A Note on the Noncentrality Parameter and Effect Size Estimates for the F Test in ANOVA

2004 ◽  
Vol 29 (2) ◽  
pp. 251-255 ◽  
Author(s):  
Xiaofeng Liu ◽  
Stephen Raudenbush
Keyword(s):  
Effect Size ◽  
Minimum Variance ◽  
Sum Of Squares ◽  
Noncentrality Parameter ◽  
Weighted Sum ◽  
Test Statistic ◽  
F Test ◽  
Effect Size Estimate ◽  
Common Effect ◽  
Size Estimates

The noncentrality parameter for the noncentral F is a precision-weighted sum of squares of treatment means, which is closely related to the test statistic and effect size. The two common effect size estimates are not based on the uniformly minimum variance unbiased (UMVU) estimate of the noncentrality parameter. The UMVU estimate of the noncentrality parameter implies a new and more conservative effect size estimate.

scholarly journals The Relationship Between a Lifetime History of Sexual Victimization and Perinatal Depression: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 152483802110216
Author(s):  
Brooke N. Lombardi ◽  
Todd M. Jensen ◽  
Anna B. Parisi ◽  
Melissa Jenkins ◽  
Sarah E. Bledsoe
Keyword(s):  
Systematic Review ◽  
Effect Size ◽  
Sexual Victimization ◽  
Perinatal Depression ◽  
Meta Analysis ◽  
Effect Size Estimate ◽  
Lifetime History ◽  
Size Estimate ◽  
History Of ◽  
Size Estimates

Background: The association between a lifetime history of sexual victimization and the well-being of women during the perinatal period has received increasing attention. However, research investigating this relationship has yet to be systematically reviewed or quantitatively synthesized. Aim: This systematic review and meta-analysis aims to calculate the pooled effect size estimate of the statistical association between a lifetime history of sexual victimization and perinatal depression (PND). Method: Four bibliographic databases were systematically searched, and reference harvesting was conducted to identify peer-reviewed articles that empirically examined associations between a lifetime history of sexual victimization and PND. A random effects model was used to ascertain an overall pooled effect size estimate in the form of an odds ratio and corresponding 95% confidence intervals (CIs). Subgroup analyses were also conducted to assess whether particular study features and sample characteristic (e.g., race and ethnicity) influenced the magnitude of effect size estimates. Results: This review included 36 studies, with 45 effect size estimates available for meta-analysis. Women with a lifetime history of sexual victimization had 51% greater odds of experiencing PND relative to women with no history of sexual victimization ( OR = 1.51, 95% CI [1.35, 1.67]). Effect size estimates varied considerably according to the PND instrument used in each study and the racial/ethnic composition of each sample. Conclusion: Findings provide compelling evidence for an association between a lifetime history of sexual victimization and PND. Future research should focus on screening practices and interventions that identify and support survivors of sexual victimization perinatally.

Association of study characteristics with estimates of effect size in studies of ecstasy use

2011 ◽  
Vol 25 (11) ◽  
pp. 1573-1577 ◽  
Author(s):  
Eleanor M Taylor ◽  
Natasha MP Greene ◽  
Celia JA Morgan ◽  
Marcus R Munafò
Keyword(s):  
Effect Size ◽  
Meta Analysis ◽  
Control Group ◽  
Effect Size Estimate ◽  
Size Estimate ◽  
Study Characteristics ◽  
Drug Naïve ◽  
Meta Analyses ◽  
And Control ◽  
Size Estimates

Studies of the chronic effects of MDMA, or ‘ecstasy’, in humans have been largely inconsistent. We explored whether study-level characteristics are associated with the effect size estimate reported. We based our analyses on the recent systematic review by Rogers and colleagues, focusing on those meta-analyses within this report where there was a relatively large number of studies contributing to each individual meta-analysis. Linear regression was used to investigate the association between study level variables and effect size estimate, weighted by the inverse of the SE of the effect size estimate, with cluster correction for studies which contributed multiple estimates. This indicated an association between effect size estimate and both user group, with smaller estimates among studies recruiting former users compared with those recruiting current users, and control group, with smaller estimates among studies recruiting polydrug user controls compared with those recruiting drug-naïve controls. In addition, increasing year of publication was associated with reduced effect size estimate, and there was a trend level association with prevalence of ecstasy use, reflecting smaller estimates among studies conducted in countries with higher prevalence of ecstasy use. Our data suggest a number of study-level characteristics which appear to influence individual study effect size estimates. These should be considered when designing future studies, and also when interpreting the ecstasy literature as a whole.

scholarly journals A Cautionary Note on Estimating Effect Size

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592199203
Author(s):  
Don van den Bergh ◽  
Julia M. Haaf ◽  
Alexander Ly ◽  
Jeffrey N. Rouder ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Statistical Inference ◽  
Effect Size ◽  
Null Hypothesis ◽  
Current Approach ◽  
Slab Model ◽  
Cautionary Note ◽  
Popular Approach ◽  
Estimation Process ◽  
Size Estimates

An increasingly popular approach to statistical inference is to focus on the estimation of effect size. Yet this approach is implicitly based on the assumption that there is an effect while ignoring the null hypothesis that the effect is absent. We demonstrate how this common null-hypothesis neglect may result in effect size estimates that are overly optimistic. As an alternative to the current approach, a spike-and-slab model explicitly incorporates the plausibility of the null hypothesis into the estimation process. We illustrate the implications of this approach and provide an empirical example.

scholarly journals Sex-specific differences in effect size estimates at established complex trait loci

2012 ◽  
Vol 41 (5) ◽  
pp. 1376-1382 ◽  
Author(s):  
Gisela Orozco ◽  
John PA Ioannidis ◽  
Andrew Morris ◽  
Eleftheria Zeggini ◽  
Keyword(s):  
Effect Size ◽  
Complex Trait ◽  
Trait Loci ◽  
Size Estimates

Bias Corrections for Standardized Effect Size Estimates Used With Single-Subject Experimental Designs

2013 ◽  
Vol 82 (3) ◽  
pp. 358-374 ◽  
Author(s):  
Maaike Ugille ◽  
Mariola Moeyaert ◽  
S. Natasha Beretvas ◽  
John M. Ferron ◽  
Wim Van den Noortgate
Keyword(s):  
Effect Size ◽  
Experimental Designs ◽  
Single Subject ◽  
Size Estimates ◽  
Bias Corrections

scholarly journals A Statistical Method for Synthesizing Meta-Analyses

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Liansheng Larry Tang ◽  
Michael Caudy ◽  
Faye Taxman
Keyword(s):  
Statistical Method ◽  
Effect Size ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Weighted Mean ◽  
Summary Effect ◽  
Different Types ◽  
Meta Analyses ◽  
Size Estimates ◽  
Similar Search

Multiple meta-analyses may use similar search criteria and focus on the same topic of interest, but they may yield different or sometimes discordant results. The lack of statistical methods for synthesizing these findings makes it challenging to properly interpret the results from multiple meta-analyses, especially when their results are conflicting. In this paper, we first introduce a method to synthesize the meta-analytic results when multiple meta-analyses use the same type of summary effect estimates. When meta-analyses use different types of effect sizes, the meta-analysis results cannot be directly combined. We propose a two-step frequentist procedure to first convert the effect size estimates to the same metric and then summarize them with a weighted mean estimate. Our proposed method offers several advantages over existing methods by Hemming et al. (2012). First, different types of summary effect sizes are considered. Second, our method provides the same overall effect size as conducting a meta-analysis on all individual studies from multiple meta-analyses. We illustrate the application of the proposed methods in two examples and discuss their implications for the field of meta-analysis.

scholarly journals Comparing the Prognostic Impact of Age and Baseline National Institutes of Health Stroke Scale in Acute Stroke due to Large Vessel Occlusion

Stroke ◽  
2021 ◽  
Author(s):  
Johanna Maria Ospel ◽  
Scott Brown ◽  
Manon Kappelhof ◽  
Wim van Zwam ◽  
Tudor Jovin ◽  
...  
Keyword(s):  
Effect Size ◽  
Interquartile Range ◽  
National Institutes Of Health ◽  
Large Vessel ◽  
Prognostic Impact ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion ◽  
Good Outcome ◽  
The Impact ◽  
Size Estimates

Background and Purpose: Little is known about the combined effect of age and National Institutes of Health Stroke Scale (NIHSS) in endovascular treatment (EVT) for acute ischemic stroke due to large vessel occlusion, and it is not clear how the effects of baseline age and NIHSS on outcome compare to each other. The previously described Stroke Prognostication Using Age and NIHSS (SPAN) index adds up NIHSS and age to a 1:1 combined prognostic index. We added a weighting factor to the NIHSS/age SPAN index to compare the relative prognostic impact of NIHSS and age and assessed EVT effect based on weighted age and NIHSS. Methods: We performed adjusted logistic regression with good outcome (90-day modified Rankin Scale score 0–2) as primary outcome. From this model, the coefficients for NIHSS and age were obtained. The ratio between the NIHSS and age coefficients was calculated to determine a weighted SPAN index. We obtained adjusted effect size estimates for EVT in patient subgroups defined by weighted SPAN increments of 3, to evaluate potential changes in treatment effect. Results: We included 1750/1766 patients from the HERMES collaboration (Highly Effective Reperfusion Using Multiple Endovascular Devices) with available age and NIHSS data. Median NIHSS was 17 (interquartile range, 13–21), and median age was 68 (interquartile range, 57–76). Good outcome was achieved by 682/1743 (39%) patients. The NIHSS/age effect coefficient ratio was ([−0.0032]/[−0.111])=3.4, which was rounded to 3, resulting in a weighted SPAN index defined as ([3×NIHSS]+age). Cumulative EVT effect size estimates across weighted SPAN subgroups consistently favored EVT, with a number needed to treat ranging from 5.3 to 8.7. Conclusions: The impact on chance of good outcome of a 1-point increase in NIHSS roughly corresponded to a 3-year increase in patient age. EVT was beneficial across all weighted age/NIHSS subgroups.

Effect Size Estimates of Lifestyle and Dietary Changes on All-Cause Mortality in Coronary Artery Disease Patients

2005 ◽  
Vol 14 (12) ◽  
pp. 15
Author(s):  
J.A. Iestra ◽  
D. Kromhout ◽  
Y.T. vaderSchouw ◽  
D.E.E. Grobbee ◽  
H.C. Boshuizen ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Effect Size ◽  
Dietary Changes ◽  
All Cause Mortality ◽  
Artery Disease ◽  
Size Estimates
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