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.

Public Health Relevance

This application will address important public health problems indirectly, through improved methodology for inference in the presence of incomplete data and for causal inference, and directly through constructing methods to specifically address important health problems in behavioral studies of smoking cessation and weight management and brain cancer studies.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Liu, Benmei
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University of Texas Austin
Schools of Arts and Sciences
United States
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Hu, Liangyuan; Hogan, Joseph W; Mwangi, Ann W et al. (2018) Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co-infection. Biometrics 74:703-713
Xu, Dandan; Daniels, Michael J; Winterstein, Almut G (2018) A Bayesian nonparametric approach to causal inference on quantiles. Biometrics 74:986-996
Kim, Chanmin; Daniels, Michael; Li, Yisheng et al. (2018) A Bayesian semiparametric latent variable approach to causal mediation. Stat Med 37:1149-1161
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
Roy, Jason; Lum, Kirsten J; Daniels, Michael J (2017) A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. Biostatistics 18:32-47
Wang, Chenguang; Scharfstein, Daniel O; Colantuoni, Elizabeth et al. (2017) Inference in randomized trials with death and missingness. Biometrics 73:431-440
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
Xu, Yanxun; Müller, Peter; Wahed, Abdus S et al. (2016) Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times. J Am Stat Assoc 111:921-935

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