scholarly journals Use of Effect Size Measures along with p-Value in Scientific Publications

2021 ◽  
Vol 2 (2) ◽  
pp. 89-97
Author(s):  
Sandheep Sugathan ◽  
Lilli Jacob
Keyword(s):  
Effect Size ◽  
Meta Analysis ◽  
Research Articles ◽  
Outcome Variable ◽  
P Value ◽  
Continuous Variables ◽  
Statistical Concept ◽  
Scientific Publications ◽  
Actual Difference ◽  
The Difference

   Background: To describe various measures for estimation of effect size, how it can be calculated and the scenarios in which each measures of effect size can be applied.  Methods: The researchers can display the effect size measures in research articles which evaluate the difference between the means of continuous variables in different groups or the difference in proportions of outcomes in different groups of individuals. When p-value alone is displayed in a research article, without mentioning the effect size, reader may not get the correct pictures regarding the effect or role of independent variable on the outcome variable.  Results: Effect size is a statistical concept that measures the actual difference between the groups or the strength of the relationship between two variables on a numeric scale.  Conclusion: Effect size measures in scientific publications can communicate the actual difference between groups or the estimate of association between the variables, not just if the association or difference is statistically significant. The researchers can make their findings more interpretable, by displaying a suitable measure of effect size. Effect size measure can help the researchers to do meta-analysis by combining the data from multiple research articles. 

VOMITING

2018 ◽  
Vol 25 (12) ◽  
pp. 1887-1891
Author(s):  
Malik Jamil Ahmed ◽  
Muhammad Nasir ◽  
Aamir Furqan
Keyword(s):  
Computer Software ◽  
Outcome Variable ◽  
Categorical Variables ◽  
P Value ◽  
Continuous Variables ◽  
Postoperative Vomiting ◽  
Hospital Data ◽  
Chi Square ◽  
Oral Ketamine ◽  
The Difference

Objectives: To investigate whether the addition of dexamethasone and chloropheniramine to oral ketamine premedication affects the incidence of postoperative vomiting. Study Design: Randomized control trail. Setting: Department of Anesthesia and Intensive Care Nishtar Hospital, Multan. Period: March 2016 to March 2017. Methodology: After obtaining ethical approval ethical and review board of hospital. Data was entered in a computer software SPSS version 23.1 and analyzed for possible variables. Continuous variables were presented as mean and standard deviation like age, weight, sedation time, anesthesia time, admission time and PACU time. Categorical variables were presented as gender, ASA statusand postoperative vomiting. Student test and chi square test was applied to see association of outcome variable. P value of 0.05 was taken as significant. Results: Overall, 100% (n=80) patients were included in this study, both genders. The study group was further divided into twoequal groups, 50% (n=40) in each, i.e. Group K (Ketamine) group and group KD (Ketamine-Dexamethasone). The main outcome variable of this study was postoperative vomiting. In this study, Postoperative vomiting observed in 35% (n=10) and 10% (n=4) patients, for group K and group KD respectively. The difference was statistically significant (p=0.007). Conclusion: Addition of dexamethasone and chloropheniramine with ketamine as premedication reduce the incidence of postoperative vomiting.

scholarly journals Effect size: a statistical basis for clinical practice

2018 ◽  
Vol 33 (1) ◽  
pp. 84
Author(s):  
José Valladares-Neto
Keyword(s):  
Effect Size ◽  
Risk Estimation ◽  
Meta Analysis ◽  
Scientific Evidence ◽  
P Value ◽  
Conceptual Basis ◽  
Statistical Measure ◽  
The Difference ◽  
Strength Of Association ◽  
Calculate Sample Size

OBJECTIVE: Effect size (ES) is the statistical measure which quantifies the strength of a phenomenon and is commonly applied to observational and interventional studies. The aim of this review was to describe the conceptual basis of this measure, including its application, calculation and interpretation.RESULTS: As well as being used to detect the magnitude of the difference between groups, to verify the strength of association between predictor and outcome variables, to calculate sample size and power, ES is also used in meta-analysis. ES formulas can be divided into these categories: I – Difference between groups, II – Strength of association, III – Risk estimation, and IV – Multivariate data. The d value was originally considered small (0.20 > d ≤ 0.49), medium (0.50 > d≤ 0.79) or large (d ≥ 0.80); however, these cut-off limits are not consensual and could be contextualized according to a specific field of knowledge. In general, a larger score implies that a larger difference was detected.CONCLUSION: The ES report, in conjunction with the confidence interval and P value, aims to strengthen interpretation and prevent the misinterpretation of data, and thus leads to clinical decisions being based on scientific evidence studies.

scholarly journals Effectiveness of a Pharmacogenetic Tool at Improving Treatment Efficacy in Major Depressive Disorder: A Meta-Analysis of Three Clinical Studies

Pharmaceutics ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 453 ◽  
Author(s):  
Vilches ◽  
Tuson ◽  
Vieta ◽  
Álvarez ◽  
Espadaler
Keyword(s):  
Random Effects ◽  
Effect Size ◽  
Clinical Studies ◽  
Clinical Utility ◽  
Meta Analysis ◽  
Severe Depression ◽  
P Value ◽  
Drug Selection ◽  
Mild Depression ◽  
Depressive Patients

Several pharmacogenetic tests to support drug selection in psychiatric patients have recently become available. The current meta-analysis aimed to assess the clinical utility of a commercial pharmacogenetic-based tool for psychiatry (Neuropharmagen®) in the treatment management of depressive patients. Random-effects meta-analysis of clinical studies that had examined the effect of this tool on the improvement of depressive patients was performed. Effects were summarized as standardized differences between treatment groups. A total of 450 eligible subjects from three clinical studies were examined. The random effects model estimated a statistically significant effect size for the pharmacogenetic-guided prescription (d = 0.34, 95% CI = 0.11–0.56, p-value = 0.004), which corresponded to approximately a 1.8-fold increase in the odds of clinical response for pharmacogenetic-guided vs. unguided drug selection. After exclusion of patients with mild depression, the pooled estimated effect size increased to 0.42 (95% CI = 0.19–0.65, p-value = 0.004, n = 287), corresponding to an OR = 2.14 (95% CI = 1.40–3.27). These results support the clinical utility of this pharmacogenetic-based tool in the improvement of health outcomes in patients with depression, especially those with moderate–severe depression. Additional pragmatic RCTs are warranted to consolidate these findings in other patient populations.

scholarly journals Do animated triangles reveal a marked difficulty among autistic people with reading minds?

Autism ◽  
2021 ◽  
pp. 136236132198915
Author(s):  
Alexander C Wilson
Keyword(s):  
Effect Size ◽  
Meta Analysis ◽  
General Information ◽  
Medium Effect ◽  
Group Differences ◽  
Social Challenges ◽  
Autistic People ◽  
The Social ◽  
Small Effect Size ◽  
The Difference

This meta-analysis tested whether autistic people show a marked, isolated difficulty with mentalising when assessed using the Frith -Happé Animations, an advanced test of mentalising (or ‘theory of mind’). Effect sizes were aggregated in multivariate meta-analysis from 33 papers reporting data for over 3000 autistic and non-autistic people. Relative to non-autistic individuals, autistic people underperformed, with a small effect size on the non-mentalising control conditions and a medium effect size on the mentalising condition. This indicates that studies have reliably found mentalising to be an area of challenge for autistic people, although the group differences were not large. It remains to be seen how important mentalising difficulties are in accounting for the social difficulties diagnostic of autism. As autistic people underperformed on the control conditions as well as the mentalising condition, it is likely that group differences on the test are partly due to domain-general information processing differences. Finally, there was evidence of publication bias, suggesting that true effects on the Frith -Happé Animations may be somewhat smaller than reported in the literature. Lay abstract Autistic people are thought to have difficulty with mentalising (our drive to track and understand the minds of other people). Mentalising is often measured by the Frith -Happé Animations task, where individuals need to interpret the interactions of abstract shapes. This review article collated results from over 3000 people to assess how autistic people performed on the task. Analysis showed that autistic people tended to underperform compared to non-autistic people on the task, although the scale of the difference was moderate rather than large. Also, autistic people showed some difficulty with the non-mentalising as well as mentalising aspects of the task. These results raise questions about the scale and specificity of mentalising difficulties in autism. It also remains unclear how well mentalising difficulties account for the social challenges diagnostic of autism.

scholarly journals Effects of stimulants and atomoxetine on emotional lability in adults: A systematic review and meta-analysis

2017 ◽  
Vol 44 ◽  
pp. 198-207 ◽  
Author(s):  
T.R. Moukhtarian ◽  
R.E. Cooper ◽  
E. Vassos ◽  
P. Moran ◽  
P. Asherson
Keyword(s):  
Systematic Review ◽  
Effect Size ◽  
Meta Analysis ◽  
Emotional Lability ◽  
Adhd Symptoms ◽  
Double Blind ◽  
Hyperactivity Disorder ◽  
Ovid Medline ◽  
Weighted Effect ◽  
The Difference

AbstractBackground:Emotional lability (EL) is an associated feature of attention-deficit/hyperactivity disorder (ADHD) in adults, contributing to functional impairment. Yet the effect of pharmacological treatments for ADHD on EL symptoms is unknown. We conducted a systematic review and meta-analysis to examine the effects of stimulants and atomoxetine on symptoms of EL and compare these with the effects on core ADHD symptoms.Methods:A systematic search was conducted on the databases Embase, PsychInfo, and Ovid Medline®and the clinicaltrials.gov website. We included randomised, double-blind, placebo-controlled trials of stimulants and atomoxetine in adults aged 18–60 years, with any mental health diagnosis characterised by emotional or mood instability, with at least one outcome measure of EL. All identified trials were on adults with ADHD. A random-effects meta-analysis with standardised mean difference and 95% confidence intervals was used to investigate the effect size on EL and compare this to the effect on core ADHD symptoms.Results:Of the 3,864 publications identified, nine trials met the inclusion criteria for the meta-analysis. Stimulants and atomoxetine led to large mean weighted effect-sizes for on ADHD symptoms (n= 9, SMD = −0.8, 95% CI:−1.07 to −0.53). EL outcomes showed more moderate but definite effects (n= 9, SMD = −0.41, 95% CI:−0.57 to −0.25).Conclusions:In this meta-analysis, stimulants and atomoxetine were moderately effective for EL symptoms, while effect size on core ADHD symptoms was twice as large. Methodological issues may partially explain the difference in effect size. Reduced average effect size could also reflect heterogeneity of EL with ADHD pharmacotherapy responsive and non-responsive sub-types. Our findings indicate that EL may be less responsive than ADHD symptoms overall, perhaps indicating the need for adjunctive psychotherapy in some cases. To clarify these questions, our findings need replication in studies selecting subjects for high EL and targeting EL as the primary outcome.

scholarly journals تأثير استخدام الوسائل التعليمية في تعليم اللغة العربية

2021 ◽  
Vol 10 (3) ◽  
pp. 548
Author(s):  
Nur Fadhillah Mukarrami ◽  
Qusaiyen Qusaiyen ◽  
Salma Hayati
Keyword(s):  
Junior High School ◽  
Effect Size ◽  
Meta Analysis ◽  
Education Level ◽  
Research Articles ◽  
Size Category ◽  
Learning Content ◽  
Level 3 ◽  
The Right ◽  
High Influence

The background of this meta-analysis is because there are still many teachers and lecturers who have not used or developed learning media especially for Arabic education. So it is necessary to analyze the articles from the results of previous studies to be able to understand the right media for learning Arabic. The purpose of this study was to determine: (1) Types of research related to Arabic learning media and their effects; (2) Effect size of Arabic learning media based on education level / level; (3) Effect size of Arabic learning media by year; (4) Effect size of Arabic learning media based on the material used as learning content; (5) Effect size of Arabic learning media based on the type of media. The method used in this study is meta-analysis with the formula of (Glass, 1981). The population in this study is 30 research articles related to the discussion of this study. The research sample is 17 research articles. The results showed that: (1) The type of research related to Arabic learning media include R&D (2,38) included in the effect size category with a high influence; (2) Effect size of Arabic learning media based on the best education level, namely at Junior High School (1.95), (3) Effect size of Arabic learning media based on the best year around 2015-2017 (4.49), (4) Effect size of Arabic learning media based on the best material on dhamir (3.95), (5) Effect size of Arabic learning media based on the best type of media that is on the computer (3.95).

scholarly journals Revisiting and expanding the meta-analysis of variation: The log coefficient of variation ratio, lnCVR

2020 ◽  
Author(s):  
Alistair M. Senior ◽  
Wolfgang Viechtbauer ◽  
Shinichi Nakagawa
Keyword(s):  
Effect Size ◽  
Meta Analysis ◽  
Experimental Treatment ◽  
Worked Examples ◽  
Sampling Variance ◽  
Post Test ◽  
Total Variability ◽  
The Difference ◽  
Meta Analyses ◽  
Analysis Of Variation

AbstractMeta-analyses are frequently used to quantify the difference in the average values of two groups (e.g., control and experimental treatment groups), but examine the difference in the variability (variance) of two groups. For such comparisons, the two relatively new effect size statistics, namely the log-transformed ‘variability ratio’ (the ratio of two standard deviations; lnVR) and the log-transformed ‘CV ratio’ (the ratio of two coefficients of variation; lnCVR) are useful. In practice, lnCVR may be of most use because a treatment may affect the mean and the variance simultaneously. We review current, and propose new, estimators for lnCVR and lnVR. We also present methods for use when the two groups are dependent (e.g., for cross-over and pre-test-post-test designs). A simulation study evaluated the performance of these estimators and we make recommendations about which estimators one should use to minimise bias. We also present two worked examples that illustrate the importance of accounting for the dependence of the two groups. We found that the degree to which dependence is accounted for in the sampling variance estimates can impact heterogeneity parameters such as τ2 (i.e., the between-study variance) and I2 (i.e., the proportion of the total variability due to between-study variance), and even the overall effect, and in turn qualitative interpretations. Meta-analytic comparison of the variability between two groups enables us to ask completely new questions and to gain fresh insights from existing datasets. We encourage researchers to take advantage of these convenient new effect size measures for the meta-analysis of variation.

scholarly journals Effect Size

Psychology ◽  
2019 ◽  
Author(s):  
David B. Flora
Keyword(s):  
Effect Size ◽  
Null Hypothesis ◽  
Power Analysis ◽  
Meta Analysis ◽  
Significance Test ◽  
Effect Sizes ◽  
Research Reporting ◽  
The Difference ◽  
Strength Of Association ◽  
True Population

Simply put, effect size (ES) is the magnitude or strength of association between or among variables. Effect sizes (ESs) are commonly represented numerically (i.e., as parameters for population ESs and statistics for sample estimates of population ESs) but also may be communicated graphically. Although the word “effect” may imply that an ES quantifies the strength of a causal association (“cause and effect”), ESs are used more broadly to represent any empirical association between variables. Effect sizes serve three general purposes: research results reporting, power analysis, and meta-analysis. Even under the same research design, an ES that is appropriate for one of these purposes may not be ideal for another. Effect size can be conveyed graphically or numerically using either unstandardized metrics, which are interpreted relative to the original scales of the variables involved (e.g., the difference between two means or an unstandardized regression slope), or standardized metrics, which are interpreted in relative terms (e.g., Cohen’s d or multiple R2). Whereas unstandardized ESs and graphs illustrating ES are typically most effective for research reporting, that is, communicating the original findings of an empirical study, many standardized ES measures have been developed for use in power analysis and especially meta-analysis. Although the concept of ES is clearly fundamental to data analysis, ES reporting has been advocated as an essential complement to null hypothesis significance testing (NHST), or even as a replacement for NHST. A null hypothesis significance test involves making a dichotomous judgment about whether to reject a hypothesis that a true population effect equals zero. Even in the context of a traditional NHST paradigm, ES is a critical concept because of its central role in power analysis.

Abstract P057: The Association Between Diabetes and Risk of Abdominal Aortic Aneurysm: A Meta-Analysis

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Lu Yao ◽  
Aaron Folsom ◽  
Alvaro Alonso ◽  
James Pankow ◽  
Weihua Guan ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Relative Risk ◽  
Aortic Aneurysm ◽  
Meta Analysis ◽  
Case Control ◽  
Outcome Variable ◽  
P Value ◽  
Published Data ◽  
Eligibility Criteria ◽  
Abdominal Aortic

Objectives: Data regarding the relationship between diabetes and abdominal aortic aneurysm (AAA) are inconsistent across studies: some studies showed an inverse relationship while others did not show an association. We conducted a meta-analysis to examine the association between diabetes and AAA based on published data from case-control and cohort studies. Methods: We searched literature in English from online databases including MEDLINE (1966-), EMBASE and Web of Science as of July 2017, plus a manual examination of references in selected articles. The eligibility criteria included (1) a case-control or cohort study conducted in adults; (2) diabetes is the exposure variable and AAA risk is the outcome variable; and (3) association estimates (hazard ratios, odds ratios or relative risks) and measurement of variance (P value, confidence interval, or standard error) were available. The literature review and data abstraction were conducted in duplicate by independent investigators. A DerSimonian and Laird random effects model was used to pool association estimates and their 95% confidence intervals from studies using STATA 13. The Cochran’s Q test was used to assess the presence of heterogeneity and the I-square index to quantify the extent of heterogeneity. Results: We included in the meta-analyses a total of 10 cohorts with 10,771 AAAs in 2,625,318 participants and 4 case-control studies with 1,065 AAAs and 11,009 controls that met the pre-determined eligibility criteria. The samples were predominantly white (88%). Study-specific relative risk and pooled relative risk as well as heterogeneity test results were shown in Figure. Diabetes was inversely associated with AAA risk (pooled relative risk: 0.55; 95%CI: 0.49 - 0.61, Figure) . Results were overall consistent by sex, study design and setting (hospital- vs community-based). Conclusions: The findings suggest that diabetes is strongly and inversely associated with the risk of AAA. Future studies are warranted to investigate the potential mechanisms.

scholarly journals Different Spectral Analysis Methods for the Theta/Beta Ratio Calculate Different Ratios But Do Not Distinguish ADHD from Controls

2020 ◽  
Vol 45 (3) ◽  
pp. 165-173
Author(s):  
Hanneke van Dijk ◽  
Roger deBeus ◽  
Cynthia Kerson ◽  
Michelle E. Roley-Roberts ◽  
Vincent J. Monastra ◽  
...  
Keyword(s):  
Signal Processing ◽  
Effect Size ◽  
Meta Analysis ◽  
Healthy Controls ◽  
Eeg Signal ◽  
Ongoing Research ◽  
Beta Power ◽  
Signal Processing Algorithms ◽  
The Difference ◽  
Eeg Signal Processing

Abstract There has been ongoing research on the ratio of theta to beta power (Theta/Beta Ratio, TBR) as an EEG-based test in the diagnosis of ADHD. Earlier studies reported significant TBR differences between patients with ADHD and controls. However, a recent meta-analysis revealed a marked decline of effect size for the difference in TBR between ADHD and controls for studies published in the past decade. Here, we test if differences in EEG processing explain the heterogeneity of findings. We analyzed EEG data from two multi-center clinical studies. Five different EEG signal processing algorithms were applied to calculate the TBR. Differences between resulting TBRs were subsequently assessed for clinical usability in the iSPOT-A dataset. Although there were significant differences in the resulting TBRs, none distinguished between children with and without ADHD, and no consistent associations with ADHD symptoms arose. Different methods for EEG signal processing result in significantly different TBRs. However, none of the methods significantly distinguished between ADHD and healthy controls in our sample. The secular effect size decline for the TBR is most likely explained by factors other than differences in EEG signal processing, e.g. fewer hours of sleep in participants and differences in inclusion criteria for healthy controls.

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