Bayesian Causal Inferencee In Meta-Analysis

University of Minnesota Ph.D. dissertation. May 2019. Major: Biostatistics. Advisor: Haitao Chu. 1 computer file (PDF); xi, 113 pages.While the randomized clinical trial (RCT) is the gold standard for investigating the effect of a medical intervention, noncompliance to assigned treatments can threaten a trial's validity. Noncompliance, if not appropriately controlled, can introduce substantial bias into the estimate of treatment effect. The complier average causal effect (CACE) approach provides a useful tool for addressing noncompliance, where CACE measures the effect of an intervention in the latent subgroup of the study population that complies with its assigned treatment (the compliers). Meta-analysis of RCTs has become a widely-used statistical technique to combine and contrast results from multiple independent studies. However, no existing methods can effectively deal with heterogeneous noncompliance in meta-analysis of RCTs. For example, the commonly used meta-analysis regression methods investigate the impact of study-level variables (e.g., mean age of the study population) on the study-specific treatment effect size by assuming the study-level covariates to be fixed. However, noncompliance rates generally differ between treatment groups within a study and are commonly considered as random rather than fixed post-randomization variables. In addition, noncompliance may dynamically interact with the primary outcome and thus affect the response to treatment. Thus, meta-regression methods are not suitable to controlling for noncompliance. This thesis focuses on developing Bayesian methods to estimate CACE in meta-analysis of RCTs with binary or ordinal outcomes. Bayesian hierarchical random effects models are developed to appropriately account for the inherent heterogeneity in treatment effect and noncompliance between studies and treatment groups. We first present a Bayesian hierarchical model to estimate the CACE where heterogeneous compliance rates are available for each study. Second, we extend our approach to deal with incomplete noncompliance when some RCTs do not report noncompliance data. The results are illustrated by a re-analysis of a meta-analysis comparing the effect of epidural analgesia in labor versus no or other analgesia in labor on the outcome cesarean section, where noncompliance varies substantially between studies. Simulations are performed to evaluate the performance of the proposed approach and to illustrate the importance of including appropriate random effects by showing the impact of over- and under-fitting. Furthermore, we develop an R package, BayesCACE, to provide user-friendly functions to implement CACE analysis for binary outcomes based on the proposed Bayesian hierarchical models. This package includes flexible functions for analyzing data from a single RCT and from a meta-analysis of multiple RCTs with either complete or incomplete noncompliance data. The package also provides various functions for generating forest, trace, posterior density, and auto-correlation plots, and to review noncompliance rates, visually assess the model, and obtain study-specific and overall CACEs

A Bayesian approach to estimating causal vaccine effects on binary post-infection outcomes

To estimate causal effects of vaccine on post-infection outcomes, Hudgens and Halloran (2006) defined a post-infection causal vaccine efficacy estimand VEI based on the principal stratification framework. They also derived closed forms for the maximum likelihood estimators (MLEs) of the causal estimand under some assumptions. Extending their research, we propose a Bayesian approach to estimating the causal vaccine effects on binary post-infection outcomes. The identifiability of the causal vaccine effect VEI is discussed under different assumptions on selection bias. The performance of the proposed Bayesian method is compared with the maximum likelihood method through simulation studies and two case studies — a clinical trial of a rotavirus vaccine candidate and a field study of pertussis vaccination. For both case studies, the Bayesian approach provided similar inference as the frequentist analysis. However, simulation studies with small sample sizes suggest that the Bayesian approach provides smaller bias and shorter confidence interval length

DBMLoc: a Database of proteins with multiple subcellular localizations

<p>Abstract</p> <p>Background</p> <p>Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.</p> <p>Description</p> <p>We have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website.</p> <p>Conclusion</p> <p>DBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at <url>http://www.bioinfo.tsinghua.edu.cn/DBMLoc/index.htm</url>.</p

Strength Retention Of Single-phase High-entropy Diboride Ceramics Up To 2000°C

The mechanical properties of single-phase (Hf0.2,Nb0.2,Ta0.2,Ti0.2,Zr0.2)B2 ceramics with high purity were investigated. The resulting ceramics had relative density greater than 99%, and an average grain size of 4.3 ± 1.6 μm. At room temperature (RT), the Vickers hardness was 25.2 ± 0.6 GPa at a load of 0.49 N, Young\u27s modulus was 551 ± 7 GPa, fracture toughness was 4.5 ± 0.4 MPa m1/2, and flexural strength was 507 ± 10 MPa. Flexural strength increased by more than 50% from 507 ± 10 MPa at RT to 776 ± 26 MPa at 1400°C. Strength remained above 750 MPa up to 2000°C, but decreased to 672 ± 18 MPa at 2100°C and the bars deformed during testing. No significant changes in residual porosity, average grain size, or oxide impurity levels were observed after testing at elevated temperatures. The increase in strength at elevated temperatures was attributed to healing of microcracks due to thermal expansion at high temperatures, and dislocation formation. The retention of strength up to 2000°C is presumably due to the lack of oxide impurities in the HEBs. This is the first reported study on the flexural strength up to 2100°C of dense and pure HEB ceramics

A big data study of language use and impact in radio broadcasting in China

Broadcasting more educating and language-reviving contents are ways radio stations can help revitalize the use of the English language in the Hunan province of China. The challenges faced in communicating in English in Chinese radio stations are majorly caused by the lack of language professionals and linguists in the broadcast stations. The absence of these professionals is a major constraint to the development of the community. The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon. Using the English language during broadcast reduces language dearth, and helps reach a much larger audience, even those not in China. Programmes anchored in English in places where the language is barely spoken enhances the vocabulary, comprehension and language vitality of the listeners. This study examined the impact of the English language used in radio broadcasting using a descriptive Big Data survey research design. The study’s population comprises of the inhabitants of the Hunan province in China, from which a sample of 50 broadcast staff and 150 regular inhabitants was drawn using a stratified random sampling technique. The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview. The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data. The results revealed that majority of the respondents in Hunan listened to radio broadcast indicating that the use of English language can have massive impact on the people. The study also found that majority of the respondents use their indigenous languages in their day-to-day activities as well as their schools with English being used majorly only in schools with only English-speaking students. The study recommends, amongst others, that the Broadcasting Corporation of China (BCC) review their policy on the allocated time of broadcast in English languages, and that more English language experts and linguists should be incorporated into the broadcast system

Fintech applications on banking stability using big data of an emerging economy.

The rapid growth and development of financial technological advancement (Fintech) services and innovations have attracted the attention of scholars who are now on a quest to analyse their impact on the banking sector. This study conducts several kinds of analyses to measure the effect of the fintech era on the stability of the Chinese banking sector. It uses Big Data and performs Pearson correlation and regression analysis on the fintech era's transition period to measure the impact of several explanatory variables— institutional regulation, government stability, bank credit to deposit ratio, and economic growth— on the outcome variables, which includes Nonperforming loans (NPLs) and its numerical measurement in relation to the mean score of the Big Data (Z-score). This study uses yearly Big Data from 1995-2018 and revealed that compared to the first wave of the fintech era, the second wave helped in the reduction of NPLs and the enhancement of financial stability in China. This study concludes that in the second wave of the fintech era, the explanatory variables mentioned above had a positive impact on NPLs and banking stability. This work helps comprehend fintech development in modern society and the importance of its disruptive forces in developing and developed countries