scholarly journals Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal’s Fail-Safe Number

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Konstantinos C. Fragkos ◽  
Michail Tsagris ◽  
Christos C. Frangos
Keyword(s):  
Confidence Intervals ◽  
Normal Approximation ◽  
Meta Analysis ◽  
Variance Estimator ◽  
Nonparametric Bootstrap ◽  
Interval Estimates ◽  
Variance Estimators ◽  
Lower Confidence ◽  
Confidence Interval Estimates ◽  
Fail Safe

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal’s fail-safe number. Although Rosenthal’s estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal’s fail-safe number. This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal’s estimator.

Meta-analysis of the impacts of water management on aquatic communities

2008 ◽  
Vol 65 (3) ◽  
pp. 437-447 ◽  
Author(s):  
Tim J Haxton ◽  
C Scott Findlay
Keyword(s):  
Water Management ◽  
Confidence Intervals ◽  
Effect Size ◽  
Meta Analysis ◽  
Ecological Impacts ◽  
Aquatic Communities ◽  
Flow Modification ◽  
Meta Analyses ◽  
Fail Safe ◽  
Habitat Generalists

Systematic meta-analyses were conducted on the ecological impacts of water management, including effects of (i) dewatering on macroinvertebrates, (ii) a hypolimnetic release on downstream aquatic fish and macro invertebrate communities, and (iii) flow modification on fluvial and habitat generalists. Our meta-analysis indicates, in general, that (i) macroinvertebrate abundance is lower in zones or areas that have been dewatered as a result of water fluctuations or low flows (overall effect size, –1.64; 95% confidence intervals (CIs), –2.51, –0.77), (ii) hypolimnetic draws are associated with reduced abundance of aquatic (fish and macroinvertebrates) communities (overall effect size, –0.84; 95% CIs, –1.38, –0.33) and macroinvertebrates (overall effect size, –0.73; 95% CIs, –1.24, –0.22) downstream of a dam, and (iii) altered flows are associated with reduced abundance of fluvial specialists (–0.42; 95% CIs, –0.81, –0.02) but not habitat generalists (overall effect size, –0.14; 95% CIs, –0.61, 0.32). Publication bias is evident in several of the meta-analyses; however, multiple experiments from a single study may be contributing to this bias. Fail-safe Ns suggest that many (>100) studies showing positive or no effects of water management on the selected endpoints would be required to qualitatively change the results of the meta-analysis, which in turn suggests that the conclusions are reasonably robust.

Corrected Standard Errors with Clustered Data

2019 ◽  
Vol 28 (3) ◽  
pp. 318-339
Author(s):  
John E. Jackson
Keyword(s):  
Confidence Intervals ◽  
Standard Errors ◽  
Number Of Clusters ◽  
Interval Estimates ◽  
Explanatory Variables ◽  
Multiple Observations ◽  
Cluster Variation ◽  
Confidence Interval Estimates ◽  
Considerable Discussion ◽  
Robust Standard Errors

The use of cluster robust standard errors (CRSE) is common as data are often collected from units, such as cities, states or countries, with multiple observations per unit. There is considerable discussion of how best to estimate standard errors and confidence intervals when using CRSE (Harden 2011; Imbens and Kolesár 2016; MacKinnon and Webb 2017; Esarey and Menger 2019). Extensive simulations in this literature and here show that CRSE seriously underestimate coefficient standard errors and their associated confidence intervals, particularly with a small number of clusters and when there is little within cluster variation in the explanatory variables. These same simulations show that a method developed here provides more reliable estimates of coefficient standard errors. They underestimate confidence intervals for tests of individual and sets of coefficients in extreme conditions, but by far less than do CRSE. Simulations also show that this method produces more accurate standard error and confidence interval estimates than bootstrapping, which is often recommended as an alternative to CRSE.

scholarly journals Confidence intervals for the common coefficient of variation of rainfall in Thailand

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10004
Author(s):  
Warisa Thangjai ◽  
Sa-Aat Niwitpong ◽  
Suparat Niwitpong
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Coefficient Of Variation ◽  
Daily Rainfall ◽  
Moisture Index ◽  
Interval Estimates ◽  
The Common ◽  
Log Normal ◽  
Confidence Interval Estimates

The log-normal distribution is often used to analyze environmental data like daily rainfall amounts. The rainfall is of interest in Thailand because high variable climates can lead to periodic water stress and scarcity. The mean, standard deviation or coefficient of variation of the rainfall in the area is usually estimated. The climate moisture index is the ratio of plant water demand to precipitation. The climate moisture index should use the coefficient of variation instead of the standard deviation for comparison between areas with widely different means. The larger coefficient of variation indicates greater dispersion, whereas the lower coefficient of variation indicates the lower risk. The common coefficient of variation, is the weighted coefficients of variation based on k areas, presents the average daily rainfall. Therefore, the common coefficient of variation is used to describe overall water problems of k areas. In this paper, we propose four novel approaches for the confidence interval estimation of the common coefficient of variation of log-normal distributions based on the fiducial generalized confidence interval (FGCI), method of variance estimates recovery (MOVER), computational, and Bayesian approaches. A Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths of the confidence intervals. In terms of coverage probability, the results show that the FGCI approach provided the best confidence interval estimates for most cases except for when the sample case was equal to six populations (k = 6) and the sample sizes were small (nI < 50), for which the MOVER confidence interval estimates were the best. The efficacies of the proposed approaches are illustrated with example using real-life daily rainfall datasets from regions of Thailand.

Empirical Nonparametric Bootstrap Strategies in Quantitative Trait Loci Mapping: Conditioning on the Genetic Model

Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 525-535
Author(s):  
Claude M Lebreton ◽  
Peter M Visscher
Keyword(s):  
Quantitative Trait Loci ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Genetic Model ◽  
Original Data ◽  
Nonparametric Bootstrap ◽  
Test Protocol ◽  
Trait Loci

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

scholarly journals Association of bisphenol A with puberty timing: a meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hui Meng ◽  
Yunping Zhou ◽  
Yunxia Jiang
Keyword(s):  
Bisphenol A ◽  
Confidence Intervals ◽  
Random Effects ◽  
Strong Correlation ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effects Models ◽  
Study Heterogeneity ◽  
The Relationship ◽  
Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

EXPRESS: A meta-analysis of aspirin and subarachnoid haemorrhage in patients with intracranial aneurysms yields different results to the general population

2021 ◽  
pp. 174749302110048
Author(s):  
Frederick Ewbank ◽  
Jacqueline Birks ◽  
Diederik Bulters
Keyword(s):  
General Population ◽  
Subarachnoid Haemorrhage ◽  
Confidence Intervals ◽  
Random Effects ◽  
Intracranial Aneurysms ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Random Effects Models ◽  
Hazard Ratios ◽  
Unruptured Intracranial Aneurysms

Abstract Background Some studies have shown a protective association between aspirin use and subarachnoid haemorrhage (SAH). Other studies have found no relationship or the reverse. These studies differ in their study populations and definitions of SAH. Aims Our aim was to establish 1) if there is an association between aspirin and SAH, 2) how this differs between the general population and those with intracranial aneurysms. Summary of review Studies reporting aspirin use and the occurrence of SAH were included and grouped based on population (general population vs aneurysm population). Odds ratios, hazard ratios and confidence intervals were combined in random-effects models. 11 studies were included. Overall, there was an association between aspirin and SAH (OR 0.68 [0.48, 0.96]). However, populations were diverse and heterogeneity between studies high (p<0.00001), questioning the validity of combining these studies and justifying analysis by population. In the general population there was no difference in aspirin use between individuals with and without SAH (OR 1.15 [0.96, 1.38]). In patients with intracranial aneurysms, aspirin use was greater in patients without SAH (OR 0.37 [0.24, 0.58]), although these studies were at higher risk of bias. Conclusions There is an association between aspirin use and SAH in patients with intracranial aneurysms. This apparent protective relationship is not seen in the general population. Prospective randomised studies are required to further investigate the effect of aspirin on unruptured intracranial aneurysms.

Emotional intelligence, Personality Variables and Career Adaptability: A Systematic Review and Meta-analysis

2021 ◽  
pp. 097226292198987
Author(s):  
Sakshi Vashisht ◽  
Poonam Kaushal ◽  
Ravi Vashisht
Keyword(s):  
Systematic Review ◽  
Emotional Intelligence ◽  
Publication Bias ◽  
Meta Analysis ◽  
Career Adaptability ◽  
Personality Variables ◽  
Regression Test ◽  
Size Measure ◽  
Fail Safe ◽  
The Relationship

This study conducted a systematic review and meta-analysis to examine the relationship between emotional intelligence, personality variables (Big V personality traits, self-esteem, self-efficacy, optimism and proactive personality) and career adaptability of students. Data were coded on CMA software version 3.0. Product–moment correlation coefficient (r) was considered as the effect size measure for this study. Publication bias was assessed using Egger’s regression test along with Orwin’s fail-safe N, but no significant publication bias was detected. From the results of 54 studies, it was found that all variables of the study had meta-analytic correlation with career adaptability of students. For heterogeneity, subgroup analysis was conducted, and significant differences were found.

scholarly journals Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (LASSO) estimator

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Weixin Cai ◽  
Mark van der Laan
Keyword(s):  
Confidence Intervals ◽  
Nonparametric Estimation ◽  
Cross Validation ◽  
Data Distribution ◽  
Average Treatment Effect ◽  
Finite Sample ◽  
Nonparametric Bootstrap ◽  
A Value ◽  
Variation Norm ◽  
Selection Operator

AbstractThe Highly-Adaptive least absolute shrinkage and selection operator (LASSO) Targeted Minimum Loss Estimator (HAL-TMLE) is an efficient plug-in estimator of a pathwise differentiable parameter in a statistical model that at minimal (and possibly only) assumes that the sectional variation norm of the true nuisance functions (i.e., relevant part of data distribution) are finite. It relies on an initial estimator (HAL-MLE) of the nuisance functions by minimizing the empirical risk over the parameter space under the constraint that the sectional variation norm of the candidate functions are bounded by a constant, where this constant can be selected with cross-validation. In this article we establish that the nonparametric bootstrap for the HAL-TMLE, fixing the value of the sectional variation norm at a value larger or equal than the cross-validation selector, provides a consistent method for estimating the normal limit distribution of the HAL-TMLE. In order to optimize the finite sample coverage of the nonparametric bootstrap confidence intervals, we propose a selection method for this sectional variation norm that is based on running the nonparametric bootstrap for all values of the sectional variation norm larger than the one selected by cross-validation, and subsequently determining a value at which the width of the resulting confidence intervals reaches a plateau. We demonstrate our method for 1) nonparametric estimation of the average treatment effect when observing a covariate vector, binary treatment, and outcome, and for 2) nonparametric estimation of the integral of the square of the multivariate density of the data distribution. In addition, we also present simulation results for these two examples demonstrating the excellent finite sample coverage of bootstrap-based confidence intervals.

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