scholarly journals Bayesian meta-analysis models for microarray data: a comparative study

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Erin M Conlon ◽  
Joon J Song ◽  
Anna Liu
Keyword(s):  
Comparative Study ◽  
Microarray Data ◽  
Meta Analysis ◽  
Analysis Models

Statistical Methods for Meta-Analysis of Microarray Data: A Comparative Study

2006 ◽  
Vol 8 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Pingzhao Hu ◽  
Celia M. T. Greenwood ◽  
Joseph Beyene
Keyword(s):  
Comparative Study ◽  
Statistical Methods ◽  
Microarray Data ◽  
Meta Analysis

Bayesian meta‐analysis models for cross cancer genomic investigation of pleiotropic effects using group structure

2020 ◽  
Author(s):  
Taban Baghfalaki ◽  
Pierre‐Emmanuel Sugier ◽  
Therese Truong ◽  
Anthony N. Pettitt ◽  
Kerrie Mengersen ◽  
...  
Keyword(s):  
Group Structure ◽  
Meta Analysis ◽  
Pleiotropic Effects ◽  
Analysis Models

scholarly journals P124 Are chronic pain syndromes associated with a unique cytokine profile? A systematic review and meta-analysis

Rheumatology ◽  
2021 ◽  
Vol 60 (Supplement_1) ◽  
Author(s):  
Luke Furtado O'Mahony ◽  
Arnav Srivastava ◽  
Puja Mehta ◽  
Coziana Ciurtin
Keyword(s):  
Systematic Review ◽  
Chronic Pain ◽  
Meta Analysis ◽  
Pooled Analysis ◽  
Cross Sectional ◽  
Pain Syndromes ◽  
Newcastle Ottawa Scale ◽  
Cytokine Milieu ◽  
Analysis Models ◽  
Chronic Pain Syndromes

Abstract Background/Aims  The aetiology of primary chronic pain syndromes (CPS) is highly disputed. One theory suggests that pain is due to a pro-inflammatory cytokine milieu leading to nociceptive activation. We performed a systematic review and meta-analysis aiming to assess differences in cytokines levels in CPS patients versus healthy controls (HC). Methods  Human studies published in English from PubMed, MEDLINE/Scopus and Cochrane databases were searched from inception up to January 2020. We included full text cross-sectional or longitudinal studies with cytokine measurements in CPS patients and HC. We excluded studies with underlying organic pathology. Quality assessment was completed using a modified version of the Newcastle-Ottawa Scale. Random-effects meta-analysis models were used to report pooled effects and 95% CIs. Study registered with PROSPERO (CRD42020193774). Results  Initial search yielded 324 papers, 36 studies (3229 participants) eligible for systematic review and 26 studies (2048 participants) suitable for metaanalysis. There were reproducible findings supporting trends of cytokine levels comparing CPS patients with HC. Eotaxin (chemokine) however was consistently raised in CPS. Meta-analysis showed significantly increased tumour necrosis factor (TNF) (SMD=0.39, p = 0.0009, %95I=0.16-0.63, p < 0.001; I2=70%, Q2 p < 0.001), interleukin (IL)-6 (SMD=0.15, 8 (SMD=0.26, p = 0.01, 95%CI =0.05-0.47; I2=61%, Q2 p = 0.005) and IL-10 (SMD=0.61; %95 = 0.34-0.89, p < 0.001; I2 = 10%, Q2 p = 0.34) in CPS compared to HC. Conclusion  We found significant differences in peripheral blood cytokine profiles of CPS patients compared to HC. However, the distinctive profile associated with CPS includes both pro-inflammatory (TNF-α, IL-6, IL-8), and anti-inflammatory cytokines (IL-10) in pooled analysis, as well as chemokine (eotaxin) signatures. Disclosure  L. Furtado O'Mahony: None. A. Srivastava: None. P. Mehta: None. C. Ciurtin: None.

Trauma-Sensitive Yoga Interventions and Posttraumatic Stress and Depression Outcomes Among Women: A Systematic Review and Analysis of Randomized Control Trials

2021 ◽  
Author(s):  
Ashleigh Kysar-Moon ◽  
Matthew Vasquez ◽  
Tierra Luppen
Keyword(s):  
Systematic Review ◽  
Posttraumatic Stress ◽  
Traumatic Event ◽  
Meta Analysis ◽  
Depression Symptoms ◽  
Randomized Control Trials ◽  
Methodological Rigor ◽  
History Of ◽  
Randomized Control ◽  
Analysis Models

Abstract Research shows that most people experience at least one traumatic event in their lifetimes, and between 6% and 8% of those with a history of trauma will develop posttraumatic stress disorder (PTSD) and/or related mental health conditions. Women face a greater threat of trauma exposure and have a higher risk of PTSD and depression than men. Trauma-Sensitive Yoga (TSY), a body-based adjunctive therapy, has shown potential in several studies as an effective method for reducing PTSD and depression symptoms. However, existing research and systematic reviews vary widely in their methodological rigor and comparison samples. Thus, in this systematic review we examined the effectiveness of TSY among women with a history of trauma and depression who had participated in randomized control trials with clear control and experimental groups. Findings in fixed- and mixed-effects meta-analysis models suggest marginally significant to no effects of TSY on PTSD and depression outcomes. Our systematic review highlights critical questions and significant gaps in the existing literature about the rationale and best practices of TSY intervention duration.

scholarly journals Coex-Rank: An approach incorporating co-expression information for combined analysis of microarray data

2012 ◽  
Vol 9 (1) ◽  
pp. 32-43 ◽  
Author(s):  
Jinlu Cai ◽  
Henry L. Keen ◽  
Curt D. Sigmund ◽  
Thomas L. Casavant
Keyword(s):  
Microarray Data ◽  
Statistical Power ◽  
Meta Analysis ◽  
Rank Aggregation ◽  
Microarray Data Analysis ◽  
Combined Approach ◽  
Linear Modeling ◽  
Modeling Process ◽  
Genome Wide ◽  
Gene Level

Summary Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Biologically related datasets from independent studies are publicly available, which requires robust combined approaches for integration and validation. Previously, meta-analysis has been adopted to solve this problem.As an alternative to meta-analysis, for microarray data with high similarity in biological experimental design, a more direct combined approach is possible. Gene-level normalization across datasets is motivated by the different scale and distribution of data due to separate origins. However, there has been limited discussion about this point in the past. Here we describe a combined approach for microarray analysis, including gene-level normalization and Coex-Rank approach. After normalization, a linear modeling process is used to identify lists of differentially expressed genes. The Coex-Rank approach incorporates co-expression information into a rank-aggregation procedure. We applied this computational approach to our data, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank approach from a biological perspective.Our combined approach for microarray data analysis (Coex-rank) is based on normalization, which is naturally driven. The Coex-rank process not only takes advantage of merging the power of multiple methods regarding normalization but also assists in the discovery of functional clusters of genes.

scholarly journals A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects

Biometrics ◽  
2017 ◽  
Vol 74 (2) ◽  
pp. 548-556 ◽  
Author(s):  
Dan Jackson ◽  
Sylwia Bujkiewicz ◽  
Martin Law ◽  
Richard D. Riley ◽  
Ian R. White
Keyword(s):  
Method Of Moments ◽  
Meta Analysis ◽  
Multiple Outcomes ◽  
Analysis Models

scholarly journals Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models

2017 ◽  
Vol 27 (10) ◽  
pp. 2885-2905 ◽  
Author(s):  
Richard D Riley ◽  
Joie Ensor ◽  
Dan Jackson ◽  
Danielle L Burke
Keyword(s):  
Meta Analysis ◽  
Information Matrix ◽  
Indirect Evidence ◽  
Parameter Estimates ◽  
Individual Participant Data ◽  
One Stage ◽  
Individual Participant ◽  
Meta Regression ◽  
Multiple Treatments ◽  
Analysis Models

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher’s information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Sign in / Sign up

Export Citation Format

Share Document