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- Causal inference (3)
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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Estimation Of Causal Effects In Complex Clustered Data, Joshua R. Nugent
Estimation Of Causal Effects In Complex Clustered Data, Joshua R. Nugent
Doctoral Dissertations
Analysis of clustered data from randomized trials or observational data often poses theoretical and practical statistical challenges, including but not limited to small numbers of independent units, many adjustment variables, continuous exposures, and/or differential clustering across trial arms. Further, commonly-used parametric methods rely on assumptions that may be violated in practice. Motivated by three scientific questions in public health, methods are developed and/or demonstrated for non-parametric estimation of causal effects. In Chapter 1, methods are elaborated for a cluster randomized trial (CRT) with missing individual-level data at baseline and follow-up, a complex sampling strategy, and limited number of clusters. Chapter …
Applications Of Statistical Physics To Ecology: Ising Models And Two-Cycle Coupled Oscillators, Vahini Reddy Nareddy
Applications Of Statistical Physics To Ecology: Ising Models And Two-Cycle Coupled Oscillators, Vahini Reddy Nareddy
Doctoral Dissertations
Many ecological systems exhibit noisy period-2 oscillations and, when they are spatially extended, they undergo phase transition from synchrony to incoherence in the Ising universality class. Period-2 cycles have two possible phases of oscillations and can be represented as two states in the bistable systems. Understanding the dynamics of ecological systems by representing their oscillations as bistable states and developing dynamical models using the tools from statistical physics to predict their future states is the focus of this thesis. As the ecological oscillators with two-cycle behavior undergo phase transitions in the Ising universality class, many features of synchrony and equilibrium …
Bayesian Hierarchical Temporal Modeling And Targeted Learning With Application To Reproductive Health, Herbert P. Susmann
Bayesian Hierarchical Temporal Modeling And Targeted Learning With Application To Reproductive Health, Herbert P. Susmann
Doctoral Dissertations
The international community via the United Nations Sustainable Development Goals has set the target of universal access to reproductive health-care services, including family planning, by 2030. Progress towards reaching this goal is assessed by tracking appropriate demographic and health indicators at national and subnational levels. This task is challenging, however, in populations where relevant data are limited or of low quality. Statistical models are then needed to estimate and project demographic and health indicators in populations based on the available data. Our first contribution, in Chapter 1, is to unify many existing demographic and health indicator models by proposing an …
Statistical Methods To Study Transposon Sequencing Data: Nonparametric Bayesian Models With Sampling Algorithms, Shai He
Doctoral Dissertations
As the development of Next Generation Sequencing(NGS) technology, researchers can easily obtain data from millions of cells( bulk samples) or just collecting data from a single cell. However, while bulk samples can capture broad changes, it may risk providing an average measurement that is not representative of the genetic state of any individual cell. While single-cell experiments can capture the genetic state of the individual cell, a single cell sample can increase uncertainty, sampling enough cells to gain a representative sample of population is expensive. Therefore, there is a need to integrate information from both bulk and single-cell data to …
Three Dimensional Spatio-Temporal Cluster Analysis Of Sars-Cov-2 Infections, Keith W. Allison
Three Dimensional Spatio-Temporal Cluster Analysis Of Sars-Cov-2 Infections, Keith W. Allison
Masters Theses
The COVID-19 pandemic has heightened the need for fine-scale analysis of the clustering of cases of infectious disease in order to better understand and prevent the localized spread of infection. The students living on the University of Massachusetts, Amherst campus provided a unique opportunity to do so, due to frequent mandatory testing during the 2020-2021 academic year, and dense living conditions. The South-West dormitory area is of particular interest due to its extremely high population density, housing around half of students living on campus during normal conditions. Using data gathered by the Public Health Promotion Center (PHPC), we analyzed the …
Gaussian Graphical Models For Omics Data: New Methodology And Applications, Katherine H. Shutta
Gaussian Graphical Models For Omics Data: New Methodology And Applications, Katherine H. Shutta
Doctoral Dissertations
Gaussian graphical models (GGMs) are useful network estimation tools for modeling direct dependencies that characterize multivariate data. The GGM modeling framework is one way to elucidate complex systems-level properties that can be difficult to detect in univariate analyses. In this dissertation, we begin by presenting a tutorial and review of the current state of the field of GGM theory and application. Next, we present a motivating application of GGMs in a study of metabolomic networks associated with chronic distress in women in the Women's Health Initiative (WHI) and in the Nurses' Health Study cohorts. In the third chapter, we present …
Impact Of Loss To Follow-Up And Time Parameterization In Multiple-Period Cluster Randomized Trials And Assessing The Association Between Institution Affiliation And Journal Publication, Jonathan Moyer
Doctoral Dissertations
Difference-in-difference cluster randomized trials (CRTs) use baseline and post-test measurements. Standard power equations for these trials assume no loss to follow-up. We present a general equation for calculating treatment effect variance in difference-in-difference CRTs, with special cases assuming loss to follow-up with replacement of lost participants and loss to follow-up with no replacement but retaining the baseline measurements of all participants. Multiple-period CRTs can represent time as continuous using random coefficients (RC) or categorical using repeated measures ANOVA (RM-ANOVA) analytic models. Previous work recommends the use of RC over RM-ANOVA for CRTs with more than two periods because RC exhibited …
Methods To Improve Inference From Dependent Network Data, Dongah Kim
Methods To Improve Inference From Dependent Network Data, Dongah Kim
Doctoral Dissertations
Over the past decade, network research has increased dramatically. Network data are used in many fields because they contain not only covariates of each observation, but also `relationships' between observations. Therefore, statistical analysis of network data has been rapidly developed. However, network data presents many challenges, such as collecting network data, inferring the prevalence of an outcome of interest, and valid statistical testing typically with highly dependent data. The methods discussed in this thesis are developed to improve statistical inference from dependent network data.
A Cost-Effective Method To Passively Sample Communities At The Forest Canopy-Aerosphere Interface, Michael Cunningham-Minnick, H. Patrick Roberts, Brian Kane Ph.D., Joan Milam, David I. King Ph.D.
A Cost-Effective Method To Passively Sample Communities At The Forest Canopy-Aerosphere Interface, Michael Cunningham-Minnick, H. Patrick Roberts, Brian Kane Ph.D., Joan Milam, David I. King Ph.D.
Data and Datasets
HOBO logger data of hourly measurements at canopy-aerosphere interface from June to August above temperate forest on campus of University of Massachusetts. Weather station data (precipitation and wind speeds) from nearby weather station extracted from Mesowest.com and needed for manuscript figures. Code (R language) to recreate foundation of figures in manuscript.