Identifying Qualitative Between-Subject and Within-Subject Variability: A Method for Clustering Regime-Switching Dynamics.
Ontology highlight
ABSTRACT:
SUBMITTER: Ou L
PROVIDER: S-EPMC7287315 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Identifying Qualitative Between-Subject and Within-Subject Variability: A Method for Clustering Regime-Switching Dynamics.
Ou Lu L Andrade Alejandro A Alberto Rosa A RA Bakker Arthur A Bechger Timo T
Frontiers in psychology 20200604
Technological advancement provides an unprecedented amount of high-frequency data of human dynamic processes. In this paper, we introduce an approach for characterizing qualitative between and within-subject variability from quantitative changes in the multi-subject time-series data. We present the statistical model and examine the strengths and limitations of the approach in potential applications using Monte Carlo simulations. We illustrate its usage in characterizing clusters of dynamics with ...[more]
Similar Datasets
Project description:Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials.
| S-EPMC7913178 | biostudies-literature
Project description:In psychology, studying multivariate dynamical processes within a person is gaining ground. An increasingly often used method is vector autoregressive (VAR) modeling, in which each variable is regressed on all variables (including itself) at the previous time points. This approach reveals the temporal dynamics of a system of related variables across time. A follow-up question is how to analyze data of multiple persons in order to grasp similarities and individual differences in within-person dynamics. We focus on the case where these differences are qualitative in nature, implying that subgroups of persons can be identified. We present a method that clusters persons according to their VAR regression weights, and simultaneously fits a shared VAR model to all persons within a cluster. The performance of the algorithm is evaluated in a simulation study. Moreover, the method is illustrated by applying it to multivariate time series data on depression-related symptoms of young women.
| S-EPMC5054011 | biostudies-literature
Project description:With the growing availability of high-resolution imaging there has been increased interest in developing new metrics for integrity of the retinal nerve fiber layer. In particular, it has been suggested that measurement of width of retinal nerve fiber bundles (RNFBs) may be useful in glaucoma, due to low between-subject variability in mean RNFB width. However, there have also been reports of substantial within-subject variability in the width of individual RNFBs. To assess within-subject variability as a potential source of selection bias in measurements of RNFB width, we used an adaptive optics scanning laser ophthalmoscope (AOSLO) to measure widths of individual RNFBs in one eye each of 11 young adults in good ocular health. In a pilot study we analyzed a large AOSLO image of RNFL in one participant then, based on those findings, in the main study we used AOSLO to image a smaller region in 10 additional healthy young adults. The pilot study of one eye found RNFB widths ranging from 10 μm to 44 μm. This suggested that biological variability was too high for measuring small changes arising from disease processes. This was confirmed in measurements of 10 eyes in the main study, RNFB widths ranged from 9 μm to 55 μm and every eye had large within-subject variability (exceeding 19 μm in all eyes) in RNFB width for nearby bundles. The within-subject variability in RNFB width, as well as variation in the width of single RNFBs over relatively short distances (<300 um) depending on the precise location of measurement, suggests that bundle width measurements would be highly susceptible to selection bias and therefore of limited clinical use.
| S-EPMC6795422 | biostudies-literature
Project description:BackgroundThe QuantiFERON®-TB Gold In-Tube test (QFT-GIT) is a viable alternative to the tuberculin skin test (TST) for detecting Mycobacterium tuberculosis infection. However, within-subject variability may limit test utility. To assess variability, we compared results from the same subjects when QFT-GIT enzyme-linked immunosorbent assays (ELISAs) were performed in different laboratories.MethodsSubjects were recruited at two sites and blood was tested in three labs. Two labs used the same type of automated ELISA workstation, 8-point calibration curves, and electronic data transfer. The third lab used a different automated ELISA workstation, 4-point calibration curves, and manual data entry. Variability was assessed by interpretation agreement and comparison of interferon-? (IFN-?) measurements. Data for subjects with discordant interpretations or discrepancies in TB Response >0.05 IU/mL were verified or corrected, and variability was reassessed using a reconciled dataset.ResultsNinety-seven subjects had results from three labs. Eleven (11.3%) had discordant interpretations and 72 (74.2%) had discrepancies >0.05 IU/mL using unreconciled results. After correction of manual data entry errors for 9 subjects, and exclusion of 6 subjects due to methodological errors, 7 (7.7%) subjects were discordant. Of these, 6 (85.7%) had all TB Responses within 0.25 IU/mL of the manufacturer's recommended cutoff. Non-uniform error of measurement was observed, with greater variation in higher IFN-? measurements. Within-subject standard deviation for TB Response was as high as 0.16 IU/mL, and limits of agreement ranged from -0.46 to 0.43 IU/mL for subjects with mean TB Response within 0.25 IU/mL of the cutoff.ConclusionGreater interlaboratory variability was associated with manual data entry and higher IFN-? measurements. Manual data entry should be avoided. Because variability in measuring TB Response may affect interpretation, especially near the cutoff, consideration should be given to developing a range of values near the cutoff to be interpreted as "borderline," rather than negative or positive.
| S-EPMC3435391 | biostudies-literature
Project description:When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset, just after the onset of the N1 peak. Interestingly, single trial N1 latency correlated significantly with reaction time, while N1 amplitude did not. In conclusion, our findings suggest that inter-trial variability in the timing of extrastriate visual processing contributes to reaction time variability.
| S-EPMC4860412 | biostudies-literature
Project description:The present study evaluated diurnal variations and day-to-day fluctuations of salivary oxidative stress (OS) markers in healthy adult individuals. Whole unstimulated saliva was collected at 2 time intervals over 3 consecutive days. Glutathione peroxidase (GPX), superoxide dismutase (SOD), total antioxidant capacity (TAC), and uric acid (UA) were analyzed using spectrophotometric methods, while 8-hydroxydeoxyguanosine (8-OHdG) and malondialdehyde (MDA) were determined using immunoassays. No significant differences for salivary OS markers between men and women were observed. For all examined OS markers, no significant day-to-day variations were demonstrated. Significant diurnal variations were found in salivary GPX, TAC and MDA levels. For SOD, TAC, GPX, and UA, good-to-moderate intraindividual coefficients of variations (CVs) were observed in more than 75% of the subjects. For MDA and 8-OHdG, intraindividual CVs?>?35% were observed in 60% and 40% of the subjects, respectively. Between-subject variance was wide for all examined OS markers (CV% 30.08%-85.70%). Due to high intraindividual variability in the salivary concentrations of MDA and 8-OHdG, those markers cannot be reliably verified based on single measurements and multiple measurements over several days would provide more reliable information. Salivary SOD, TAC, GPX, and UA proved stable across three days of measurement. Trial Registration. ClinicalTrials.gov NCT03029494. Registered on 2017-01-19.
| S-EPMC5705883 | biostudies-literature
Project description:We implement an allocation strategy through a regime-switching model using recursive utility preferences in an out-of-sample exercise accounting for transaction costs. We study portfolios turnover and leverage, proposing two procedures to constrain the allocation strategies: a low-turnover control (LoT) and a maximum leverage control (MaxLev). LoT sets a dynamic threshold to trim minor rebalancing, reducing portfolio turnover, mitigating costs. MaxLev calculates dynamic adjustments to the risk aversion parameter to constrain the portfolio leverage. The MaxLev adjustments depend on the risk aversion and permitted portfolio leverage, which enables optimal strategies considering the leverage constraints. The study uses US equity portfolios, and shows that, first, models with LoT result in superior return-to-risk measures than those without it when transaction costs increase. Second, considering transaction costs, the return-to-risk measures of the models using MaxLev closely match or exceed those from the corresponding unconstrained regime-switching benchmarks. Third, MaxLev returns have lower volatility and higher return-to-risk than conventional numerically constrained benchmarks. Fourth, the certainty equivalent returns indicate that models using MaxLev and LoT outperform both single-state models and unconstrained regime-switching models with statistical significance.Supplementary informationThe online version contains supplementary material available at 10.1007/s11408-022-00414-x.
| S-EPMC9243879 | biostudies-literature
Project description:A novel approach to recover and identify immune complexes (ICs) was developed using size exclusion chromatography (SEC) and affinity chromatography on immunoglobulin binding columns (HiTrap Protein G). The purification process was monitored by 1D SDS-PAGE, protein staining, Western blotting and, finally, liquid chromatography tandem mass spectrometry (LC MS/MS) was used to identify the recovered antigens. This approach was applied to serum with artificially created immune complexes (ICs) comprising vaccine antigen (influenza) and antibody, which led to recovery and identification of influenza peptides within the recovered ICs. This approach was compared with the established method for IC detection and recovery, polyethylene glycol (PEG) precipitation, followed by LC MS/MS. Both approaches successfully enabled capture, recovery and characterization of immunoglobulins and influenza antigen(s) in complex with the immunoglobulins. However, PEG precipitation has the advantage of simplicity and is more suited for large scale studies.
| S-EPMC7757895 | biostudies-literature
Project description:To assess the prognostic significance of blood pressure (BP) variability, we followed health outcomes in a family-based random population sample representative of the general population (n=2944; mean age: 44.9 years; 50.7% women). At baseline, BP was measured 5 times consecutively at each of 2 home visits 2 to 4 weeks apart. We assessed within-subject overall (10 readings), within- and between-visit systolic BP variability from variability independent of the mean, the difference between maximum and minimum BP, and average real variability. Over a median follow-up of 12 years, 401 deaths occurred and 311 participants experienced a fatal or nonfatal cardiovascular event. Overall systolic BP variability averaged (SD) 5.45 (2.82) units, 15.87 (8.36) mmHg, and 4.08 (2.05) mmHg for variability independent of the mean, difference between maximum and minimum BP, and average real variability, respectively. Female sex, older age, higher-mean systolic BP, lower body mass index, a history of peripheral arterial disease, and use of ?-blockers were the main correlates of systolic BP variability. In multivariable-adjusted analyses, overall and within- and between-visit BP variability did not predict total or cardiovascular mortality or the composite of any fatal plus nonfatal cardiovascular end point. For instance, the hazard ratios for all cardiovascular events combined in relation to overall variability independent of the mean, difference between maximum and minimum BP, and average real variability were 1.05 (0.96-1.15), 1.06 (0.96-1.16), and 1.08 (0.98-1.19), respectively. By contrast, mean systolic BP was a significant predictor of all end points under study, independent of BP variability. In conclusion, in an unbiased population sample, BP variability did not contribute to risk stratification over and beyond mean systolic BP.
| S-EPMC3607229 | biostudies-literature
Project description:Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.
| S-EPMC2909310 | biostudies-other