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Integrated Multiple Mediation Analysis: A Robustness–Specificity Trade-Off In Causal Structure, An-Shun Tai, Sheng-Hsuan Lin 2020 Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan.

Integrated Multiple Mediation Analysis: A Robustness–Specificity Trade-Off In Causal Structure, An-Shun Tai, Sheng-Hsuan Lin

Harvard University Biostatistics Working Paper Series

Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and ...


Sensitivity Analysis For Incomplete Data And Causal Inference, Heng Chen 2020 Southern Methodist University

Sensitivity Analysis For Incomplete Data And Causal Inference, Heng Chen

Statistical Science Theses and Dissertations

In this dissertation, we explore sensitivity analyses under three different types of incomplete data problems, including missing outcomes, missing outcomes and missing predictors, potential outcomes in \emph{Rubin causal model (RCM)}. The first sensitivity analysis is conducted for the \emph{missing completely at random (MCAR)} assumption in frequentist inference; the second one is conducted for the \emph{missing at random (MAR)} assumption in likelihood inference; the third one is conducted for one novel assumption, the ``sixth assumption'' proposed for the robustness of instrumental variable estimand in causal inference.


Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model, Lauren A. Sugden 2020 Duquesne University

Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model, Lauren A. Sugden

Biology and Medicine Through Mathematics Conference

No abstract provided.


Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim 2020 Washington University in St. Louis

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

Engineering and Applied Science Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is ...


Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever 2020 HCA Healthcare

Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever

HCA Healthcare Journal of Medicine

This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.


Survival Mediation Analysis With The Death-Truncated Mediator: The Completeness Of The Survival Mediation Parameter, An-Shun Tai, Chun-An Tsai, Sheng-Hsuan Lin 2020 National Chiao Tung University

Survival Mediation Analysis With The Death-Truncated Mediator: The Completeness Of The Survival Mediation Parameter, An-Shun Tai, Chun-An Tsai, Sheng-Hsuan Lin

Harvard University Biostatistics Working Paper Series

In medical research, the development of mediation analysis with a survival outcome has facilitated investigation into causal mechanisms. However, studies have not discussed the death-truncation problem for mediators, the problem being that conventional mediation parameters cannot be well-defined in the presence of a truncated mediator. In the present study, we systematically defined the completeness of causal effects to uncover the gap, in conventional causal definitions, between the survival and nonsurvival settings. We proposed three approaches to redefining the natural direct and indirect effects, which are generalized forms of the conventional causal effects for survival outcomes. Furthermore, we developed three statistical ...


Recruitment Strategies For Cognitively Impaired Older Adults In Assisted Living Communities, Paige Greer, Elizabeth Hill, Katelyn Ware 2020 Grand Valley State University

Recruitment Strategies For Cognitively Impaired Older Adults In Assisted Living Communities, Paige Greer, Elizabeth Hill, Katelyn Ware

Student Scholars Day Posters

It is well documented that recruiting persons with dementia for research in long term care settings is challenging (Lam, et. al. 2018). The purpose of this study is to explore recruitment techniques suggested by the National Institute on Aging (2018), including the use of brochures, community contact introductions (CCI), presentations, event tables, 1:1 interactions and activity events. We examined the success of each method of recruitment in two recruitment waves based on the number recruited in relation to the number of hours spent on that recruitment method. Of the 119 people that were screened, 47% were enrolled in the ...


Evaluation Of Pre-Processing On The Meta-Analysis Of Dna Methylation Data From The Illumina Humanmethylation450 Beadchip Platform, Claudia Sala, Pietro Di Lena, Danielle Fernandes Durso, Andrea Prodi, Gastone Castellani, Christine Nardini 2020 University of Bologna

Evaluation Of Pre-Processing On The Meta-Analysis Of Dna Methylation Data From The Illumina Humanmethylation450 Beadchip Platform, Claudia Sala, Pietro Di Lena, Danielle Fernandes Durso, Andrea Prodi, Gastone Castellani, Christine Nardini

Infectious Diseases and Immunology Publications

INTRODUCTION: Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values -obtained after computational manipulation- are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability.

MATERIAL AND ...


Simulation-Based Power And Sample Size Calculation For Designing Interrupted Time Series Analyses Of Count Outcomes In Evaluation Of Health Policy Interventions, Wei Liu, Shangyuan Ye, Bruce A. Barton, Melissa A. Fischer, Colleen Lawrence, Elizabeth J. Rahn, Maria I. Danila, Kenneth G. Saag, Paul A. Harris, Stephenie C. Lemon, Jeroan J. Allison, Bo Zhang 2020 Harbin Institute of Technology

Simulation-Based Power And Sample Size Calculation For Designing Interrupted Time Series Analyses Of Count Outcomes In Evaluation Of Health Policy Interventions, Wei Liu, Shangyuan Ye, Bruce A. Barton, Melissa A. Fischer, Colleen Lawrence, Elizabeth J. Rahn, Maria I. Danila, Kenneth G. Saag, Paul A. Harris, Stephenie C. Lemon, Jeroan J. Allison, Bo Zhang

Population and Quantitative Health Sciences Publications

Objective: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies.

Methods: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series ...


Asymptotic Simultaneous Estimations For Contrasts Of Quantiles, Lawrence Sethor Segbehoe, Frank Schaarschmidt, Gemechis Dilba Djira 2020 Institute of Biostatistics, Leibniz Universitat Hannover

Asymptotic Simultaneous Estimations For Contrasts Of Quantiles, Lawrence Sethor Segbehoe, Frank Schaarschmidt, Gemechis Dilba Djira

SDSU Data Science Symposium

Although the expected value is popular, many researches in the health and social sciences involve skewed distributions and inferences concerning quantiles. Most standard multiple comparison procedures require the normality assumption. For example, few methods exist for comparing the medians of independent samples or quantiles of several distributions in general. To our knowledge, there is no general-purpose method for constructing simultaneous confidence intervals for multiple contrasts of quantiles. In this paper, we develop an asymptotic method for constructing such intervals and extend the idea to that of time-to-event data in survival analysis. Small-sample performance of the proposed method is assessed in ...


Estimating Marginal Hazard Ratios By Simultaneously Using A Set Of Propensity Score Models: A Multiply Robust Approach, Di Shu, Peisong Han, Rui Wang, Sengwee Toh 2020 Harvard Pilgrim Health Care Institute

Estimating Marginal Hazard Ratios By Simultaneously Using A Set Of Propensity Score Models: A Multiply Robust Approach, Di Shu, Peisong Han, Rui Wang, Sengwee Toh

Harvard University Biostatistics Working Paper Series

The inverse probability weighted Cox model is frequently used to estimate marginal hazard ratios. Its validity requires a crucial condition that the propensity score model is correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to ...


Estimation Of Conditional Power For Cluster-Randomized Trials With Interval-Censored Endpoints, Kaitlyn Cook, Rui Wang 2020 Harvard University

Estimation Of Conditional Power For Cluster-Randomized Trials With Interval-Censored Endpoints, Kaitlyn Cook, Rui Wang

Harvard University Biostatistics Working Paper Series

Cluster-randomized trials (CRTs) of infectious disease preventions often yield correlated, interval-censored data: dependencies may exist between observations from the same cluster, and event occurrence may be assessed only at intermittent clinic visits. This data structure must be accounted for when conducting interim monitoring and futility assessment for CRTs. In this article, we propose a flexible framework for conditional power estimation when outcomes are correlated and interval-censored. Under the assumption that the survival times follow a shared frailty model, we first characterize the correspondence between the marginal and cluster-conditional survival functions, and then use this relationship to semiparametrically estimate the cluster-specific ...


Randomization-Based Confidence Intervals For Cluster Randomized Trials, Dustin J. Rabideau, Rui Wang 2020 Harvard University

Randomization-Based Confidence Intervals For Cluster Randomized Trials, Dustin J. Rabideau, Rui Wang

Harvard University Biostatistics Working Paper Series

In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires randomization testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this paper, we propose a general method for ...


Predicting Diabetes Diagnoses, Sarah Netchert 2020 Misericordia University

Predicting Diabetes Diagnoses, Sarah Netchert

Student Research Poster Presentations 2020

This study explored the traits and health state of African Americans in central Virginia in order to determine what traits put people at a higher probability of being diagnosed with diabetes. We also want to know which traits will generate the highest probability a person will be diagnosed with diabetes. Traits that were included and used in this study were cholesterol, stabilized glucose, high density lipoprotein levels, age(years), gender, height(inches), weight(pounds), systolic blood pressure, diastolic blood pressure, waist size(inches), and hip size(inches). There were 403 individuals included in study since they were only ones screened ...


Lifestyle Factors And Social Determinants As Predictors Of Weight/Body Mass Index, Uthman Alhaji Baba 2020 Walden University

Lifestyle Factors And Social Determinants As Predictors Of Weight/Body Mass Index, Uthman Alhaji Baba

Walden Dissertations and Doctoral Studies

Obesity is a major public health concern that includes the risk of developing cardiovascular disease and premature death in adults. Previous studies have established the relationship between gender, educational level, household income and respondents’ weight but additional research is needed to factor the nature of education in relation to gender differences, diet, and other important behavioral mediators such as social determinants. The purpose of this quantitative cross-sectional study is to determine the extent to which frequency of physical activity, household income, social determinants of health (money for balanced meals, finances at the end of month, and poor mental health), respondent ...


An Assessment Of Convergence In The Feeding Morphology Of Xiphactinus Audax And Megalops Atlanticus Using Landmark-Based Geometric Morphometrics, Edward Chase Shelburne 2020 Fort Hays State University

An Assessment Of Convergence In The Feeding Morphology Of Xiphactinus Audax And Megalops Atlanticus Using Landmark-Based Geometric Morphometrics, Edward Chase Shelburne

Master's Theses

Convergence is an evolutionary phenomenon wherein distantly related organisms independently develop features or functional adaptations to overcome similar environmental constraints. Historically, convergence among organisms has been speculated or asserted with little rigorous or quantitative investigation. More recent advancements in systematics has allowed for the detection and study of convergence in a phylogenetic context, but this does little to elucidate convergent anatomical features in extinct taxa with poorly understood evolutionary histories. The purpose of this study is to investigate one potentially convergent system—the feeding structure of Xiphactinus audax (Teleostei: Ichthyodectiformes) and Megalops atlanticus (Teleostei: Elopiformes)—using a comparative anatomical approach ...


Generalized Matrix Decomposition Regression: Estimation And Inference For Two-Way Structured Data, Yue Wang, Ali Shojaie, Tim Randolph, Jing Ma 2019 University of Washington

Generalized Matrix Decomposition Regression: Estimation And Inference For Two-Way Structured Data, Yue Wang, Ali Shojaie, Tim Randolph, Jing Ma

UW Biostatistics Working Paper Series

Analysis of two-way structured data, i.e., data with structures among both variables and samples, is becoming increasingly common in ecology, biology and neuro-science. Classical dimension-reduction tools, such as the singular value decomposition (SVD), may perform poorly for two-way structured data. The generalized matrix decomposition (GMD, Allen et al., 2014) extends the SVD to two-way structured data and thus constructs singular vectors that account for both structures. While the GMD is a useful dimension-reduction tool for exploratory analysis of two-way structured data, it is unsupervised and cannot be used to assess the association between such data and an outcome of ...


Inference Of Heterogeneity In Meta-Analysis Of Rare Binary Events And Rss-Structured Cluster Randomized Studies, Chiyu Zhang 2019 Southern Methodist University

Inference Of Heterogeneity In Meta-Analysis Of Rare Binary Events And Rss-Structured Cluster Randomized Studies, Chiyu Zhang

Statistical Science Theses and Dissertations

This dissertation contains two topics: (1) A Comparative Study of Statistical Methods for Quantifying and Testing Between-study Heterogeneity in Meta-analysis with Focus on Rare Binary Events; (2) Estimation of Variances in Cluster Randomized Designs Using Ranked Set Sampling.

Meta-analysis, the statistical procedure for combining results from multiple studies, has been widely used in medical research to evaluate intervention efficacy and safety. In many practical situations, the variation of treatment effects among the collected studies, often measured by the heterogeneity parameter, may exist and can greatly affect the inference about effect sizes. Comparative studies have been done for only one or ...


Statistical Inference For Networks Of High-Dimensional Point Processes, Xu Wang, Mladen Kolar, Ali Shojaie 2019 University of Washington - Seattle Campus

Statistical Inference For Networks Of High-Dimensional Point Processes, Xu Wang, Mladen Kolar, Ali Shojaie

UW Biostatistics Working Paper Series

Fueled in part by recent applications in neuroscience, high-dimensional Hawkes process have become a popular tool for modeling the network of interactions among multivariate point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work have only focused on estimation. To bridge this gap, this paper proposes a high-dimensional statistical inference procedure with theoretical guarantees for multivariate Hawkes process. Key to this inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarizes the entire history of the process. We apply this ...


Statistical Methods For Estimating And Testing Treatment Effect For Multiple Treatment Groups In Observational Studies., Xiaofang Yan 2019 University of Louisville

Statistical Methods For Estimating And Testing Treatment Effect For Multiple Treatment Groups In Observational Studies., Xiaofang Yan

Electronic Theses and Dissertations

Note: Abstract would not save due to an issue with some of the characters.


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