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Grant Details

Grant Number: 5R01CA183854-06 Interpret this number
Primary Investigator: Daniels, Michael
Organization: University Of Florida
Project Title: Bayesian Approaches for Missingness and Causality in Cancer and Behavior Studies
Fiscal Year: 2019
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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 effort 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 fit 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 define 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.

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Authors: Kim C. , Daniels M.J. , Hogan J.W. , Choirat C. , Zigler C.M. .
Source: The annals of applied statistics, 2019 Sep; 13(3), p. 1927-1956.
EPub date: 2019-10-17.
PMID: 31656548
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Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.
Authors: Siddique J. , Daniels M.J. , Carroll R.J. , Raghunathan T.E. , Stuart E.A. , Freedman L.S. .
Source: Biometrics, 2019 09; 75(3), p. 927-937.
EPub date: 2019-04-06.
PMID: 30724332
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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 09; 75(3), p. 917-926.
EPub date: 2019-04-01.
PMID: 30666621
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Causal comparative effectiveness analysis of dynamic continuous-time treatment initiation rules with sparsely measured outcomes and death.
Authors: Hu L. , Hogan J.W. .
Source: Biometrics, 2019 06; 75(2), p. 695-707.
EPub date: 2019-06-20.
PMID: 30638268
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A note on compatibility for inference with missing data in the presence of auxiliary covariates.
Authors: Daniels M.J. , Luo X. .
Source: Statistics in medicine, 2019-03-30; 38(7), p. 1190-1199.
EPub date: 2018-11-18.
PMID: 30450746
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Optimizing and evaluating biomarker combinations as trial-level general surrogates.
Authors: Gabriel E.E. , Sachs M.C. , Daniels M.J. , Halloran M.E. .
Source: Statistics in medicine, 2019-03-30; 38(7), p. 1135-1146.
EPub date: 2018-10-10.
PMID: 30306600
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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.
EPub date: 2019-05-28.
PMID: 31245006
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Fixed choice design and augmented fixed choice design for network data with missing observations.
Authors: Ott M.Q. , Harrison M.T. , Gile K.J. , Barnett N.P. , Hogan J.W. .
Source: Biostatistics (Oxford, England), 2019-01-01; 20(1), p. 97-110.
PMID: 29267874
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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.
EPub date: 2018-03-26.
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A Bayesian nonparametric approach to causal inference on quantiles.
Authors: Xu D. , Daniels M.J. , Winterstein A.G. .
Source: Biometrics, 2018 09; 74(3), p. 986-996.
EPub date: 2018-02-25.
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Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co-infection.
Authors: Hu L. , Hogan J.W. , Mwangi A.W. , Siika A. .
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EPub date: 2017-09-28.
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Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions.
Authors: Linero A.R. , Daniels M.J. .
Source: Statistical science : a review journal of the Institute of Mathematical Statistics, 2018 May; 33(2), p. 198-213.
EPub date: 2018-05-03.
PMID: 31889740
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A Bayesian semiparametric latent variable approach to causal mediation.
Authors: Kim C. , Daniels M. , Li Y. , Milbury K. , Cohen L. .
Source: Statistics in medicine, 2018-03-30; 37(7), p. 1149-1161.
EPub date: 2017-12-18.
PMID: 29250817
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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.
PMID: 29109594
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Inference in randomized trials with death and missingness.
Authors: Wang C. , Scharfstein D.O. , Colantuoni E. , Girard T.D. , Yan Y. .
Source: Biometrics, 2017 06; 73(2), p. 431-440.
EPub date: 2016-10-17.
PMID: 27753071
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A framework for Bayesian nonparametric inference for causal effects of mediation.
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Source: Biometrics, 2017 06; 73(2), p. 401-409.
EPub date: 2016-08-01.
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A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome.
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Source: Biostatistics (Oxford, England), 2017 01; 18(1), p. 32-47.
EPub date: 2016-06-26.
PMID: 27345532
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Comparing biomarkers as trial level general surrogates.
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Source: Biometrics, 2016 12; 72(4), p. 1046-1054.
EPub date: 2016-04-01.
PMID: 27038302
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A note on posterior predictive checks to assess model fit for incomplete data.
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Source: Statistics in medicine, 2016-11-30; 35(27), p. 5029-5039.
EPub date: 2016-07-18.
PMID: 27426216
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Sequential BART for imputation of missing covariates.
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Source: Biostatistics (Oxford, England), 2016 07; 17(3), p. 589-602.
EPub date: 2016-03-15.
PMID: 26980459
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Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data.
Authors: Gaskins J.T. , Daniels M.J. .
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; 25(1), p. 167-186.
EPub date: 2016-03-09.
PMID: 27175055
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Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.
Authors: Xu Y. , Müller P. , Wahed A.S. , Thall P.F. .
Source: Journal of the American Statistical Association, 2016; 111(515), p. 921-935.
EPub date: 2016-10-18.
PMID: 28018015
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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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Source: EGEMS (Washington, DC), 2016; 4(1), p. 1203.
EPub date: 2016-08-31.
PMID: 27668265
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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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Source: Journal of the Royal Statistical Society. Series C, Applied statistics, 2016-01-01; 65(1), p. 131-144.
EPub date: 2015-06-23.
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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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Quantile regression in the presence of monotone missingness with sensitivity analysis.
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Pattern mixture models for the analysis of repeated attempt designs.
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Are all biases missing data problems?
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Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function.
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A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies with Nonignorable Missingness with Application to an Acute Schizophrenia Clinical Trial.
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Source: Journal of the American Statistical Association, 2015 Mar; 110(509), p. 45-55.
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