Scalable Semi-parametric Methods in Biostatistics

Individualized health, or precision medicine, is an emerging approach for disease prevention and treatment guided by the individual characteristics of the genome, medical imaging, family history, environment and lifestyle of each person. To achieve this goal, it requires efficient and scalable statistical technologies to decipher the connection between this information and the health outcomes. In this thesis, we present statistical methods in support of the goal of individualized health. In Part I, the primary goal is to provide flexible and efficient estimation to the latent etiology distribution given imperfect measurements. We parameterize the latent etiologic state as a multivariate binary variable, where each binary node represents the presence/absence of an etiologic agent. The multivariate binary measurements are assumed to be conditionally independent given the latent state. Their relation is parameterized by the true positive rates and false positive rates of the measurements. External information extracted from previous literature on the true positive rates are summarized by Beta prior distributions and used to improve the model identifiability. Experts' knowledge on the competition mechanism among etiologic agents is translated into a sparse correlation structure of the latent state. A scalable Markov Chain Monte Carlo algorithm is proposed for approximating the exact posterior distribution. Also, a variational Bayesian algorithm is developed for fast and even more scalable estimation in case of large-scale problems. We demonstrate the model using the data from the motivating Pneumonia Etiology Research for Child Health (PERCH) study, which aims to provide a comprehensive estimation of the etiology distribution of childhood pneumonia in developing countries. In Part II, the key objective is to improve the efficiency of survival regression estimators by incorporating external information on the population level survival rates. The accelerated failure time (AFT) model and the Cox proportional hazards model are considered. For each model, the first estimating equation is created based on the benchmark semi-parametric estimator (partial-likelihood estimator for Cox and log-rank estimator for AFT), then additional estimating equations are formed based on the auxiliary survival information. The estimating equations are transformed by applying functional delta method to a set of over-identifying moment conditions. Finally, the parameter estimation and model diagnostics are carried out following the standard generalized method of moments (GMM) framework. We show that the new GMM-based estimators are asymptotically and empirically more efficient than the benchmark estimators. These new estimators are applied to a recent retrospective study on the prognosis of pancreatic cancer

Cenozoic Depositional Evolution and Stratal Patterns in the Western Pearl River Mouth Basin, South China Sea: Implications for Hydrocarbon Exploration

Investigating the deposition evolution and stratal stacking patterns in continental rift basins is critical not only to better understand the mechanism of basin fills but also to reveal the enrichment regularity of hydrocarbon reservoirs. The Pearl River Mouth Basin (PRMB) is a petroliferous continental rift basin located in the northern continental shelf of the South China Sea. In this study, the depositional evolution process and stacking pattern of the Zhu III Depression, western PRMB were studied through the integration of 3D seismic data, core data, and well logs. Five types of depositional systems formed from the Eocene to the Miocene, including the fan delta, meandering river delta, tidal flat, lacustrine system, and neritic shelf system. The representative depositional systems changed from the proximal fan delta and lacustrine system in the Eocene–early Oligocene, to the tidal flat and fan delta in the late Oligocene, and then the neritic shelf system in the Miocene. The statal stacking pattern varied in time and space with a total of six types of slope break belts developed. The diversity of sequence architecture results from the comprehensive effect of tectonic activities, sediment supply, sea/lake level changes, and geomorphic conditions. In addition, our results suggest that the types of traps are closely associated with stratal stacking patterns. Structural traps were developed in the regions of tectonic slope breaks, whereas lithological traps occurred within sedimentary slope breaks. This study highlights the diversity and complexity of sequence architecture in the continental rift basin, and the proposed hydrocarbon distribution patterns are applicable to reservoir prediction in the PRMB and the other continental rift basins

Predicting survival time for metastatic castration resistant prostate cancer: An iterative imputation approach [version 1; referees: 2 approved, 1 approved with reservations]

In this paper, we present our winning method for survival time prediction in the 2015 Prostate Cancer DREAM Challenge, a recent crowdsourced competition focused on risk and survival time predictions for patients with metastatic castration-resistant prostate cancer (mCRPC). We are interested in using a patient's covariates to predict his or her time until death after initiating standard therapy. We propose an iterative algorithm to multiply impute right-censored survival times and use ensemble learning methods to characterize the dependence of these imputed survival times on possibly many covariates. We show that by iterating over imputation and ensemble learning steps, we guide imputation with patient covariates and, subsequently, optimize the accuracy of survival time prediction. This method is generally applicable to time-to-event prediction problems in the presence of right-censoring. We demonstrate the proposed method's performance with training and validation results from the DREAM Challenge and compare its accuracy with existing methods