scholarly journals Temporal variation in lymphocyte proteomics

2021 ◽  
Author(s):  
Michaela A McCown ◽  
Carolyn Allen ◽  
Daniel D Machado ◽  
Hannah Boekweg ◽  
Yiran Liang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Small Sample ◽  
Lymphocytic Leukemia ◽  
Small Samples ◽  
Tumor Evolution ◽  
Multiple Time ◽  
Time Points ◽  
Asymptomatic Stage ◽  
Multiple Time Points ◽  
Over Time

Chronic Lymphocytic Leukemia (CLL) is a slow progressing disease, characterized by a long asymptomatic stage followed by a symptomatic stage during which patients receive treatment. While proteomic studies have discovered differential pathways in CLL, the proteomic evolution of CLL during the asymptomatic stage has not been studied. In this pilot study, we show that by using small sample sizes comprising ~145 cells, we can detect important features of CLL necessary for studying tumor evolution. Our small samples are collected at two time points and reveal large proteomic changes in healthy individuals over time. A meta-analysis of two CLL proteomic papers showed little commonality in differentially expressed proteins and demonstrates the need for larger control populations sampled over time. To account for proteomic variability between time points and individuals, large control populations sampled at multiple time points are necessary for understanding CLL progression. Data is available via ProteomeXchange with identifier PXD027429.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

Predictors of healthcare use in community women with eating disorder symptoms: a longitudinal study

2021 ◽  
pp. 103985622110092
Author(s):  
Nicol Holtzhausen ◽  
Phillipa Hay ◽  
Nasim Foroughi ◽  
Haider Mannan
Keyword(s):  
Mental Health ◽  
Eating Disorder ◽  
Psychological Distress ◽  
Symptom Severity ◽  
Multiple Time ◽  
Healthcare Use ◽  
Defense Style ◽  
Time Points ◽  
Multiple Time Points ◽  
Over Time

Objectives This study aimed to investigate associations between eating disorder mental health literacy (EDMHL), defense style, eating disorder (ED) symptom severity, psychological distress and mental-health-related quality of life (MHRQoL) and the likelihood of formal and informal healthcare use (HCU) across multiple time points. Methods A community sample of 445 young women with ED symptoms were followed over 7 years. Questionnaires were distributed via email and postal mail across multiple time points; this study includes data from years 2 (baseline in this study), 4 and 9. The inclusion criteria was provision of HCU data at year 2. Results ED symptom severity at baseline was significantly associated with greater HCU two and seven years later. Accurate identification of an ED by participants (i.e. EDMHL) at baseline was associated with greater HCU seven years later. Defense style, psychological distress, MHRQoL and other aspects of EDMHL were not significantly associated with HCU over time. Conclusions Individuals with more severe ED symptoms, and with greater EDMHL, may be more likely to seek help over time. However, individuals with EDs may not seek help directly for poorer MHRQoL and higher levels of psychological distress. This reinforces the importance of ED screening, particularly in primary care settings.

scholarly journals Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0164898 ◽  
Author(s):  
Alfred Musekiwa ◽  
Samuel O. M. Manda ◽  
Henry G. Mwambi ◽  
Ding-Geng Chen
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Meta Analysis ◽  
Effect Sizes ◽  
General Linear ◽  
Multiple Time ◽  
Time Points ◽  
Multiple Time Points ◽  
General Linear Mixed Model

scholarly journals Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

2021 ◽  
Vol 13 (15) ◽  
pp. 3042
Author(s):  
Kateřina Gdulová ◽  
Jana Marešová ◽  
Vojtěch Barták ◽  
Marta Szostak ◽  
Jaroslav Červenka ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Synergistic Effects ◽  
Canopy Height ◽  
Surface Model ◽  
Multiple Time ◽  
Central European ◽  
Time Points ◽  
Bohemian Forest ◽  
Multiple Time Points ◽  
Surrounding Areas

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

THE ROLE OF MEMORY PERSPECTIVE IN SOCIAL PHOBIA: IMMEDIATE AND DELAYED MEMORIES FOR ROLE-PLAYED SITUATIONS

2002 ◽  
Vol 30 (4) ◽  
pp. 415-425 ◽  
Author(s):  
Meredith E. Coles ◽  
Cynthia L. Turk ◽  
Richard G. Heimberg
Keyword(s):  
Social Phobia ◽  
Multiple Time ◽  
Behavioral Models ◽  
Social Situations ◽  
Time Points ◽  
Cognitive Behavioral Models ◽  
Multiple Time Points ◽  
Memory Perspective ◽  
Field Perspective

Cognitive-behavioral models (Clark & Wells, 1995; Rapee & Heimberg, 1997) and recent research suggest that individuals with social phobia (SP) experience both images (Hackmann, Surawy, & Clark, 1998) and memories (Coles, Turk, Heimberg, & Fresco, 2001; Wells, Clark, & Ahmad, 1998) of anxiety-producing social situations from an observer perspective. The current study examines memory perspective for two role-played situations (speech and social interaction) at multiple time points (immediate and 3 weeks post) in 22 individuals with generalized SP and 30 non-anxious controls (NACs). At both time points, SPs recalled the role-plays from a more observer/less field perspective than did NACs. Further, over time, the memory perspective of SPs became even more observer/less field while the memory perspective of NAC remained relatively stable.

The Silicon Peaches

2021 ◽  
pp. 31-42
Author(s):  
Dan Breznitz
Keyword(s):  
Information And Communication Technologies ◽  
Silicon Valley ◽  
Communication Technologies ◽  
Multiple Time ◽  
Rapid Rise ◽  
Time Points ◽  
Start Up ◽  
Information And Communication ◽  
Multiple Time Points ◽  
Start Ups

This chapter acknowledges that, for many regions, the idea of attracting cutting-edge tech start-ups is almost irresistible. Seemingly every community aspires to become the next Silicon Valley. But is that feasible? This chapter make these lessons concrete by elaborating on the rapid rise and, even faster and deeper, decline of America’s first Silicon Valley—Cleveland, Ohio. It then shows the near impossibility of trying to become the next Silicon Valley by analyzing the mysterious failure of Atlanta, Georgia—a city that diligently followed all the advice ever given to an aspiring new start-up hub, but somehow was always left only with the “potential.” We will see how at multiple time-points Atlanta’s companies were the leading innovators with the best products in the newest information and communication technologies (ICT), only to falter and be taken over by Silicon Valley companies without leaving any apparent impact on the region. It then brings in social-network research and the concept of embeddedness to explain why trying to recreate a Silicon Valley is a doomed (and expensive) enterprise.

scholarly journals Bayesian splines versus fractional polynomials in network meta-analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Andreas Heinecke ◽  
Marta Tallarita ◽  
Maria De Iorio
Keyword(s):  
Meta Analysis ◽  
Indirect Comparison ◽  
Real Data ◽  
Multiple Time ◽  
Computationally Efficient ◽  
Fractional Polynomials ◽  
Time Points ◽  
Model Based ◽  
Multiple Treatments ◽  
Indirect Comparisons

Abstract Background Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. Methods In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. Results We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. Conclusions The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

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