Biology subjects

Crainiceanu, C.

Publications and source records attributed to Crainiceanu, C..

6 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↗

bioRxiv2021

Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study

SO_SCPLOWUMMARYC_SCPLOWAs health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We provide the first published analysis of tri-axial accelerometry data from Zio XT patch and introduce an extension of posture classification algorithms for use with ECG patches worn in the free-living environment. Our novel extensions to posture classification include (1) estimation of an upright posture for each individual without the reference measurements used by existing posture classification algorithms; (2) correction for device removal and re-positioning using novel spherical change-point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. Methods were built using data from 14 participants from the Multicenter AIDS Cohort Study (MACS), and applied to 1, 250 MACS participants. As no posture labels exist in the free-living environment, we evaluate the algorithm against labelled data from the Towson Accelerometer Study and against data labelled by hand from the MACS study.

bioengineering↗

bioRxiv2018

On statistical tests of functional connectome fingerprinting

Fingerprinting of functional connectomes is an increasingly standard measure of reproducibility in functional magnetic resonance imaging connectomics. In such studies, one attempts to match a subjects first session image with their second, in a blinded fashion, in a group of subjects measured twice. The number or percentage of correct matches is usually reported as a statistic. In this manuscript, we investigate the statistical tests of matching based on exchangeability assumption in the fingerprinting analysis. We show that a nearly universal Poisson(1) approximation applies for different matching schemes. We theoretically investigate the permutation tests and explore the issue that the test is overly sensitive to uninteresting directions in the alternative hypothesis, such as clustering due to familial status or demographics. We perform a numerical study on two functional magnetic resonance imaging (fMRI) resting state datasets, the Human Connectome Project (HCP) and the Baltimore Longitudinal Study of Aging (BLSA). These datasets are instructive, as the HCP includes techinical replications of long scans and includes monozygotic and dyzogotic twins as well as non-twin siblings. In contrast, the BLSA study incorporates more typical length resting state scans in a longitudinal study. Finally, a study of single regional connections is performed on the HCP data.

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↗

bioRxiv2018

The upstrap

Bootstrap [2] is a landmark method for quantifying variability. It uses sampling with replacement with a sample size equal to that of the original data. We propose the upstrap, which samples with replacement either more or fewer samples than the original sample size. We illustrate the upstrap by solving a hard, but common, sample size calculation problem.

epidemiology↗

bioRxiv2017

Testing Equality of Curves After Covariate Adjustment

SO_SCPLOWUMMARYC_SCPLOWThis paper is concerned with providing simple methodological approaches for global and local tests of difference between the mean of treatment and control groups when the measured outcome is a function. The added complexity is that for every subject we have repeated samples for the same curve and additional covariates of interest. We propose a permutation based approach to test for a global difference between the averages of two functional processes after covariate adjustment. The within group averages are estimated by modeling the relationship of the functional outcome on the covariate using functional regression methods and then averaging with respect to the covariate distribution in each group. The test statistic is the L2 area under the squared difference curve. We also test for the localized differences between the two average curves using a nonparametric bootstrap of subjects to obtain the 95% pointwise and joint confidence intervals for the estimated covariate-adjusted difference curve. Extensive simulation studies illustrate that the proposed tests preserve the type one error and are highly sensitive to detecting departures from the null assumption. We illustrate our method by studying the differences in time varying oxygen consumption between the frail Interleukin 10tm1Cgn (IL10tm) mice and the wildtype mice after adjusting for body composition measures.

epidemiology↗