Empirical assessment of prediction intervals in Cochrane meta‐analyses

2021 ◽  
Author(s):  
Fahad M. Al Amer ◽  
Lifeng Lin
Keyword(s):  
Prediction Intervals ◽  
Empirical Assessment ◽  
Meta Analyses

Clinical outcomes of cancer patients with COVID-19: A systematic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18600-e18600
Author(s):  
Maryam Alasfour ◽  
Salman Alawadi ◽  
Malak AlMojel ◽  
Philippos Apolinario Costa ◽  
Priscila Barreto Coelho ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Cancer Patients ◽  
Clinical Outcomes ◽  
Meta Analysis ◽  
Small Sample ◽  
Prediction Intervals ◽  
Invasive Ventilation ◽  
Newcastle Ottawa Scale ◽  
Small Sample Sizes ◽  
Meta Analyses

e18600 Background: Patients with coronavirus disease 2019 (COVID-19) and cancer have worse clinical outcomes compared to those without cancer. Primary studies have examined this population, but most had small sample sizes and conflicting results. Prior meta-analyses exclude most US and European data or only examine mortality. The present meta-analysis evaluates the prevalence of several clinical outcomes in cancer patients with COVID-19, including new emerging data from Europe and the US. Methods: A systematic search of PubMED, medRxiv, JMIR and Embase by two independent investigators included peer-reviewed papers and preprints up to July 8, 2020. The primary outcome was mortality. Other outcomes were ICU and non-ICU admission, mild, moderate and severe complications, ARDS, invasive ventilation, stable, and clinically improved rates. Study quality was assessed through the Newcastle–Ottawa scale. Random effects model was used to derive prevalence rates, their 95% confidence intervals (CI) and 95% prediction intervals (PI). Results: Thirty-four studies (N = 4,371) were included in the analysis. The mortality prevalence rate was 25.2% (95% CI: 21.1–29.7; 95% PI: 9.8-51.1; I 2 = 85.4), with 11.9% ICU admissions (95% CI: 9.2-15.4; 95% PI: 4.3-28.9; I 2= 77.8) and 25.2% clinically stable (95% CI: 21.1-29.7; 95% PI: 9.8-51.1; I 2 = 85.4). Furthermore, 42.5% developed severe complications (95% CI: 30.4-55.7; 95% PI: 8.2-85.9; I 2 = 94.3), with 22.7% developing ARDS (95% CI: 15.4-32.2; 95% PI: 5.8-58.6; I 2 = 82.4), and 11.3% needing invasive ventilation (95% CI: 6.7-18.4; 95% PI: 2.3-41.1; I 2 = 79.8). Post-follow up, 49% clinically improved (95% CI: 35.6-62.6; 95% PI: 9.8-89.4; I 2 = 92.5). All outcomes had large I 2 , suggesting high levels of heterogeneity among studies, and wide PIs indicating high variability within outcomes. Despite this variability, the mortality rate in cancer patients with COVID-19, even at the lower end of the PI (9.8%), is higher than the 2% mortality rate of the non-cancer with COVID-19 population, but not as high as what other meta-analyses conclude, which is around 25%. Conclusions: Patients with cancer who develop COVID-19 have a higher probability of mortality compared to the general population with COVID-19, but possibly not as high as previous studies have shown. A large proportion of them developed severe complications, but a larger proportion recovered. Prevalence of mortality and other outcomes published in prior meta-analyses did not report prediction intervals, which compromises the clinical utilization of such results.

scholarly journals Environmental Risk Factors and Congenital Heart Disease: An Umbrella Review of 165 Systematic Reviews and Meta-Analyses With More Than 120 Million Participants

2021 ◽  
Vol 8 ◽  
Author(s):  
Tie-Ning Zhang ◽  
Qi-Jun Wu ◽  
Ya-Shu Liu ◽  
Jia-Le Lv ◽  
Hui Sun ◽  
...  
Keyword(s):  
Risk Factors ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Environmental Factors ◽  
Congenital Heart ◽  
Prediction Intervals ◽  
Umbrella Review ◽  
Wide Range ◽  
Null Value ◽  
Meta Analyses

Background: The etiology of congenital heart disease (CHD) has been extensively studied in the past decades. Therefore, it is critical to clarify clear hierarchies of evidence between types of environmental factors and CHD.Methods: Electronic searches in PubMed, Embase, Web of Science, Cochrane database were conducted from inception to April 20, 2020 for meta-analyses investigating the aforementioned topic.Results: Overall, 41 studies including a total of 165 meta-analyses of different environmental factors and CHD were examined, covering a wide range of risk factors. The summary random effects estimates were significant at P < 0.05 in 63 meta-analyses (38%), and 15 associations (9%) were significant at P < 10−6. Of these meta-analyses, eventually one risk factor (severe obesity; relative risk: 1.38, 95% confidence interval: 1.30–1.47) had significant summary associations at P < 10−6, included more than 1,000 cases, had 95% prediction intervals excluding the null value, and were not suggestive of large heterogeneity (I2 < 50%), small-study effects (P-value for Egger's test > 0.10), or excess significance (P > 0.10). Eight associations (5%) (including maternal lithium exposure, maternal obesity, maternal alcohol consumption, and maternal fever) had results that were significant at P < 10−6, included more than 1,000 cases, and had 95% prediction intervals excluding the null value (highly suggestive).Conclusion: This umbrella review shows that many environmental factors have substantial evidence in relation to the risk of developing CHD. More and better-designed studies are needed to establish robust evidence between environmental factors and CHD.Systematic Review Registration: [PROSPERO], identifier [CRD42020193381].

Reporting of meta-analyses of randomized controlled trials with a focus on drug safety: An empirical assessment

2013 ◽  
Vol 10 (3) ◽  
pp. 389-397 ◽  
Author(s):  
Tarek A Hammad ◽  
George A Neyarapally ◽  
Simone P Pinheiro ◽  
Solomon Iyasu ◽  
George Rochester ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Drug Safety ◽  
Controlled Trials ◽  
Empirical Assessment ◽  
Randomized Controlled ◽  
Meta Analyses

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.

USING PREDICTION INTERVALS FROM RANDOM-EFFECTS META-ANALYSES IN AN ECONOMIC MODEL

2014 ◽  
Vol 30 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Conor Teljeur ◽  
Michelle O'Neill ◽  
Patrick Moran ◽  
Linda Murphy ◽  
Patricia Harrington ◽  
...  
Keyword(s):  
Random Effects ◽  
Treatment Effect ◽  
Meta Analysis ◽  
Clinical Effectiveness ◽  
Cost Utility ◽  
Prediction Intervals ◽  
Confidence Bounds ◽  
Robot Assisted ◽  
Meta Analyses ◽  
Robot Assisted Surgery

Objectives: When incorporating treatment effect estimates derived from a random-effect meta-analysis it is tempting to use the confidence bounds to determine the potential range of treatment effect. However, prediction intervals reflect the potential effect of a technology rather than the more narrowly defined average treatment effect. Using a case study of robot-assisted radical prostatectomy, this study investigates the impact on a cost-utility analysis of using clinical effectiveness derived from random-effects meta-analyses presented as confidence bounds and prediction intervals, respectively.Methods: To determine the cost-utility of robot-assisted prostatectomy, an economic model was developed. The clinical effectiveness of robot-assisted surgery compared with open and conventional laparoscopic surgery was estimated using meta-analysis of peer-reviewed publications. Assuming treatment effect would vary across studies due to both sampling variability and differences between surgical teams, random-effects meta-analysis was used to pool effect estimates.Results: Using the confidence bounds approach the mean and median ICER was €24,193 and €26,731/QALY (95%CI: €13,752 to €68,861/QALY), respectively. The prediction interval approach produced an equivalent mean and median ICER of €26,920 and €26,643/QALY (95%CI: -€135,244 to €239,166/QALY), respectively. Using prediction intervals, there is a probability of 0.042 that robot-assisted surgery will result in a net reduction in QALYs.Conclusions: Using prediction intervals rather than confidence bounds does not affect the point estimate of the treatment effect. In meta-analyses with significant heterogeneity, the use of prediction intervals will produce wider ranges of treatment effect, and hence result in greater uncertainty, but a better reflection of the effect of the technology.

scholarly journals A note on the graphical presentation of prediction intervals in random-effects meta-analyses

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Charlotte Guddat ◽  
Ulrich Grouven ◽  
Ralf Bender ◽  
Guido Skipka
Keyword(s):  
Random Effects ◽  
Prediction Intervals ◽  
Meta Analyses ◽  
Graphical Presentation
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