Biology subjects

Karas, M.

Publications and source records attributed to Karas, M..

4 recordsLinked to original sources

bioRxiv2021

Upstrap for estimating power and sample size in complex models

Power and sample size calculation are major components of statistical analyses. The upstrap resampling method introduced by Crainiceanu and Crainiceanu (2018) was proposed as a general solution to this problem but has not been assessed in numerical experiments. We evaluate the power estimation properties of the upstrap for target data sets that are larger or smaller than the original observed data set. We also expand the scope of upstrap and propose a solution to estimate the power to detect: (1) an effect size observed in the original data; and (2) an effect size chosen by a researcher. Simulations include the following scenarios: one- and two-sample t-tests; linear regression with both Gaussian and binary outcomes; multilevel mixed effects models with both Gaussian and binary outcomes. In addition, our simulations consider cases where the distribution of a covariate in the target setting is preserved and when it is purposefully changed compared to the original data set. We illustrate the approach using a reanalysis of a cluster-randomized controlled trial of malaria transmission. The GitHub repository with R code used in manuscript analyses is available at https://git.io/J0TH1. The accompanying data are publicly available.

ecology↗

bioRxiv2018

Connectivity-Informed Adaptive Regularization for Generalized Outcomes

One of the challenging problems in the brain imaging research is a principled incorporation of information from different imaging modalities in association studies. Frequently, data from each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, griPEER (generalized ridgified Partially Empirical Eigenvectors for Regression) to estimate the association between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER provides a principled approach to use external information from the structural brain connectivity to improve the regression coefficient estimation. Our proposal incorporates a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. We address both theoretical and computational issues and show that our method is robust to the incomplete information about the structural brain connectivity. We also provide a significance testing procedure for performing inference on the estimated coefficients in this model. griPEER is evaluated in extensive simulation studies and it is applied in classification of the HIV+ and HIV- individuals.

neuroscience↗

bioRxiv2018

Accelerometry data in health research: challenges and opportunities. Review and examples

Wearable accelerometers provide detailed, objective, and continu-ous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popula-rity of wearable technology in health research. An ever increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper we discuss problems related to the collection and analysis of raw acce-lerometry data and provide insights into potential solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability and the effects of sensor location on the body. We also provide a short tutorial for dealing with sampling frequency, device calibration, data labeling and multiple PA monitors synchronization. We illustrate these po-ints using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.

epidemiology↗

bioRxiv2017

Brain connectivity-informed regularization methods for regression

A challenging problem arising in brain imaging research is principled incorporation of information from different imaging modalities. Frequently each modality is analyzed separately using, for instance, dimensionality reduction techniques which result in a loss of mutual information. We propose a novel regularization method to estimate the association between the brain structure features and a scalar outcome within the linear regression framework. Our regularization technique provides a principled approach to utilizing external information arising from the structural brain connectivity to inform the estimation of the regression coefficients. Our proposal extends the classical Tikhonov regularization framework by defining a penalty term based on the structural connectivity-derived Laplacian matrix. In the work presented, we address both theoretical and computational issues. The approach is illustrated using simulated data and compared with other penalized regression methods. Finally, we apply our regularization method to study the associations between the alcoholism phenotypes and brain cortical thickness using a diffusion tensor imaging (DTI) derived measure of structural connectivity.

neuroscience↗