Collecting and Delivering Client Feedback

2019 ◽  
pp. 580-630 ◽  
Author(s):  
Michael J. Lambert ◽  
Jason L. Whipple ◽  
Maria Kleinstäuber
Keyword(s):  
Meta Analysis ◽  
Effect Sizes ◽  
Routine Outcome Monitoring ◽  
Outcome Questionnaire ◽  
Client Feedback ◽  
Outcome Monitoring ◽  
Clinically Significant ◽  
Meta Analyses ◽  
Client Progress ◽  
The Impact

This meta-analysis examines the impact of measuring, monitoring, and feeding back information on client progress to clinicians while they deliver psychotherapy. It considers the effects of the two most frequently studied routine outcome monitoring practices: the Partners for Change Outcome System and the Outcome Questionnaire System. Meta-analyses of 24 studies produced effect sizes from small to moderate. Feedback practices reduced deterioration rates and nearly doubled clinically significant/reliable change rates in clients who were predicted to have a poor outcome. Clinical examples, diversity considerations, and therapeutic advances are provided.

scholarly journals A protocol for the systematic review and meta-analysis of thigmotactic behaviour in the open field test in rodent models associated with persistent pain

2021 ◽  
Vol 5 (1) ◽  
pp. e100135
Author(s):  
Xue Ying Zhang ◽  
Jan Vollert ◽  
Emily S Sena ◽  
Andrew SC Rice ◽  
Nadia Soliman
Keyword(s):  
Systematic Review ◽  
Outcome Measure ◽  
Meta Analysis ◽  
Persistent Pain ◽  
Effect Sizes ◽  
Bias Assessment ◽  
Animal Pain ◽  
Trim And Fill ◽  
Meta Analyses ◽  
The Impact

ObjectiveThigmotaxis is an innate predator avoidance behaviour of rodents and is enhanced when animals are under stress. It is characterised by the preference of a rodent to seek shelter, rather than expose itself to the aversive open area. The behaviour has been proposed to be a measurable construct that can address the impact of pain on rodent behaviour. This systematic review will assess whether thigmotaxis can be influenced by experimental persistent pain and attenuated by pharmacological interventions in rodents.Search strategyWe will conduct search on three electronic databases to identify studies in which thigmotaxis was used as an outcome measure contextualised to a rodent model associated with persistent pain. All studies published until the date of the search will be considered.Screening and annotationTwo independent reviewers will screen studies based on the order of (1) titles and abstracts, and (2) full texts.Data management and reportingFor meta-analysis, we will extract thigmotactic behavioural data and calculate effect sizes. Effect sizes will be combined using a random-effects model. We will assess heterogeneity and identify sources of heterogeneity. A risk-of-bias assessment will be conducted to evaluate study quality. Publication bias will be assessed using funnel plots, Egger’s regression and trim-and-fill analysis. We will also extract stimulus-evoked limb withdrawal data to assess its correlation with thigmotaxis in the same animals. The evidence obtained will provide a comprehensive understanding of the strengths and limitations of using thigmotactic outcome measure in animal pain research so that future experimental designs can be optimised. We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines and disseminate the review findings through publication and conference presentation.

scholarly journals Influence of Pilot and Small Trials in Meta-Analyses of Behavioral Interventions: A Meta-epidemiological Study

2020 ◽  
Author(s):  
Michael W. Beets ◽  
R. Glenn Weaver ◽  
John P.A. Ioannidis ◽  
Alexis Jones ◽  
Lauren von Klinggraeff ◽  
...  
Keyword(s):  
Sample Size ◽  
Behavioral Interventions ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Feasibility Studies ◽  
Small Sample ◽  
Effect Sizes ◽  
Absolute Difference ◽  
Meta Analyses ◽  
The Impact

Abstract Background: Pilot/feasibility or studies with small sample sizes may be associated with inflated effects. This study explores the vibration of effect sizes (VoE) in meta-analyses when considering different inclusion criteria based upon sample size or pilot/feasibility status. Methods: Searches were conducted for meta-analyses of behavioral interventions on topics related to the prevention/treatment of childhood obesity from 01-2016 to 10-2019. The computed summary effect sizes (ES) were extracted from each meta-analysis. Individual studies included in the meta-analyses were classified into one of the following four categories: self-identified pilot/feasibility studies or based upon sample size (N≤100, N>100, and N>370 the upper 75th of sample size). The VoE was defined as the absolute difference (ABS) between the re-estimations of summary ES restricted to study classifications compared to the originally reported summary ES. Concordance (kappa) of statistical significance between summary ES was assessed. Fixed and random effects models and meta-regressions were estimated. Three case studies are presented to illustrate the impact of including pilot/feasibility and N≤100 studies on the estimated summary ES.Results: A total of 1,602 effect sizes, representing 145 reported summary ES, were extracted from 48 meta-analyses containing 603 unique studies (avg. 22 avg. meta-analysis, range 2-108) and included 227,217 participants. Pilot/feasibility and N≤100 studies comprised 22% (0-58%) and 21% (0-83%) of studies. Meta-regression indicated the ABS between the re-estimated and original summary ES where summary ES were comprised of ≥40% of N≤100 studies was 0.29. The ABS ES was 0.46 when summary ES comprised of >80% of both pilot/feasibility and N≤100 studies. Where ≤40% of the studies comprising a summary ES had N>370, the ABS ES ranged from 0.20-0.30. Concordance was low when removing both pilot/feasibility and N≤100 studies (kappa=0.53) and restricting analyses only to the largest studies (N>370, kappa=0.35), with 20% and 26% of the originally reported statistically significant ES rendered non-significant. Reanalysis of the three case study meta-analyses resulted in the re-estimated ES rendered either non-significant or half of the originally reported ES. Conclusions: When meta-analyses of behavioral interventions include a substantial proportion of both pilot/feasibility and N≤100 studies, summary ES can be affected markedly and should be interpreted with caution.

scholarly journals Peto odds ratios demonstrate no advantage over classic odds ratios in meta-analysis of binary rare outcomes

2020 ◽  
Author(s):  
Chang Xu ◽  
Luis Furuya-Kanamori ◽  
Lifeng Lin ◽  
Suhail A. Doi
Keyword(s):  
Event Rate ◽  
Meta Analysis ◽  
Rare Event ◽  
Effect Sizes ◽  
Binary Outcomes ◽  
Odds Ratios ◽  
P Values ◽  
Cochrane Database ◽  
Meta Analyses ◽  
The Impact

AbstractIn this study, we examined the discrepancy between large studies and small studies in meta-analyses of rare event outcomes and the impact of Peto versus the classic odds ratios (ORs) through empirical data from the Cochrane Database of Systematic Reviews that collected from January 2003 to May 2018. Meta-analyses of binary outcomes with rare events (event rate ≤5%), with at least 5 studies, and with at least one large study (N≥1000) were extracted. The Peto and classic ORs were used as the effect sizes in the meta-analyses, and the magnitude and direction of the ORs of the meta-analyses of large studies versus small studies were compared. The p-values of the meta-analyses of small studies were examined to assess if the Peto and the classic OR methods gave similar results. Totally, 214 meta-analyses were included. Over the total 214 pairs of pooled ORs of large studies versus pooled small studies, 66 (30.84%) had a discordant direction (kappa=0.33) when measured by Peto OR and 69 (32.24%) had a discordant direction (kappa=0.22) when measured by classic OR. The Peto ORs resulted in smaller p-values compared to classic ORs in a substantial (83.18%) number of cases. In conclusion, there is considerable discrepancy between large studies and small studies among the results of meta-analyses of sparse data. The use of Peto odds ratios does not improve this situation and is not recommended as it may result in less conservative error estimation.

scholarly journals A Meta-Analysis: Improvement of Students’ Algebraic Reasoning through Metacognitive Training

2018 ◽  
Vol 11 (10) ◽  
pp. 42 ◽  
Author(s):  
Yujin Lee ◽  
Mary M. Capraro ◽  
Robert M. Capraro ◽  
Ali Bicer
Keyword(s):  
Effect Size ◽  
Positive Impact ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Algebraic Reasoning ◽  
Metacognitive Training ◽  
Mathematical Performance ◽  
Meta Analyses ◽  
The Impact

Although algebraic reasoning has been considered as an important factor influencing students’ mathematical performance, many students struggle to build concrete algebraic reasoning. Metacognitive training has been regarded as one effective method to develop students’ algebraic reasoning; however, there are no published meta-analyses that include an examination of the effects of metacognitive training on students’ algebraic reasoning. Therefore, the purpose of this meta-analysis was to examine the impact of metacognitive training on students’ algebraic reasoning. Eighteen studies with 22 effect sizes were selected for inclusion in the present meta-analysis. In the process of the analysis, one study was determined as an outlier; therefore, another meta-analysis was reconstructed without the outlier to calculate more robust results. The findings indicated that the overall effect size without an outlier equaled d=0.973 with SE=0.196. Q=20.201 (p<.05) and I2=0.997, which indicated heterogeneity of the studies. These results showed that the metacognitive training had a statistically significant positive impact on students’ algebraic reasoning.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

scholarly journals Effects of Sport-Based Interventions on Children’s Executive Function: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 755
Author(s):  
Falonn Contreras-Osorio ◽  
Christian Campos-Jara ◽  
Cristian Martínez-Salazar ◽  
Luis Chirosa-Ríos ◽  
Darío Martínez-García
Keyword(s):  
Systematic Review ◽  
Working Memory ◽  
Executive Function ◽  
Inhibitory Control ◽  
Cognitive Flexibility ◽  
Meta Analysis ◽  
Scientific Evidence ◽  
Healthy Children ◽  
Meta Analyses ◽  
The Impact

One of the most studied aspects of children’s cognitive development is that of the development of the executive function, and research has shown that physical activity has been demonstrated as a key factor in its enhancement. This meta-analysis aims to assess the impact of specific sports interventions on the executive function of children and teenagers. A systematic review was carried out on 1 November 2020 to search for published scientific evidence that analysed different sports programs that possibly affected executive function in students. Longitudinal studies, which assessed the effects of sports interventions on subjects between 6 and 18 years old, were identified through a systematic search of the four principal electronic databases: Web of Science, PubMed, Scopus, and EBSCO. A total of eight studies, with 424 subjects overall, met the inclusion criteria and were classified based on one or more of the following categories: working memory, inhibitory control, and cognitive flexibility. The random-effects model for meta-analyses was performed with RevMan version 5.3 to facilitate the analysis of the studies. Large effect sizes were found in all categories: working memory (ES −1.25; 95% CI −1.70; −0.79; p < 0.0001); inhibitory control (ES −1.30; 95% CI −1.98; −0.63; p < 0.00001); and cognitive flexibility (ES −1.52; 95% CI −2.20; −0.83; p < 0.00001). Our analysis concluded that healthy children and teenagers should be encouraged to practice sports in order to improve their executive function at every stage of their development.

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.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

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.

Sign in / Sign up

Export Citation Format

Share Document