scholarly journals Dose-response meta-analysis: application and practice using the R software

2019 ◽  
Vol 41 ◽  
pp. e2019006 ◽  
Author(s):  
Sung Ryul Shim ◽  
Jonghoo Lee
Keyword(s):  
Dose Response ◽  
Binary Data ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Quadratic Model ◽  
R Software ◽  
Theoretical Concepts ◽  
Quantitative Synthesis ◽  
Analysis Application ◽  
The Difference

The objective of this study was to describe the general approaches of dose-response meta-analysis (DRMA) available for the quantitative synthesis of data using the R software. We conducted a DRMA using two types of data, the difference of means in continuous data and the odds ratio in binary data. The package commands of the R software were “doseresmeta” for the overall effect sizes that were separated into a linear model, quadratic model, and restricted cubic split model for better understanding. The effect sizes according to the dose and a test for linearity were demonstrated and interpreted by analyzing one-stage and two-stage DRMA. The authors examined several flexible models of exposure to pool study-specific trends and made a graphical presentation of the dose-response trend. This study focused on practical methods of DRMA rather than theoretical concepts for researchers who did not major in statistics. The authors hope that this study will help many researchers use the R software to perform DRMAs more easily, and that related research will be pursued.

scholarly journals Network meta-analysis: application and practice using R software

2019 ◽  
Vol 41 ◽  
pp. e2019013 ◽  
Author(s):  
Sung Ryul Shim ◽  
Seong-Jang Kim ◽  
Jonghoo Lee ◽  
Gerta Rücker
Keyword(s):  
Meta Analysis ◽  
Effect Sizes ◽  
Analysis Model ◽  
R Software ◽  
Data Synthesis ◽  
Theoretical Concepts ◽  
Moderator Effects ◽  
Analysis Application ◽  
R Packages ◽  
Meta Analyses

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.

scholarly journals Intervention meta-analysis: application and practice using R software

2019 ◽  
Vol 41 ◽  
pp. e2019008 ◽  
Author(s):  
Sung Ryul Shim ◽  
Seong-Jang Kim
Keyword(s):  
Publication Bias ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Forest Plot ◽  
R Software ◽  
Data Synthesis ◽  
Moderator Effect ◽  
Theoretical Concepts ◽  
Analysis Application ◽  
Meta Regression Analysis

The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.

scholarly journals Pre-post effect sizes should be avoided in meta-analyses

2016 ◽  
Vol 26 (4) ◽  
pp. 364-368 ◽  
Author(s):  
P. Cuijpers ◽  
E. Weitz ◽  
I. A. Cristea ◽  
J. Twisk
Keyword(s):  
Comparison Group ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Behaviour Therapy ◽  
Natural Processes ◽  
Cognitive Behaviour ◽  
Post Test ◽  
The Difference ◽  
Meta Analyses ◽  
Highly Cited

AimsThe standardised mean difference (SMD) is one of the most used effect sizes to indicate the effects of treatments. It indicates the difference between a treatment and comparison group after treatment has ended, in terms of standard deviations. Some meta-analyses, including several highly cited and influential ones, use the pre-post SMD, indicating the difference between baseline and post-test within one (treatment group).MethodsIn this paper, we argue that these pre-post SMDs should be avoided in meta-analyses and we describe the arguments why pre-post SMDs can result in biased outcomes.ResultsOne important reason why pre-post SMDs should be avoided is that the scores on baseline and post-test are not independent of each other. The value for the correlation should be used in the calculation of the SMD, while this value is typically not known. We used data from an ‘individual patient data’ meta-analysis of trials comparing cognitive behaviour therapy and anti-depressive medication, to show that this problem can lead to considerable errors in the estimation of the SMDs. Another even more important reason why pre-post SMDs should be avoided in meta-analyses is that they are influenced by natural processes and characteristics of the patients and settings, and these cannot be discerned from the effects of the intervention. Between-group SMDs are much better because they control for such variables and these variables only affect the between group SMD when they are related to the effects of the intervention.ConclusionsWe conclude that pre-post SMDs should be avoided in meta-analyses as using them probably results in biased outcomes.

Physical activity and multiple sclerosis: a meta-analysis

2005 ◽  
Vol 11 (4) ◽  
pp. 459-463 ◽  
Author(s):  
Robert W Motl ◽  
Edward McAuley ◽  
Erin M Snook
Keyword(s):  
Physical Activity ◽  
Multiple Sclerosis ◽  
Meta Analysis ◽  
Self Report ◽  
Weighted Mean ◽  
Physically Active ◽  
Manual Search ◽  
Relapsing Remitting ◽  
Quantitative Synthesis ◽  
The Difference

Using meta-analytic procedures, this study involved a quantitative synthesis of the difference in physical activity among individuals with multiple sclerosis (MS) compared with nondiseased and diseased populations and then examined factors (i.e., moderators) that explain variation in the overall difference in physical activity. We searched MEDLINE, PsycINFO and Current Contents Plus using the key words physical activity, exercise and physical fitness in conjunction with multiple sclerosis; conducted a manual search of bibliographies of the retrieved papers; and contacted study authors about additional studies. Overall, 53 effects were retrieved from 13 studies with 2360 MS participants and yielded a weighted mean effect size (ES) of -0.60 (95% CI= -0.44,-0.77). The weighted mean ES was heterogenous, Q=1164.11, df=52, p<0.0001. There were larger effects with objective versus self-report measures of physical activity, nondiseased versus diseased populations and primary progressive versus relapsing-remitting MS. The cumulative evidence suggests that individuals with MS are less physically active than nondiseased, but not diseased, populations.

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.

scholarly journals From problem-solving to sensemaking: A comparative meta-analysis of preparatory approaches for future learning

2021 ◽  
Author(s):  
Tanmay Sinha ◽  
Manu Kapur
Keyword(s):  
Problem Solving ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Bayesian Analyses ◽  
Ps I ◽  
Significant Difference ◽  
Small Effect Size ◽  
Moderate Effect ◽  
The Difference ◽  
Future Learning

Against the backdrop of a growing body of research showing the effectiveness of problem-solving activities followed by instruction (PS-I), we report a meta-analysis of the effectiveness of three broad categories of preparatory activities on future learning from instruction: (a) problem-solving followed by instruction (PS-I), (b) scaffolded problem-solving followed by instruction (+PS-I), or (c) an alternative sensemaking activity followed by instruction (!PS-I)? We examined 118 experimental comparisons spanning 33 articles that compared PS-I with +PS-I and !PS-I designs. Although scaffolding was descriptively associated with a small effect size, there was no significant difference relative to PS-I (Hedge’s g -0.08 [95% CI -0.20, 0.04]). Additionally, PS-I exhibited a non-significant moderate effect (Hedge’s g 0.22 [95% CI -0.06, 0.51]) compared to !PS-I. Bayesian analyses strongly favored the null hypothesis for the comparison of PS-I with +PS-I (suggesting a 99% probability of the difference in effect between these designs being less than 0.2), while it suggested a 40.37% probability of at least a moderate effect favoring PS-I relative to !PS-I. Further, the estimation of true effect sizes after accounting for the publication bias suggested moderate effect sizes in favor of PS-I, when considering both comparison conditions +PS-I (Hedge’s g 0.55) and !PS-I (Hedge’s g 0.64).

scholarly journals A point estimator of the probability difference of overall treatment effect–simulation study

2011 ◽  
Vol 50 (1) ◽  
pp. 25-38
Author(s):  
Pavla Krajíčková
Keyword(s):  
Clinical Trials ◽  
Binary Data ◽  
Standard Treatment ◽  
Meta Analysis ◽  
Box Plots ◽  
Multicentre Trials ◽  
Point Estimator ◽  
New Type ◽  
The Difference ◽  
New Treatment

ABSTRACT One of the aims of the meta-analysis of clinical trials is to deter- mine the efficacy of a new type of treatment. This efficacy is commonly measured by the difference between the efficacy of a standard treatment and the new treatment. For binary data the difference can be measured by a probability difference. We investigate, by simulations and using box plots, the basic statistical properties of the point estimator of the probability difference of overall treatment effects in the meta-analysis based on multicentre trials for various chosen situations. This estimator was suggested in Dokoupilova (2011).

A-25 Paced Auditory Serial Addition Test in Individuals with Cocaine Versus Methamphetamine Dependence

2019 ◽  
Vol 34 (6) ◽  
pp. 884-884
Author(s):  
A Pollard ◽  
A Hauson ◽  
N Stelmach ◽  
S Sarkissians ◽  
A Walker ◽  
...  
Keyword(s):  
Neuropsychological Functioning ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Data Synthesis ◽  
Methamphetamine Dependence ◽  
Significant Difference ◽  
Calculated Effect ◽  
Axis I ◽  
The Difference ◽  
Serial Addition

Abstract Objective Research suggests that cocaine and methamphetamine differ in their impact on executive functions (EF). The Paced Auditory Serial Addition Test (PASAT) is used to assess working memory; a component of EF. The purpose of this meta-analysis was to examine the difference between the effect of these two drugs on PASAT scores. Data Selection Three researchers independently searched nine databases (e.g., PsycINFO, Pubmed, ProceedingsFirst), extracted required data, and calculated effect sizes. Inclusion criteria identified studies that had (a) compared cocaine or methamphetamine dependent groups to healthy controls and (b) matched groups on either age, education, or IQ (at least 2 out of 3). Studies were excluded if participants were reported to have Axis I diagnoses (other than cocaine or methamphetamine dependence) or comorbidities known to impact neuropsychological functioning. Six articles were coded and analyzed for the current study. Data Synthesis Cocaine studies showed a medium statistically significant effect size (g = 0.370, p = 0.020), while methamphetamine did not (g = 0.198, p = 0.172). There was no heterogeneity in effect sizes for both drugs. Subgroup analysis found no significant difference between the two drugs on the PASAT (Q-between = 0.646, p = 0.421). Conclusions In contrast to methamphetamine, cocaine is associated with poorer performance on PASAT. This is in line with previous studies that found that cocaine had more significant impact on EF than methamphetamine. Given the preliminary nature of this meta-analysis and the small number of studies on the topic, future primary studies should directly contrast how these two drugs impact EF.

Mental Imagery Meta-analysis

2017 ◽  
Author(s):  
Dominic Conroy ◽  
Martin S Hagger
Keyword(s):  
Mental Imagery ◽  
Meta Analysis ◽  
Health Behaviours ◽  
Effect Sizes ◽  
Data Sets ◽  
Independent Data ◽  
Physiological Measures ◽  
Post Intervention ◽  
Moderator Effects ◽  
Quantitative Synthesis

This review provided a quantitative synthesis of the effectiveness of mental imagery interventions in health behaviour and tested key moderator effects. Thirty-three independent data sets were eligible for inclusion. Mental imagery interventions led to non-trivial, small averaged corrected effect sizes of imagery interventions on post-intervention behaviour (d+ = 0.23) and on psychological predictors of behavior (d+ = 0.08-0.19) and a small-to-medium sized on post-intervention physiological measures (d+ = 0.29). Moderation effects are also reported. Results support effects of mental imagery interventions on health behaviours, identifies the conditions where they may be more effective, and points to how future imagery interventions might be optimized.

scholarly journals Meta-Analysis of a Web-Based Disease Forecast System for Control of Anthracnose and Botrytis Fruit Rots of Strawberry in Southeastern United States

Plant Disease ◽  
2017 ◽  
Vol 101 (11) ◽  
pp. 1910-1917 ◽  
Author(s):  
Leandro G. Cordova ◽  
Laurence V. Madden ◽  
Achour Amiri ◽  
Guido Schnabel ◽  
Natalia A. Peres
Keyword(s):  
South Carolina ◽  
Meta Analysis ◽  
Fruit Rot ◽  
Effect Sizes ◽  
Colletotrichum Acutatum ◽  
Web Based ◽  
The Mean ◽  
Disease Forecast ◽  
The Difference ◽  
Marketable Fruit

Strawberry production in Florida and South Carolina is affected by two major diseases, anthracnose fruit rot (AFR) and Botrytis fruit rot (BFR), caused by Colletotrichum acutatum and Botrytis cinerea, respectively. The effective management of both diseases traditionally relied on weekly fungicide applications. However, to improve timing and reduce the number of fungicide sprays, many growers follow the Strawberry Advisory System (StAS), a decision support system for forecasting fungicide applications based on environmental conditions and previously developed models. The objective of this study was to perform a meta-analysis to determine the effectiveness of the StAS for AFR and BFR management compared with a calendar-based spray program. Thirty-nine trials were conducted from 2009 to 2014 in Florida and South Carolina commercial strawberry fields. Meta-analysis was conducted to quantify the treatment effects on four effect sizes, all based on the difference in response variables for StAS and the calendar-based treatments in each trial. The mean difference in BFR incidence, AFR incidence, yield, and number of marketable fruit between the two treatments was not significantly different from 0 (P < 0.05). However, the number of fungicide applications per season was reduced by a median of seven when using the StAS, a 50% reduction in sprays compared with the calendar-based approach. Effect sizes were not influenced by location or the favorability of the environment for disease development. These findings indicate that use of StAS in commercial fields is effective in controlling fruit rot diseases with no reduction in yield while substantially reducing fungicide applications.

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