Browsing by Author "Kopciuk, Karen A."

Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemOpen Access
    A goodness-of-fit test for the bivariate necessary-but-not-sufficient relationship
    (2020-07-31) Ilagan, Michael John; de Leon, Alexander R.; Kopciuk, Karen A.; Ngamkham, Thuntida; Godley, Jenny
    In the social sciences, theory often casts bivariate relationships between constructs in terms of logical asymmetries. For example, in psychology, one theory is that intelligence is necessary but not sufficient for creativity. But as average-based linear models fail to accommodate nuances of logical asymmetries, a mismatch between theory and method is common in the literature. Recent methodological work proposed the Linear Ceiling and Floor Probability Region (LCFPR) model, which analyzes bivariate relationships in terms of necessity and sufficiency. However, an erroneous treatment of nested models and a lack of a formal goodness-of-fit test remain unaddressed in the LCFPR framework. In this thesis, I propose a goodness-of-fit test for LCFPR that addresses such shortcomings. A simulation study shows that, using a nonparametric quantile, the power and size of the test are largely acceptable. Analyses of real datasets demonstrate the proposed procedure. Conclusions and future directions are outlined in the final chapter.
  • Thumbnail Image
    ItemOpen Access
    Analysis of Misclassified Categorical Response via Incomplete Surrogate Variables and Likelihood Method
    (2020-12-20) Yu, Zheng; Shen, Hua; Shen, Hua; De Leon, Alexander R.; Kopciuk, Karen A.
    Misclassification of a dependent categorical variable often occurs in observational studies due to imperfect measuring procedures, and it may result in potential threats to the validity of the analytic results. We first investigate the consequences of naively ignoring the misclassification issue in response variable on parameter estimation using a range of naive methods and ad hoc methods. Then we develop a robust algorithm utilizing the surrogate variables to enable the estimation of the covariate effects in regression models under the framework of latent variable models in the absence of validation data. The resulting estimates are utilized in prediction and estimation of the average treatment effect (ATE). The estimation methods of ATE examined include outcome regression, G-computation, propensity score (PS) stratification, inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW). Variance estimation of ATE is obtained through bootstrap method. Moreover, we extend the algorithm to cope with the complication that some of the surrogate measurements are missing. Simulation studies represent of various scenarios are conducted to assess the performances of the proposed methods with a binary latent response variable. Based on the simulation studies, we show that the proposed method outperforms other approaches and corrects for both problems of misclassification and missingness simultaneously for a binary response variable, ensuring valid statistical inferences. An application to the stimulating study on breast cancer is given for illustration. Discussion and future work are outlined in the end.
  • Thumbnail Image
    ItemOpen Access
    Causal Inference with Mismeasured Confounders or Mediators
    (2021-09-23) Ren, Mingchen; De Leon, Alexander R.; Yan, Ying; Tekougang, Thierry Chekouo; Shen, Hua; Kopciuk, Karen A.; He, Wenqing
    This thesis includes three projects to correct measurement error in covariates or mediators when estimating causal estimands under survival model, marginal structure model and covariate balancing models. In Chapter 2, we decompose the causal effect on difference scale with more than one mediator under additive hazard model, and correct the bias caused by error-prone covariates and mediators. The simulation study shows the good performance of the proposed method under various measurement error settings. The method is further applied to a real data study of HIV-infected adults (Hammer et al., 1996), where a causal interpretation of the mediated effects is given. The asymptotic distributions of estimators are provided in the appendix. In Chapter 3, we develop two estimation methods to correct the bias of average treatment effect via marginal structural model when covariate variables are subject to measurement error. We consider the scenario that the confounders and exposures are time-varying and the confounders are error-prone. The first approach depends on a logistic-based correction method, which corrects the error-prone confounders in the logistic regression model of the treatment variable (Stefanski & Carroll, 1987). The second one relies on the simulation-extrapolation-based correction method (Shu & Yi, 2019d), which corrects the error-prone average treatment effect directly and could be used when a closed form of weight can not be found. Simulation studies are provided and the proposed approaches are illustrated by a real data analysis of the Women’s Interagency HIV Study in the United States from 1993 to 2015. In Chapter 4, when pretreatment covariates are subject to measurement error, we apply the augmented simulation extrapolation estimation developed by Shu and Yi (2019d) to correct the estimates of average treatment effect on the treated via entropy balancing and covariate balancing propensity score methods. The correction method is illustrated by a real data set.
  • Thumbnail Image
    ItemOpen Access
    Data Subset-Based Methods of Inference for Spatial Individual Level Epidemic Models
    (2023-08) Nyein, Thet Htet Chan; Deardon, Rob; Shen, Hua; Kopciuk, Karen A.
    Mathematical models are essential to understand infectious disease dynamics, enabling to control the spread of those diseases and preparing for public health measures. Since time and space are important factors affecting the transmission of infectious diseases, spatial individual-level models (ILM) with both temporal and spatial information are developed. Typically, Markov Chain Monte Carlo (MCMC) methods are utilized for the inference of ILM. Nonetheless, this approach can be computationally intensive for complex or large models, resulting in repeated likelihood calculations. This thesis explores various spatial and temporal subset methods to conduct statistical inference for spatial epidemic models, aiming to provide appropriate parameter estimates with minimum computational resources. In this thesis, we utilize the spatial ILM with the Euclidean distance between susceptible individuals and infectious individuals as a kernel function.
  • Thumbnail Image
    ItemOpen Access
    Using prior-data conflict to tune Bayesian regularized regression models
    (2023-09-22) Biziaev, Timofei; Chekouo Tekougang, Thierry; Kopciuk, Karen A.; Deardon, Rob; Evans, Michael
    In high-dimensional regression models, variable selection becomes challenging from a computational and theoretical perspective. Bayesian regularized regression via shrinkage priors like the Laplace or spike-and-slab prior are effective methods for variable selection in p > n scenarios provided the shrinkage priors are configured adequately. We propose configuring shrinkage priors using checks for prior-data conflict: tests that assess whether there is disagreement in parameter information provided by the prior and data. We apply our proposed method to the Bayesian LASSO and spike-and-slab shrinkage priors and assess variable selection performance of our prior configurations against competing models through a linear and logistic high-dimensional simulation study. Additionally, we apply our method to proteomic data collected from patients admitted to the Albany Medical Center in Albany NY in April of 2020 with COVID-like respiratory issues. Simulation results suggest our proposed configurations may outperform competing models when the true regression effects are small.