8 research outputs found
Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm
High-dimensional longitudinal data is increasingly used in a wide range of
scientific studies. However, there are few statistical methods for
high-dimensional linear mixed models (LMMs), as most Bayesian variable
selection or penalization methods are designed for independent observations.
Additionally, the few available software packages for high-dimensional LMMs
suffer from scalability issues. This work presents an efficient and accurate
Bayesian framework for high-dimensional LMMs. We use empirical Bayes estimators
of hyperparameters for increased flexibility and an
Expectation-Conditional-Minimization (ECM) algorithm for computationally
efficient maximum a posteriori probability (MAP) estimation of parameters. The
novelty of the approach lies in its partitioning and parameter expansion as
well as its fast and scalable computation. We illustrate Linear Mixed Modeling
with PaRtitiOned empirical Bayes ECM (LMM-PROBE) in simulation studies
evaluating fixed and random effects estimation along with computation time. A
real-world example is provided using data from a study of lupus in children,
where we identify genes and clinical factors associated with a new lupus
biomarker and predict the biomarker over time
The Intersection of Rural Residence and Minority Race/Ethnicity in Cancer Disparities in the United States
One in every twenty-five persons in America is a racial/ethnic minority who lives in a rural area. Our objective was to summarize how racism and, subsequently, the social determinants of health disproportionately affect rural racial/ethnic minority populations, provide a review of the cancer disparities experienced by rural racial/ethnic minority groups, and recommend policy, research, and intervention approaches to reduce these disparities. We found that rural Black and American Indian/Alaska Native populations experience greater poverty and lack of access to care, which expose them to greater risk of developing cancer and experiencing poorer cancer outcomes in treatment and ultimately survival. There is a critical need for additional research to understand the disparities experienced by all rural racial/ethnic minority populations. We propose that policies aim to increase access to care and healthcare resources for these communities. Further, that observational and interventional research should more effectively address the intersections of rurality and race/ethnicity through reduced structural and interpersonal biases in cancer care, increased data access, more research on newer cancer screening and treatment modalities, and continued intervention and implementation research to understand how evidence-based practices can most effectively reduce disparities among these populations
The Intersection of Rural Residence and Minority Race/Ethnicity in Cancer Disparities in the United States
One in every twenty-five persons in America is a racial/ethnic minority who lives in a rural area. Our objective was to summarize how racism and, subsequently, the social determinants of health disproportionately affect rural racial/ethnic minority populations, provide a review of the cancer disparities experienced by rural racial/ethnic minority groups, and recommend policy, research, and intervention approaches to reduce these disparities. We found that rural Black and American Indian/Alaska Native populations experience greater poverty and lack of access to care, which expose them to greater risk of developing cancer and experiencing poorer cancer outcomes in treatment and ultimately survival. There is a critical need for additional research to understand the disparities experienced by all rural racial/ethnic minority populations. We propose that policies aim to increase access to care and healthcare resources for these communities. Further, that observational and interventional research should more effectively address the intersections of rurality and race/ethnicity through reduced structural and interpersonal biases in cancer care, increased data access, more research on newer cancer screening and treatment modalities, and continued intervention and implementation research to understand how evidence-based practices can most effectively reduce disparities among these populations
Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm
Bayesian variable selection methods are powerful techniques for fitting and
inferring on sparse high-dimensional linear regression models. However, many
are computationally intensive or require restrictive prior distributions on
model parameters. In this paper, we proposed a computationally efficient and
powerful Bayesian approach for sparse high-dimensional linear regression.
Minimal prior assumptions on the parameters are used through the use of plug-in
empirical Bayes estimates of hyperparameters. Efficient maximum a posteriori
(MAP) estimation is completed through a Parameter-Expanded
Expectation-Conditional-Maximization (PX-ECM) algorithm. The PX-ECM results in
a robust computationally efficient coordinate-wise optimization, which adjusts
for the impact of other predictor variables. The completion of the E-step uses
an approach motivated by the popular two-groups approach to multiple testing.
The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to
sparse high-dimensional linear regression, which can be completed using
one-at-a-time or all-at-once type optimization. We compare the empirical
properties of PROBE to comparable approaches with numerous simulation studies
and an analysis of cancer cell lines drug response study. The proposed approach
is implemented in the R package probe
Heteroscedastic sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm
Sparse linear regression methods for high-dimensional data often assume that
residuals have constant variance. When this assumption is violated, it can lead
to bias in estimated coefficients, prediction intervals (PI) with improper
length, and increased type I errors. We propose a heteroscedastic
high-dimensional linear regression model through a partitioned empirical Bayes
Expectation Conditional Maximization (H-PROBE) algorithm. H-PROBE is a
computationally efficient maximum a posteriori estimation approach based on a
Parameter-Expanded Expectation-Conditional-Maximization algorithm. It requires
minimal prior assumptions on the regression parameters through plug-in
empirical Bayes estimates of hyperparameters. The variance model uses a
multivariate log-Gamma prior on coefficients that can incorporate covariates
hypothesized to impact heterogeneity. The motivation of our approach is a study
relating Aphasia Quotient (AQ) to high-resolution T2 neuroimages of brain
damage in stroke patients. AQ is a vital measure of language impairment and
informs treatment decisions, but it is challenging to measure and subject to
heteroscedastic errors. It is, therefore, of clinical importance -- and the
goal of this paper -- to use high-dimensional neuroimages to predict and
provide PIs for AQ that accurately reflect the heterogeneity in residual
variance. Our analysis demonstrates that H-PROBE can use markers of
heterogeneity to provide narrower PI widths than standard methods without
sacrificing coverage. Through extensive simulation studies, we exhibit that
H-PROBE results in superior prediction, variable selection, and predictive
inference than competing methods