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

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.

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

International Journal of Yoga Therapy ◽

10.17761/2021-d-20-00005 ◽

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.

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Anxiety and Stress-Related Disorders and Mindfulness-Based Interventions: a Systematic Review and Multilevel Meta-analysis and Meta-Regression of Multiple Outcomes

Mindfulness ◽

10.1007/s12671-018-1058-1 ◽

2018 ◽

Vol 10 (6) ◽

pp. 996-1005 ◽

Cited By ~ 16

Author(s):

Marianna de Abreu Costa ◽

Gabriela Santos D’Alò de Oliveira ◽

Tiago Tatton-Ramos ◽

Gisele Gus Manfro ◽

Giovanni Abrahão Salum

Keyword(s):

Systematic Review ◽

Meta Analysis ◽

Multiple Outcomes ◽

Meta Regression

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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

Statistical Methods in Medical Research ◽

10.1177/0962280216688033 ◽

2017 ◽

Vol 27 (10) ◽

pp. 2885-2905 ◽

Cited By ~ 8

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).

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