scholarly journals A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio

2018 ◽  
Vol 37 (7) ◽  
pp. 1059-1085 ◽  
Author(s):  
Dan Jackson ◽  
Martin Law ◽  
Theo Stijnen ◽  
Wolfgang Viechtbauer ◽  
Ian R. White
Keyword(s):  
Random Effects ◽  
Odds Ratio ◽  
Random Effects Models ◽  
Meta Analyses ◽  
Summary Odds Ratio

scholarly journals Accuracy and Feasibility of Clinically Applied Frailty Instruments before Surgery

2020 ◽  
Vol 133 (1) ◽  
pp. 78-95 ◽  
Author(s):  
Sylvie D. Aucoin ◽  
Mike Hao ◽  
Raman Sohi ◽  
Julia Shaw ◽  
Itay Bentov ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Primary Outcome ◽  
Cochrane Library ◽  
Patient Centered ◽  
Clinical Frailty Scale ◽  
Frailty Phenotype ◽  
Random Effects Models ◽  
The Individual ◽  
Meta Analyses ◽  
Instrument Level

Background A barrier to routine preoperative frailty assessment is the large number of frailty instruments described. Previous systematic reviews estimate the association of frailty with outcomes, but none have evaluated outcomes at the individual instrument level or specific to clinical assessment of frailty, which must combine accuracy with feasibility to support clinical practice. Methods The authors conducted a preregistered systematic review (CRD42019107551) of studies prospectively applying a frailty instrument in a clinical setting before surgery. Medline, Excerpta Medica Database, Cochrane Library and the Comprehensive Index to Nursing and Allied Health Literature, and Cochrane databases were searched using a peer-reviewed strategy. All stages of the review were completed in duplicate. The primary outcome was mortality and secondary outcomes reflected routinely collected and patient-centered measures; feasibility measures were also collected. Effect estimates were pooled using random-effects models or narratively synthesized. Risk of bias was assessed. Results Seventy studies were included; 45 contributed to meta-analyses. Frailty was defined using 35 different instruments; five were meta-analyzed, with the Fried Phenotype having the largest number of studies. Most strongly associated with: mortality and nonfavorable discharge was the Clinical Frailty Scale (odds ratio, 4.89; 95% CI, 1.83 to 13.05 and odds ratio, 6.31; 95% CI, 4.00 to 9.94, respectively); complications was associated with the Edmonton Frail Scale (odds ratio, 2.93; 95% CI, 1.52 to 5.65); and delirium was associated with the Frailty Phenotype (odds ratio, 3.79; 95% CI, 1.75 to 8.22). The Clinical Frailty Scale had the highest reported measures of feasibility. Conclusions Clinicians should consider accuracy and feasibility when choosing a frailty instrument. Strong evidence in both domains support the Clinical Frailty Scale, while the Fried Phenotype may require a trade-off of accuracy with lower feasibility. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

scholarly journals Prognostic Role of Matrix Metalloproteinases in Cervical Cancer: A Meta-Analysis

2021 ◽  
Vol 28 ◽  
pp. 107327482110337
Author(s):  
Weiwei Chen ◽  
Shenjiao Huang ◽  
Kun Shi ◽  
Lisha Yi ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Matrix Metalloproteinases ◽  
Random Effects ◽  
Fixed Effects ◽  
Prognostic Value ◽  
Meta Analysis ◽  
Free Survival ◽  
Random Effects Models ◽  
Registration Number ◽  
Meta Analyses

Objective Studies have published the association between the expression of matrix metalloproteinases (MMPs) and the outcome of cervical cancer. However, the prognostic value in cervical cancer remains controversial. This meta-analysis was conducted to evaluate the prognostic functions of MMP expression in cervical cancer. Methods A comprehensive search of PubMed, Embase, and Web of Science databases was conducted to identify the eligible studies according to defined selection and excluding criteria and analyzed according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Fixed and random effects models were evaluated through the hazard ratios (HRs) and 95% confidence intervals (CIs) to estimate the overall survival (OS), recurrence-free survival (RFS), and progress-free survival (PFS). Results A total of 18 eligible studies including 1967 patients were analyzed for prognostic value. Totally 16 selected studies including 21 tests were relevant to the cervical cancer OS, 4 studies focused on RFS, and 1 study on PFS. The combined pooled HRs and 95% CIs of OS were calculated with random-effects models (HR = 1.64, 95% CI = 1.01–2.65, P = .000). In the subgroup analysis for OS, there was no heterogeneity in MMP-2 (I2 = .0%, P = .880), MMP-1 (I2 = .0%, P = .587), and MMP-14 (I2 = 28.3%, P = .248). In MMP-7 and MMP-9, the heterogeneities were obvious (I2 = 99.2% ( P = .000) and I2 = 77.9% ( P = .000), respectively). The pooled HRs and 95% CIs of RFS were calculated with fixed-effects models (HR = 2.22, 95% CI = 1.38–3.58, P = .001) and PFS (HR = 2.29, 95% CI = 1.14–4.58, P = .035). Conclusions The results indicated that MMP overexpression was associated with shorter OS and RFS in cervical cancer patients. It suggested that MMP overexpression might be a poor prognostic marker in cervical cancer. Research Registry Registration Number: reviewregistry 1159.

Triclosan-Coated Sutures Reduce the Risk of Surgical Site Infections: A Systematic Review and Meta-analysis

2015 ◽  
Vol 36 (2) ◽  
pp. 169-179 ◽  
Author(s):  
Anucha Apisarnthanarak ◽  
Nalini Singh ◽  
Aila Nica Bandong ◽  
Gilbert Madriaga
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Random Effects ◽  
Meta Analysis ◽  
Surgical Site Infections ◽  
Sensitivity Analyses ◽  
Publication Type ◽  
Random Effects Models ◽  
Meta Regression ◽  
Meta Analyses

OBJECTIVETo analyze available evidence on the effectiveness of triclosan-coated sutures (TCSs) in reducing the risk of surgical site infection (SSI).DESIGNSystematic review and meta-analysis.METHODSA systematic search of both randomized (RCTs) and nonrandomized (non-RCT) studies was performed on PubMed Medline, OVID, EMBASE, and SCOPUS, without restrictions in language and publication type. Random-effects models were utilized and pooled estimates were reported as the relative risk (RR) ratio with 95% confidence interval (CI). Tests for heterogeneity as well as meta-regression, subgroup, and sensitivity analyses were performed.RESULTSA total of 29 studies (22 RCTs, 7 non-RCTs) were included in the meta-analysis. The overall RR of acquiring an SSI was 0.65 (95% CI: 0.55–0.77; I2=42.4%, P=.01) in favor of TCS use. The pooled RR was particularly lower for the abdominal surgery group (RR: 0.56; 95% CI: 0.41–0.77) and was robust to sensitivity analysis. Meta-regression analysis revealed that study design, in part, may explain heterogeneity (P=.03). The pooled RR subgroup meta-analyses for randomized controlled trials (RCTs) and non-RCTs were 0.74 (95% CI: 0.61–0.89) and 0.53 (95% CI: 0.42–0.66), respectively, both of which favored the use of TCSs.CONCLUSIONThe random-effects meta-analysis based on RCTs suggests that TCSs reduced the risk of SSI by 26% among patients undergoing surgery. This effect was particularly evident among those who underwent abdominal surgery.Infect Control Hosp Epidemiol 2015;36(2): 1–11

scholarly journals Prediction intervals for random-effects meta-analysis: A confidence distribution approach

2018 ◽  
Vol 28 (6) ◽  
pp. 1689-1702 ◽  
Author(s):  
Kengo Nagashima ◽  
Hisashi Noma ◽  
Toshi A Furukawa
Keyword(s):  
Random Effects ◽  
Prediction Interval ◽  
Meta Analysis ◽  
Prediction Intervals ◽  
Point Estimate ◽  
Random Effects Models ◽  
Confidence Distribution ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Meta Analyses

Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins–Thompson–Spiegelhalter prediction interval, replaces the heterogeneity parameter with its point estimate, but its validity strongly depends on a large sample approximation. This is a weakness in meta-analyses with few studies. We propose an alternative based on bootstrap and show by simulations that its coverage is close to the nominal level, unlike the Higgins–Thompson–Spiegelhalter method and its extensions. The proposed method was applied in three meta-analyses.

scholarly journals Stereotactic radiosurgery for non-functioning pituitary adenomas: meta-analysis and International Stereotactic Radiosurgery Society practice opinion

2019 ◽  
Vol 22 (3) ◽  
pp. 318-332 ◽  
Author(s):  
Rupesh Kotecha ◽  
Arjun Sahgal ◽  
Muni Rubens ◽  
Antonio De Salles ◽  
Laura Fariselli ◽  
...  
Keyword(s):  
Systematic Review ◽  
Stereotactic Radiosurgery ◽  
Random Effects ◽  
Local Control ◽  
Pituitary Adenomas ◽  
Meta Analysis ◽  
Random Effects Models ◽  
Review Reports ◽  
Meta Analyses ◽  
Non Functioning Pituitary Adenomas

Abstract Background This systematic review reports on outcomes and toxicities following stereotactic radiosurgery (SRS) for non-functioning pituitary adenomas (NFAs) and presents consensus opinions regarding appropriate patient management. Methods Using the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, a systematic review was performed from articles of ≥10 patients with NFAs published prior to May 2018 from the Medline database using the key words “radiosurgery” and “pituitary” and/or “adenoma.” Weighted random effects models were used to calculate pooled outcome estimates. Results Of the 678 abstracts reviewed, 35 full-text articles were included describing the outcomes of 2671 patients treated between 1971 and 2017 with either single fraction SRS or hypofractionated stereotactic radiotherapy (HSRT). All studies were retrospective (level IV evidence). SRS was used in 27 studies (median dose: 15 Gy, range: 5–35 Gy) and HSRT in 8 studies (median total dose: 21 Gy, range: 12–25 Gy, delivered in 3–5 fractions). The 5-year random effects local control estimate after SRS was 94% (95% CI: 93.0–96.0%) and 97.0% (95% CI: 93.0–98.0%) after HSRT. The 10-year local control random effects estimate after SRS was 83.0% (95% CI: 77.0–88.0%). Post-SRS hypopituitarism was the most common treatment-related toxicity observed, with a random effects estimate of 21.0% (95% CI: 15.0–27.0%), whereas visual dysfunction or other cranial nerve injuries were uncommon (range: 0–7%). Conclusions SRS is an effective and safe treatment for patients with NFAs. Encouraging short-term data support HSRT for select patients, and mature outcomes are needed before definitive recommendations can be made. Clinical practice opinions were developed on behalf of the International Stereotactic Radiosurgery Society (ISRS).

scholarly journals Meta-Analysis of Sensorimotor Gating Deficits in Patients With Schizophrenia Evaluated by Prepulse Inhibition Test

2020 ◽  
Vol 46 (6) ◽  
pp. 1482-1497
Author(s):  
Rodrigo San-Martin ◽  
Leonardo Andrade Castro ◽  
Paulo Rossi Menezes ◽  
Francisco José Fraga ◽  
Priscyla Waleska Simões ◽  
...  
Keyword(s):  
Prepulse Inhibition ◽  
Random Effects ◽  
Effect Size ◽  
Meta Analysis ◽  
Sensorimotor Gating ◽  
Control Group ◽  
Random Effects Models ◽  
Meta Regression Analysis ◽  
High Level ◽  
Meta Analyses

Abstracts Prepulse inhibition (PPI) of startle is an operational measure of sensorimotor gating that is often impaired in patients with schizophrenia. Despite the large number of studies, there is considerable variation in PPI outcomes reported. We conducted a systematic review and meta-analysis investigating PPI impairment in patients with schizophrenia compared with healthy control subjects, and examined possible explanations for the variation in results between studies. Major databases were screened for observational studies comparing healthy subjects and patients with schizophrenia for the prepulse and pulse intervals of 60 and 120 ms as primary outcomes, ie, PPI-60 and PPI-120. Standardized mean difference (SMD) and 95% confidence intervals (CI) were extracted and pooled using random effects models. We then estimated the mean effect size of these measures with random effects meta-analyses and evaluated potential PPI heterogeneity moderators, using sensitivity analysis and meta-regressions. Sixty-seven primary studies were identified, with 3685 healthy and 4290 patients with schizophrenia. The schizophrenia group showed reduction in sensorimotor gating for both PPI-60 (SMD = −0.50, 95% CI = [−0.61, −0.39]) and PPI-120 (SMD = −0.44, 95% CI = [−0.54, −0.33]). The sensitivity and meta-regression analysis showed that sample size, gender proportion, imbalance for gender, source of control group, and study continent were sources of heterogeneity (P < .05) for both PPI-60 and PPI-120 outcomes. Our findings confirm a global sensorimotor gating deficit in schizophrenia patients, with overall moderate effect size for PPI-60 and PPI-120. Methodological consistency should decrease the high level of heterogeneity of PPI results between studies.

Random-effects meta-analysis: the number of studies matters

2015 ◽  
Vol 26 (3) ◽  
pp. 1500-1518 ◽  
Author(s):  
Annamaria Guolo ◽  
Cristiano Varin
Keyword(s):  
Random Effects ◽  
Type I Error ◽  
Meta Analysis ◽  
Practical Interest ◽  
Error Rates ◽  
Type I ◽  
Model Framework ◽  
Random Effects Models ◽  
Meta Analyses ◽  
The Impact

This paper investigates the impact of the number of studies on meta-analysis and meta-regression within the random-effects model framework. It is frequently neglected that inference in random-effects models requires a substantial number of studies included in meta-analysis to guarantee reliable conclusions. Several authors warn about the risk of inaccurate results of the traditional DerSimonian and Laird approach especially in the common case of meta-analysis involving a limited number of studies. This paper presents a selection of likelihood and non-likelihood methods for inference in meta-analysis proposed to overcome the limitations of the DerSimonian and Laird procedure, with a focus on the effect of the number of studies. The applicability and the performance of the methods are investigated in terms of Type I error rates and empirical power to detect effects, according to scenarios of practical interest. Simulation studies and applications to real meta-analyses highlight that it is not possible to identify an approach uniformly superior to alternatives. The overall recommendation is to avoid the DerSimonian and Laird method when the number of meta-analysis studies is modest and prefer a more comprehensive procedure that compares alternative inferential approaches. R code for meta-analysis according to all of the inferential methods examined in the paper is provided.

scholarly journals The risk of being culpable for or involved in a road crash after using cannabis: A systematic review and meta-analyses

2021 ◽  
Vol 7 ◽  
pp. 205032452110553
Author(s):  
Michael A. White ◽  
Nicholas R. Burns
Keyword(s):  
Odds Ratio ◽  
Meta Analysis ◽  
Case Control ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Directional Bias ◽  
Increased Risk ◽  
Meta Analyses ◽  
Summary Odds Ratio ◽  
Average Measure

Background The development of drug driving policies should rest on sound epidemiological evidence as to the crash risks of driving after using psychoactive drugs. The findings from individual studies of the increased risk of crashing from the acute use of cannabis range in size from no increase (and perhaps even a protective effect) to a 10-fold increase. Coherent cannabis-driving policies cannot readily be developed from such an incoherent evidence base. A weighted average measure of risk, as provided by a meta-analysis, might be useful. However, if the range of risks found in the cannabis-crash studies reflects the different ways that a variety of biases are being expressed, then the simple application of a meta-analysis might provide little more than an average measure of bias. In other words, if the biases were predominantly inflationary, the meta-analysis would give an inflated estimate of crash risk; and if the biases were predominantly deflationary, the meta-analysis would give a deflated estimate of risk. Review We undertook a systematic search of electronic databases, and identified 13 culpability studies and 4 case–control studies from which cannabis-crash odds ratios could be extracted. Random-effects meta-analyses gave summary odds ratios of 1.37 (1.10–1.69) for the culpability studies and 1.45 (0.94–2.25) for the case–control studies. A tool was designed to identify and score biases arising from: confounding by uncontrolled covariates; inappropriate selection of cases and controls; and the inappropriate measurement of the exposure and outcome variables. Each study was scrutinised for the presence of those biases, and given a total ‘directional bias score’. Most of the biases were inflationary. A meta-regression against the total directional bias scores was performed for the culpability studies, giving a bias-adjusted summary odds ratio of 0.68 (0.45–1.05). The same analysis could not be performed for the case–control studies because there were only four such studies. Nonetheless, a monotonic relationship was found between the total bias scores and the cannabis-crash odds ratios, with Spearman's rho  =  0.95, p  =  0.05, indicating that the summary odds ratio of 1.45 is an overestimate. It is evident that the risks from driving after using cannabis are much lower than from other behaviours such as drink-driving, speeding or using mobile phones while driving. With the medical and recreational use of cannabis becoming more prevalent, the removal of cannabis-presence driving offences should be considered (while impairment-based offences would remain).

Moment and Least-Squares Based Approaches to Meta-analytic Inference

2013 ◽  
Author(s):  
Michael S. Rosenberg
Keyword(s):  
Least Squares ◽  
Random Effects ◽  
Meta Analysis ◽  
Structural Models ◽  
Sums Of Squares ◽  
Random Effects Models ◽  
Weighted Means ◽  
The Moment ◽  
The One ◽  
Meta Analyses

Chapter 8 introduced variance and structural models and various statistical inference approaches used in meta-analysis. This chapter describes the basic details behind the moment and least-squares approach to meta-analysis. This approach represents “classic” meta-analysis; it is the one most frequently found in meta-analytic introductions and used in ecological meta-analyses to date. This approach to meta-analytic inference has the advantage of using fairly simple formulas (for basic structural models) that can be easily calculated, and it is clearly and directly comparable to common statistical concepts, such as weighted means and sums of squares. The disadvantages of this approach is that it is less amenable to more complex modeling, particularly when considering features such as interaction effects; it also has certain limitations that in some cases reduce the applicability of random-effects models.

scholarly journals The Application of Meta-Analytic Models with Multiple Random Effects: A Systematic Review

2019 ◽  
Author(s):  
Belén Fernández ◽  
Laleh Jamshidi ◽  
Lies Declercq ◽  
S. Natasha Beretvas ◽  
Patrick Onghena ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Structure ◽  
Random Effects ◽  
Multilevel Models ◽  
Meta Analysis ◽  
Simulation Studies ◽  
Random Effects Models ◽  
Sophisticated Model ◽  
Meta Analyses ◽  
Study Participants

In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as a model with a random study effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models or cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of three-level meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified random effects models are not often used although they might account better for the meta-analytic data structure of the analyzed datasets. Also, we have found that the simulation studies done on multilevel meta-analysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed and further suggestions are given.

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