing Bayesian approaches for auxiliary covariates, and nonparametric modeling of nonignorable missingness. The mechanisms of treatment effectiveness are of particular interest in behavioral trials. Specifically, how do different processes mediate the effect of an intervention? This can facilitate constructing future interventions. However, determining the causal effect of such 'mediators' on the outcomes is difficult. We will develop new approaches to identify these effects in complex settings with multiple mediators and longitudinal mediators for which little work has been done. Another important question is how to de ne and identify causal effects of interventions on outcomes in the setting of semi-competing risks. Semi-competing risks occur in studies where a progression endpoint may be pre-empted by death or censored due to loss to follow-up or study termination. Subjects who experience a progression event are also followed for survival, which may be censored. Data of this form has been termed semi-competing risks data. This paradigm is particularly relevant to certain brain cancer trials, where the semi-competing risks are death and cerebellar progression. For all these settings, a Bayesian approach is ideal as it allows one to appropriately characterize uncertainty about invariable assumptions (which are present in all these problems). The methods developed here will help answer numerous important clinical questions including the mechanisms of behavior change, both in weight management and smoking cessation, via the ability to appropriately assess mediation, and the joint causal effect of treatment on time to death and cerebellar progression in brain cancer. We will disseminate code for these methods (via the PI's webpage) to ensure the methods will be readily usable by investigators in their own studies. The history of the PI's collaboration with the PI's of the individual clinical studies and the statistician co- investigators will help the team produce the best science and facilitate dissemination of our clinical findings and new methods to the appropriate audience via both subject matter publications and presentations at relevant conferences.




Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees.
Authors: Xu Y. , Hogan J. , Daniels M. , Kantor R. , Mwangi A. .
Source: Journal Of Computational And Graphical Statistics : A Joint Publication Of American Statistical Association, Institute Of Mathematical Statistics, Interface Foundation Of North America, 2025 Jun; 34(2), p. 498-508.
EPub date: 2024-09-24 00:00:00.0.
PMID: 40894954
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A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.
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Source: Biometrics, 2024-07-01 00:00:00.0; 80(3), .
PMID: 39311673
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Variable Selection Using Bayesian Additive Regression Trees.
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Source: Statistical Science : A Review Journal Of The Institute Of Mathematical Statistics, 2024 May; 39(2), p. 286-304.
EPub date: 2024-05-05 00:00:00.0.
PMID: 39281973
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A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.
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Source: Biostatistics (oxford, England), 2024-02-09 00:00:00.0; , .
EPub date: 2024-02-09 00:00:00.0.
PMID: 38332624
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Dirichlet process mixture models for the analysis of repeated attempt designs.
Authors: Daniels M.J. , Lee M. , Feng W. .
Source: Biometrics, 2023-06-22 00:00:00.0; , .
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PMID: 37349969
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A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.
Authors: Josefsson M. , Daniels M.J. , Pudas S. .
Source: Biostatistics (oxford, England), 2023-04-14 00:00:00.0; 24(2), p. 372-387.
PMID: 33880509
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Adjusting for selection bias due to missing data in electronic health records-based research.
Authors: Peskoe S.B. , Arterburn D. , Coleman K.J. , Herrinton L.J. , Daniels M.J. , Haneuse S. .
Source: Statistical Methods In Medical Research, 2021 10; 30(10), p. 2221-2238.
EPub date: 2021-08-26 00:00:00.0.
PMID: 34445911
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Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.
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Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2021 Mar; 70(2), p. 398-414.
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idem: An R Package for Inferences in Clinical Trials with Death and Missingness.
Authors: Wang C. , Colantuoni E. , Leroux A. , Scharfstein D.O. .
Source: Journal Of Statistical Software, 2020 May; 93, .
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A Bayesian parametric approach to handle missing longitudinal outcome data in trial-based health economic evaluations.
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Source: Journal Of The Royal Statistical Society. Series A, (statistics In Society), 2020 Feb; 183(2), p. 607-629.
EPub date: 2019-09-26 00:00:00.0.
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A Note on Monotonicity in Repeated Attempt Selection Models.
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Source: Statistics & Probability Letters, 2020 Jan; 156, .
EPub date: 2019-08-22 00:00:00.0.
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A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies with Auxiliary Covariates.
Authors: Zhou T. , Daniels M.J. , Müller P. .
Source: Journal Of Computational And Graphical Statistics : A Joint Publication Of American Statistical Association, Institute Of Mathematical Statistics, Interface Foundation Of North America, 2020; 29(1), p. 1-12.
EPub date: 2019-07-02 00:00:00.0.
PMID: 33013150
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A sensitivity analysis approach for informative dropout using shared parameter models.
Authors: Su L. , Li Q. , Barrett J.K. , Daniels M.J. .
Source: Biometrics, 2019 Sep; 75(3), p. 917-926.
EPub date: 2019-04-01 00:00:00.0.
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BAYESIAN METHODS FOR MULTIPLE MEDIATORS: RELATING PRINCIPAL STRATIFICATION AND CAUSAL MEDIATION IN THE ANALYSIS OF POWER PLANT EMISSION CONTROLS.
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Source: The Annals Of Applied Statistics, 2019 Sep; 13(3), p. 1927-1956.
EPub date: 2019-10-17 00:00:00.0.
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Handling Missing Data in Instrumental Variable Methods for Causal Inference.
Authors: Kennedy E.H. , Mauro J.A. , Daniels M.J. , Burns N. , Small D.S. .
Source: Annual Review Of Statistics And Its Application, 2019 Mar; 6(1), p. 125-148.
EPub date: 2018-11-28 00:00:00.0.
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Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.
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Causal comparative effectiveness analysis of dynamic continuous-time treatment initiation rules with sparsely measured outcomes and death.
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Fixed choice design and augmented fixed choice design for network data with missing observations.
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Effect of dose of behavioral weight loss treatment on glycemic control in adults with prediabetes.
Authors: Bauman V. , Ariel-Donges A.H. , Gordon E.L. , Daniels M.J. , Xu D. , Ross K.M. , Limacher M.C. , Perri M.G. .
Source: Bmj Open Diabetes Research & Care, 2019; 7(1), p. e000653.
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Discussion of PENCOMP.
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Source: Journal Of The American Statistical Association, 2019; 114(525), p. 24-27.
EPub date: 2019-04-19 00:00:00.0.
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Bayesian nonparametric generative models for causal inference with missing at random covariates.
Authors: Roy J. , Lum K.J. , Zeldow B. , Dworkin J.D. , Re V.L. , Daniels M.J. .
Source: Biometrics, 2018 12; 74(4), p. 1193-1202.
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A note on compatibility for inference with missing data in the presence of auxiliary covariates.
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Optimizing and evaluating biomarker combinations as trial-level general surrogates.
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Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions.
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ARMA Cholesky Factor Models for the Covariance Matrix of Linear Models.
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Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data.
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Causal inference with longitudinal outcomes and non-ignorable drop-out: Estimating the effect of living alone on cognitive decline.
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A General Framework For Considering Selection Bias In Ehr-based Studies: What Data Are Observed And Why?
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