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Browsing by Author "Hasan, Mohammad"
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Item Innovative Bayesian Designs for Clinical Trials(2022-10) He, Tian; Zang, Yong; Liu, Hao; Bakoyannis, Giorgos; Zhao, Yi; Hasan, MohammadTraditional clinical trial designs are generally based on the doctrine of studying one drug for one disease at a time, which may be slow and inefficient. With a high failure rate in drug development, there is a great need to speed up the process of drug development and minimize the cost. Novel trial designs have been proposed, such as the master protocol approach, which has expanded the trial design horizon to umbrella, basket, and platform trials. Compared to traditional clinical protocols, the master protocol enables investigators to evaluate multiple drugs and diverse disease populations simultaneously in a single protocol with the capacity to modify the protocol based on the observed trial data and new drugs. While many statistical methods for trial designs have been proposed for umbrella, basket, and platform trials in the literature, most of the designs are based on a binary or continuous endpoint. However, in the context of oncology trials, there is a great need to develop novel methods for survival endpoints. In this dissertation, we propose three novel Bayesian statistical methods for three distinctive trial design problems, respectively: 1) an optimal Bayesian design for platform trials with multiple endpoints; 2) a novel Bayesian design for basket trials with survival outcomes; 3) an adaptive Bayesian design for seamless phase II/III platform trials with survival endpoints. Extensive simulation studies are performed to evaluate the operating characteristics of the proposed designs under various scenarios.Item Solving Prediction Problems from Temporal Event Data on Networks(2021-08) Sha, Hao; Mohler, George; Hasan, Mohammad; Dundar, Murat; Mukhopadhyay, SnehasisMany complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.Item Using Social Media Websites to Support Scenario-Based Design of Assistive Technology(2020-01) Yu, Xing; Brady, Erin; Palakal, Mathew; Bolchini, Davide; Chakraborty, Sunandan; Hasan, MohammadHaving representative users, who have the targeted disability, in accessibility studies is vital to the validity of research findings. Although it is a widely accepted tenet in the HCI community, many barriers and difficulties make it very resource-demanding for accessibility researchers to recruit representative users. As a result, researchers recruit non-representative users, who do not have the targeted disability, instead of representative users in accessibility studies. Although such an approach has been widely justified, evidence showed that findings derived from non-representative users could be biased and even misleading. To address this problem, researchers have come up with different solutions such as building pools of users to recruit from. But still, the data is not widely available and needs a lot of effort and resource to build and maintain. On the other hand, online social media websites have become popular in the last decade. Many online communities have emerged that allow online users to discuss health-related subjects, exchange useful information, or provide emotional support. A large amount of data accumulated in such online communities have gained attention from researchers in the healthcare domain. And many researches have been done based on data from social media websites to better understand health problems to improve the wellbeing of people. Despite the increasing popularity, the value of data from social media websites for accessibility research remains untapped. Hence, my work aims to create methods that could extract valuable information from data collected on social media websites for accessibility practitioners to support their design process. First, I investigate methods that enable researchers to effectively collect representative data from social media websites. More specifically, I look into machine learning approaches that could allow researchers to automatically identify online users who have disabilities (representative users). Second, I investigate methods that could extract useful information from user-generated free-text using techniques drawn from the information extraction domain. Last, I explore how such information should be visualized and presented for designers to support the scenario-based design process in accessibility studies.