scholarly journals Studi meta analisis: Efektivitas progressive muscle relaxation untuk menurunkan kecemasan orang dengan penderita penyakit kronis.

2021 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
Nur Fadillah ◽  
Ananta Yudiarso
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Meta Analysis ◽  
Muscle Relaxation ◽  
Random Effect ◽  
Progressive Muscle Relaxation ◽  
Cohen’S D ◽  
Cohen's D

Objektif: Kecemasan sering terjadi oleh siapapun, terutama yang memiliki riwayat penyakit akan memiliki tingkat kecemasan yang lebih tinggi. Progressive muscle relaxation merupakan salah satu pengobatan non farmakologis yang dapat digunakan untuk menurunkan kecemasan. Tujuan dari penelitian ini untuk mengetahui efektivitas progressive muscle relaxation dalam menurunkan kecemasan. Metode: Menggunakan meta-analysis berupa review literatur 14 jurnal penelitian internasional. Total partisipan sebanyak 1.022 yang terdiri dari kelompok kontrol sebesar 516 dan kelompok eksperimen sebesar 506. Analisis data menggunakan website Meta-mar. Peneliti menggunakan pedoman dari PRISMA dan MARS.Temuan: Dengan menggunakan analisis statistik cohen’s d effect size, diperoleh hasil pengolahan data yaitu mean (M), standar deviasi (SD), dan sample size (N) menghasilkan effect size dengan menggunakan random effect dengan Hedges'g sebesar 0.81, 95%CI=0,224 sampai 1.401 dengan nilai Inconsistency (I2)= 94,5%. yang berarti bahwa progressive muscle relaxation memiliki efek yang besar untuk menurunkan kecemasan.Kesimpulan: Progressive muscle relaxation memberikan efek yang besar dalam menurunkan kecemasan.

scholarly journals Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Christopher R Brydges
Keyword(s):  
Individual Differences ◽  
Sample Size ◽  
Effect Size ◽  
Effect Sizes ◽  
Group Differences ◽  
Cohen’S D ◽  
Pearson's R ◽  
Sample Size Calculations ◽  
Cohen's D ◽  
Pearson’S R

Abstract Background and Objectives Researchers typically use Cohen’s guidelines of Pearson’s r = .10, .30, and .50, and Cohen’s d = 0.20, 0.50, and 0.80 to interpret observed effect sizes as small, medium, or large, respectively. However, these guidelines were not based on quantitative estimates and are only recommended if field-specific estimates are unknown. This study investigated the distribution of effect sizes in both individual differences research and group differences research in gerontology to provide estimates of effect sizes in the field. Research Design and Methods Effect sizes (Pearson’s r, Cohen’s d, and Hedges’ g) were extracted from meta-analyses published in 10 top-ranked gerontology journals. The 25th, 50th, and 75th percentile ranks were calculated for Pearson’s r (individual differences) and Cohen’s d or Hedges’ g (group differences) values as indicators of small, medium, and large effects. A priori power analyses were conducted for sample size calculations given the observed effect size estimates. Results Effect sizes of Pearson’s r = .12, .20, and .32 for individual differences research and Hedges’ g = 0.16, 0.38, and 0.76 for group differences research were interpreted as small, medium, and large effects in gerontology. Discussion and Implications Cohen’s guidelines appear to overestimate effect sizes in gerontology. Researchers are encouraged to use Pearson’s r = .10, .20, and .30, and Cohen’s d or Hedges’ g = 0.15, 0.40, and 0.75 to interpret small, medium, and large effects in gerontology, and recruit larger samples.

scholarly journals Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility

2020 ◽  
Author(s):  
Neil Smalheiser ◽  
Elena E. Graetz ◽  
Zhou Yu ◽  
Jing Wang
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Forced Swim Test ◽  
Real Life ◽  
Swim Test ◽  
Cohen’S D ◽  
Forced Swim ◽  
Cohen's D ◽  
Adequate Power

A recent flood of publications has documented serious problems in scientific reproducibility, power, and reporting of biomedical articles, yet scientists persist in their usual practices. Why? We examined a popular and important preclinical assay, the Forced Swim Test (FST) in mice used to test putative antidepressants. Whether the mice were assayed in a naïve state vs. in a model of depression or stress, and whether the mice were given test agents vs. known antidepressants regarded as positive controls, the mean effect sizes seen in the experiments were indeed extremely large (1.5 – 2.5 in Cohen’s d units); most of the experiments utilized 7-10 animals per group which did have adequate power to reliably detect effects of this magnitude. We propose that this may at least partially explain why investigators using the FST do not perceive intuitively that their experimental designs fall short -- even though proper prospective design would require ~21-26 animals per group to detect, at a minimum, large effects (0.8 in Cohen’s d units) when the true effect of a test agent is unknown. Our data provide explicit parameters and guidance for investigators seeking to carry out prospective power estimation for the FST. More generally, altering the real-life behavior of scientists in planning their experiments may require developing educational tools that allow them to actively visualize the inter-relationships among effect size, sample size, statistical power, and replicability in a direct and intuitive manner.

scholarly journals Effect Size Interpretation, Sample Size Calculation, and Statistical Power in Gerontology

2019 ◽  
Author(s):  
Christopher Brydges
Keyword(s):  
Individual Differences ◽  
Sample Size ◽  
Effect Size ◽  
Sample Size Calculation ◽  
Effect Sizes ◽  
Group Differences ◽  
Cohen’S D ◽  
Pearson's R ◽  
Cohen's D ◽  
Pearson’S R

Background and Objectives: Researchers typically use Cohen’s guidelines of Pearson’s r = .10, .30, and .50, and Cohen’s d = 0.20, 0.50, and 0.80 to interpret observed effect sizes as small, medium, or large, respectively. However, these guidelines were not based on quantitative estimates, and are only recommended if field-specific estimates are unknown. The current study investigated the distribution of effect sizes in both individual differences research and group differences research in gerontology to provide estimates of effect sizes in the field.Research Design and Methods: Effect sizes (Pearson’s r, Cohen’s d, and Hedges’ g) were extracted from meta-analyses published in ten top-ranked gerontology journals. The 25th, 50th, and 75th percentile ranks were calculated for Pearson’s r (individual differences) and Cohen’s d or Hedges’ g (group differences) values as indicators of small, medium, and large effects. A priori power analyses were conducted for sample size calculations given the observed effect size estimates.Results: Effect sizes of Pearson’s r = .12, .20, and .32 for individual differences research and Hedges’ g = 0.16, 0.38, and 0.76 for group differences research were interpreted as small, medium, and large effects in gerontology. Discussion and Implications: Cohen’s guidelines appear to overestimate effect sizes in gerontology. Researchers are encouraged to use Pearson’s r = .10, .20, and .30, and Cohen’s d or Hedges’ g = 0.15, 0.40, and 0.75 to interpret small, medium, and large effects in gerontology, and recruit larger samples.

scholarly journals Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0243668
Author(s):  
Neil R. Smalheiser ◽  
Elena E. Graetz ◽  
Zhou Yu ◽  
Jing Wang
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Forced Swim Test ◽  
Real Life ◽  
Swim Test ◽  
Cohen’S D ◽  
Forced Swim ◽  
Cohen's D ◽  
Adequate Power

A recent flood of publications has documented serious problems in scientific reproducibility, power, and reporting of biomedical articles, yet scientists persist in their usual practices. Why? We examined a popular and important preclinical assay, the Forced Swim Test (FST) in mice used to test putative antidepressants. Whether the mice were assayed in a naïve state vs. in a model of depression or stress, and whether the mice were given test agents vs. known antidepressants regarded as positive controls, the mean effect sizes seen in the experiments were indeed extremely large (1.5–2.5 in Cohen’s d units); most of the experiments utilized 7–10 animals per group which did have adequate power to reliably detect effects of this magnitude. We propose that this may at least partially explain why investigators using the FST do not perceive intuitively that their experimental designs fall short—even though proper prospective design would require ~21–26 animals per group to detect, at a minimum, large effects (0.8 in Cohen’s d units) when the true effect of a test agent is unknown. Our data provide explicit parameters and guidance for investigators seeking to carry out prospective power estimation for the FST. More generally, altering the real-life behavior of scientists in planning their experiments may require developing educational tools that allow them to actively visualize the inter-relationships among effect size, sample size, statistical power, and replicability in a direct and intuitive manner.

scholarly journals Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility

2020 ◽  
Author(s):  
Neil R. Smalheiser ◽  
Elena E. Graetz ◽  
Zhou Yu ◽  
Jing Wang
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Forced Swim Test ◽  
Real Life ◽  
Swim Test ◽  
Cohen’S D ◽  
Forced Swim ◽  
Cohen's D ◽  
Adequate Power

AbstractA recent flood of publications has documented serious problems in scientific reproducibility, power, and reporting of biomedical articles, yet scientists persist in their usual practices. Why? We examined a popular and important preclinical assay, the Forced Swim Test (FST) in mice used to test putative antidepressants. Whether the mice were assayed in a naïve state vs. in a model of depression or stress, and whether the mice were given test agents vs. known antidepressants regarded as positive controls, the mean effect sizes seen in the experiments were indeed extremely large (1.5 – 2.5 in Cohen’s d units); most of the experiments utilized 7-10 animals per group which did have adequate power to reliably detect effects of this magnitude. We propose that this may at least partially explain why investigators using the FST do not perceive intuitively that their experimental designs fall short -- even though proper prospective design would require ~21-26 animals per group to detect, at a minimum, large effects (0.8 in Cohen’s d units) when the true effect of a test agent is unknown. Our data provide explicit parameters and guidance for investigators seeking to carry out prospective power estimation for the FST. More generally, altering the real-life behavior of scientists in planning their experiments may require developing educational tools that allow them to actively visualize the inter-relationships among effect size, sample size, statistical power, and replicability in a direct and intuitive manner.

A non-parametric effect size measure capturing changes in central tendency and shape of data distributions more flexibly than Cohen’s d

2020 ◽  
Author(s):  
Jörn Lötsch ◽  
Alfred Ultsch
Keyword(s):  
Effect Size ◽  
Data Science ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Central Tendency ◽  
Effect Size Measure ◽  
Specific Data ◽  
Cohen’S D ◽  
Size Measure ◽  
Cohen's D

Abstract Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treatment-related effects on the data if the effects were not reflected by the central tendency. "Impact” is a novel nonparametric measure of effect size obtained as the sum of two separate components and includes (i) the change in the central tendency of the group-specific data, normalized to the overall variability, and (ii) the difference in the probability density of the group-specific data. Results obtained on artificial data and empirical biomedical data showed that impact outperforms Cohen's d by this additional component. It is shown that in a multivariate setting, while standard statistical analyses and Cohen’s d are not able to identify effects that lead to changes in the form of data distribution, “Impact” correctly captures them. The proposed effect size measure shares the ability to observe such an effect with machine learning algorithms. It is numerically stable even for degenerate distributions consisting of singular values. Therefore, the proposed effect size measure is particularly well suited for data science and artificial intelligence-based knowledge discovery from (big) and heterogeneous data.

scholarly journals Prevalence of HTN in Iran: Meta-analysis of Published Studies in 2004-2018

2019 ◽  
Vol 15 (2) ◽  
pp. 113-122
Author(s):  
Mehdi Jafari Oori ◽  
Farahnaz Mohammadi ◽  
Kian Norozi ◽  
Masoud Fallahi-Khoshknab ◽  
Abbas Ebadi ◽  
...  
Keyword(s):  
Sample Size ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Effect Model ◽  
Significant Difference ◽  
Standard Tool ◽  
Egger Test ◽  
Significant Health

Introduction: Prevalence of hypertension (HTN) is increasing in the developing countries like Iran. Various studies have reported different rates of HTN in Iran. The purpose of this study was to estimate an overall prevalence of HTN in Iran. Methodology: Using the English and Persian key derived from Mesh, the databases including MagIran, Iran Medex, SID, Web of Sciences, PubMed, Science Direct and Google Scholar as a searching engine were reviewed: from 2004 to 2018. The overall prevalence of MA was estimated using Random effect model. The I2 test was used to assess the heterogeneity of the studies. Additionally, the quality of studies was evaluated using a standard tool. Publication bias was conducted with the Egger test. Meta-regression and analysis of subgroups were analyzed based on variables such as age, marital status, region and tools. Data were analyzed using STATA 12 software. Results: Analysis of 58 primary articles with a sample size of 902580 showed that the prevalence of HTN in Iran was 25% (with 95% CI of 22-28). The highest prevalence of HTN was related to elderly (42%). The prevalence of HTN was 25% (95% CI: 19-31) in women and 24% (95% CI: 20-28) in men with no significant difference (p = 0.758). The results also indicated that the prevalence of HTN was not related to the year of studies (p = 0.708) or sample size (p = 769). Conclusion: Despite the advancements in science and technology, along with health and prevention of diseases, the overall prevalence of HTN raised in Iran. Since HTN is a silent disease with significant health consequences and economic burden, programs designed to better HTN control seem vital to enhance community health.

scholarly journals 0636 Using AI To Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A243-A243
Author(s):  
W Hevener ◽  
B Beine ◽  
J Woodruff ◽  
D Munafo ◽  
C Fernandez ◽  
...  
Keyword(s):  
Effect Size ◽  
Statistical Significance ◽  
Post Intervention ◽  
Cpap Adherence ◽  
Cohen’S D ◽  
Usage Patterns ◽  
Patient Outreach ◽  
Cohen's D ◽  
The Impact ◽  
Usage Data

Abstract Introduction Clinical management of CPAP adherence remains an ongoing challenge. Behavioral and technical interventions such as patient outreach, coaching, troubleshooting, and resupply may be deployed to positively impact adherence. Previous authors have described adherence phenotypes that retrospectively categorize patients by discrete usage patterns. We design an AI model that predictively categorizes patients into previously studied adherence phenotypes and analyzes the statistical significance and effect size of several types of interventions on subsequent CPAP adherence. Methods We collected a cross-sectional cohort of subjects (N = 13,917) with 455 days of daily CPAP usage data acquired. Patient outreach notes and resupply data were temporally synchronized with daily CPAP usage. Each 30-days of usage was categorized into one of four adherence phenotypes as defined by Aloia et al. (2008) including Good Users, Variable Users, Occasional Attempters, and Non-Users. Cross-validation was used to train and evaluate a Recurrent Neural Network model for predicting future adherence phenotypes based on the dynamics of prior usage patterns. Two-sided 95% bootstrap confidence intervals and Cohen’s d statistic were used to analyze the significance and effect size of changes in usage behavior 30-days before and after administration of several resupply interventions. Results The AI model predicted the next 30-day adherence phenotype with an average of 90% sensitivity, 96% specificity, 95% accuracy, and 0.83 Cohen’s Kappa. The AI model predicted the number of days of CPAP non-use, use under 4-hours, and use over 4-hours for the next 30-days with OLS Regression R-squared values of 0.94, 0.88, and 0.95 compared to ground truth. Ten resupply interventions were associated with statistically significant increases in adherence, and ranked by adherence effect size using Cohen’s d. The most impactful were new cushions or masks, with a mean post-intervention CPAP adherence increase of 7-14% observed in Variable User, Occasional Attempter, and Non-User groups. Conclusion The AI model applied past CPAP usage data to predict future adherence phenotypes and usage with high sensitivity and specificity. We identified resupply interventions that were associated with significant increases in adherence for struggling patients. This work demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence. Support  

scholarly journals Stage 2 Registered Report: Anomalous perception in a Ganzfeld condition - A meta-analysis of more than 40 years investigation

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 234
Author(s):  
Patrizio E. Tressoldi ◽  
Lance Storm
Keyword(s):  
Regression Model ◽  
Effect Size ◽  
Close Agreement ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Questionable Research Practices ◽  
Effect Model ◽  
Meta Regression ◽  
Stage 1

This meta-analysis is an investigation into anomalous perception (i.e., conscious identification of information without any conventional sensorial means). The technique used for eliciting an effect is the ganzfeld condition (a form of sensory homogenization that eliminates distracting peripheral noise). The database consists of studies published between January 1974 and December 2020 inclusive. The overall effect size estimated both with a frequentist and a Bayesian random-effect model, were in close agreement yielding an effect size of .088 (.04-.13). This result passed four publication bias tests and seems not contaminated by questionable research practices. Trend analysis carried out with a cumulative meta-analysis and a meta-regression model with Year of publication as covariate, did not indicate sign of decline of this effect size. The moderators analyses show that selected participants outcomes were almost three-times those obtained by non-selected participants and that tasks that simulate telepathic communication show a two-fold effect size with respect to tasks requiring the participants to guess a target. The Stage 1 Registered Report can be accessed here: https://doi.org/10.12688/f1000research.24868.3

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