BAYESIAN MODELING OF CENSORED DATA WITH APPLICATION TO META-ANALYSIS OF IMMUNOTHERAPY TRIALS

My dissertation builds on a systematic review of 125 clinical trials reporting on treatment-related adverse events (AEs) associated with PD-1/PD-L1 inhibitors published from 2010 to 2018. The motivating dataset contained the following study-level components extracted from each publication: trial name, number of treated patients, selected immunotherapy drug, dosing schedule, cancer type, number of AEs within each category, and the pre-specified criteria for AE reporting. The number of AEs were reported based upon all-grade (Grade 1-5) and Grade 3 or higher (Grade 3-5) severity. My overall objective was to increase our understanding of the toxicity profiles of five most common cancer immunotherapy drugs, and to evaluate AE incidence across subgroups in a meta-analysis setting. However, for assessing drug safety in clinical trials, a common challenge is that many published clinical studies do not report rare AEs. In particular, if the number of AEs observed is lower than a pre-specified cutoff value, these events may not always be reported in the publication (i.e., they are censored). My doctoral dissertation research, thus, proposes an innovative statistical methodology for effectively handling censored rare AEs in the context of meta-analysis of immunotherapy trials. First, by deriving exact inference and robust estimates for the missing not at random data, we proposed a Bayesian multilevel regression model in the coarsened data framework to accommodate censored rare event data. We also demonstrated that if the censored information was ignored, the incidence probability of AEs would be overestimated. Second, to select the best Bayesian censored data model among a set of candidate models in the presence of complicated or high-dimensional features, we proposed an alternative strategy to implement Bayesian model selection for censored data analysis in Just Another Gibbs Sampling (JAGS). To generate deviance samples from a Bayesian model using JAGS, if censoring occurs, an existing function incorrectly calculates the value of deviance function because of the “wrong focus”, i.e., the incorrect likelihood computed on the basis of model specification in JAGS. Therefore, we proposed a strategy to establish a simultaneous way to calculate the true value of deviance function in JAGS. The alternative strategy could be generalized to model other types of data and be applied to many other disciplines. Third, we developed a sparse Bayesian selection model with prior specifications on meta-analysis of censored rare AEs to perform selection of pairwise interactions between various study-level factors. Because the toxicity profiles of immunotherapy drugs may not be explained comprehensively by main effects of study-level factors, we identified the high-risk group by considering two-way interactions that impact the outcome of interest. Through simulation studies, we demonstrated that the proposed interaction selection method outperforms others in prediction accuracy and interaction identification in the presence of missing outcome data. Lastly, we also applied the proposed method to our real-world motivating dataset. In sum, my dissertation work makes significant and innovative contributions to the field of applied statistics and cancer research

Camouflaged Object Detection with Feature Grafting and Distractor Aware

The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detection as the texture between the target and background is visually indistinguishable. In this paper, we proposed a novel Feature Grafting and Distractor Aware network (FDNet) to handle the COD task. Specifically, we use CNN and Transformer to encode multi-scale images in parallel. In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module. A Distractor Aware Module is designed to explicitly model the two possible distractors in the COD task to refine the coarse camouflage map. We also proposed the largest artificial camouflaged object dataset which contains 2000 images with annotations, named ACOD2K. We conducted extensive experiments on four widely used benchmark datasets and the ACOD2K dataset. The results show that our method significantly outperforms other state-of-the-art methods. The code and the ACOD2K will be available at https://github.com/syxvision/FDNet.Comment: ICME2023 pape

A Note on Bayesian Modeling Specification of Censored Data in JAGS

Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function dinterval() to model censored data misspecifies the computation of deviance function, which may limit its usage to perform likelihood based model comparison. To establish an automatic approach to specify the correct deviance function in JAGS, we propose a simple alternative modeling strategy to implement Bayesian model selection for analysis of censored outcomes. The proposed approach is applicable to a broad spectrum of data types, which include survival data and many other right-, left- and interval-censored Bayesian model structures

Correction: On Bayesian modeling of censored data in JAGS

Following the publication of the original article [1], the authors identified errors in the model specifications 1 and 2. The correct models are given below

Progress in the clinical treatment of keloids

Keloid is a pathological scar that is higher than the skin surface following skin damage. Its lesion range often extends beyond the original damage boundary and does not naturally subside over time. Its pathogenesis is very complex, currently the main causes include fibroblast excessive proliferation, collagen and extracellular matrix (Extracellular matrix, ECM) excessive deposition, excessive angiogenesis, and so on. The traditional treatment method primarily involves surgical intervention, but it is associated with a high recurrence rate post-surgery. Consequently, many treatment methods are derived according to the different clinical characteristics of keloid. This paper will review the therapeutic progress in recent years from surgical treatment, physiotherapy, drug therapy, and biological therapy, with the goal of offering valuable insights for the clinical treatment of keloids

A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation

Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government authorities to take preventive measures. In this paper, the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) models are combined. Then a hybrid CNN-LSTM model is proposed to predict the daily PM2.5 concentration in Beijing based on spatiotemporal correlation. Specifically, a Pearson's correlation coefficient is adopted to measure the relationship between PM2.5 in Beijing and air pollutants in its surrounding cities. In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the LSTM model is used to extract the temporal information. In order to evaluate the proposed model, three evaluation indexes are introduced, including root mean square error, mean absolute percent error, and R-squared. As a result, the hybrid CNN-LSTM model achieves the best performance compared with the Multilayer perceptron model (MLP) and LSTM. Moreover, the prediction accuracy of the proposed model considering spatiotemporal correlation outperforms the same model without spatiotemporal correlation. Therefore, the hybrid CNN-LSTM model can be adopted for PM2.5 concentration prediction