Sequential monitoring of response-adaptive randomized clinical trials

Clinical trials are complex and usually involve multiple objectives such as controlling type I error rate, increasing power to detect treatment difference, assigning more patients to better treatment, and more. In literature, both response-adaptive randomization (RAR) procedures (by changing randomization procedure sequentially) and sequential monitoring (by changing analysis procedure sequentially) have been proposed to achieve these objectives to some degree. In this paper, we propose to sequentially monitor response-adaptive randomized clinical trial and study it's properties. We prove that the sequential test statistics of the new procedure converge to a Brownian motion in distribution. Further, we show that the sequential test statistics asymptotically satisfy the canonical joint distribution defined in Jennison and Turnbull (\citeyearJT00). Therefore, type I error and other objectives can be achieved theoretically by selecting appropriate boundaries. These results open a door to sequentially monitor response-adaptive randomized clinical trials in practice. We can also observe from the simulation studies that, the proposed procedure brings together the advantages of both techniques, in dealing with power, total sample size and total failure numbers, while keeps the type I error. In addition, we illustrate the characteristics of the proposed procedure by redesigning a well-known clinical trial of maternal-infant HIV transmission.Comment: Published in at http://dx.doi.org/10.1214/10-AOS796 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bubbling Over: Soda Consumption and Its Link to Obesity in California

Based on 2005 Health Interview Survey data, examines the link between soda consumption and the prevalence of overweight and obese adults and children, geographical differences in soda consumption, and the social and environmental factors that affect it

A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

Computational genomics and genetics of developmental disorders

Computational genomics is at the intersection of computational applied physics, math, statistics, computer science and biology. With the advances in sequencing technology, large amounts of comprehensive genomic data are generated every year. However, the nature of genomic data is messy, complex and unstructured; it becomes extremely challenging to explore, analyze and understand the data based on traditional methods. The needs to develop new quantitative methods to analyze large-scale genomics datasets are urgent. By collecting, processing and organizing clean genomics datasets and using these datasets to extract insights and relevant information, we are able to develop novel methods and strategies to address specific genetics questions using the tools of applied mathematics, statistics, and human genetics. This thesis describes genetic and bioinformatics studies focused on utilizing and developing state-of-the-art computational methods and strategies in order to identify and interpret de novo mutations that are likely causing developmental disorders. We performed whole exome sequencing as well as whole genome sequencing on congenital diaphragmatic hernia parents-child trios and identified a new candidate risk gene MYRF. Additionally, we found male and female patients carry a different burden of likely-gene- disrupting mutations, and isolated and complex patients carry different gene expression levels in early development of diaphragm tissues for likely-gene-disrupting mutations. To increase the power to detect risk genes and risk variants, we developed a deep neural network classifier called MVP to accurately predict the pathogenicity of missense variants. MVP implemented an advanced structure of ResNet model and based on two independent data sets, MVP achieved clearly better results in prioritizing pathogenic variants than other methods. Additionally, we studied the genetic connection between developmental disorders and cancer. We found that in developmental disorder patients predicted deleterious de novo mutations are more enriched in cancer driver genes than non cancer driver genes. A Hidden Markov Model was implemented to discover cancer somatic missense mutation hotspots and we demonstrated many cancer driver genes shared a similar mode of action in developmental disorders and caner. By improving ability to interpret missense mutations and leveraging cancer genomics data, we can improve risk gene inference in developmental disorders