An Application of Meta-Analysis in Food Safety Consumer Research To Evaluate Consumer Behaviors and Practices

2004 ◽  
Vol 67 (11) ◽  
pp. 2587-2595 ◽  
Author(s):  
SUMEET R. PATIL ◽  
ROBERTA MORALES ◽  
SHERYL CATES ◽  
DONALD ANDERSON ◽  
DAVID KENDALL
Keyword(s):  
Food Safety ◽  
Consumer Research ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Ground Beef ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Sampling Errors ◽  
Weighted Averages ◽  
Combining Information

Meta-analysis provides a structured method for combining results from several studies and accounting for and differentiating between study variables. Numerous food safety consumer research studies often focus on specific behaviors among different subpopulations but fail to provide a holistic picture of consumer behavior. Combining information from several studies provides a broader understanding of differences and trends among demographic subpopulations, and thus, helps in developing effective risk communication messages. In the illustrated example, raw/undercooked ground beef consumption and hygienic practices were evaluated according to gender, ethnicity, and age. Percentages of people engaging in each of the above behaviors (referred to as effect sizes) were combined using weighted averages of these percentages. Several measures, including sampling errors, random variance between studies, sample sizes of studies, and homogeneity of findings across studies, were used in the meta-analysis. The statistical significance of differences in behaviors across demographic segments was evaluated using analysis of variance. The meta-analysis identified considerable variability in effect sizes for raw/undercooked ground beef consumption and poor hygienic practices. More males, African Americans, and adults between 30 and 54 years (midage) consumed raw/undercooked ground beef than other demographic segments. Males, Caucasians, and Hispanics and young adults between 18 and 29 years were more likely to engage in poor hygienic practices. Compared to traditional qualitative review methods, meta-analysis quantitatively accounts for interstudy differences, allows greater consideration of data from studies with smaller sample sizes, and offers ease of analysis as newer data become available, and thus, merits consideration for its application in food safety consumer research.

scholarly journals Conditional power of antidepressant network meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lisa Holper
Keyword(s):  
Meta Analysis ◽  
Indirect Evidence ◽  
Dropout Rate ◽  
Conclusive Evidence ◽  
Effect Sizes ◽  
Current Evidence ◽  
Hamilton Depression Scale ◽  
Conditional Power ◽  
Sample Sizes ◽  
Antidepressant Treatments

Abstract Background Conditional power of network meta-analysis (NMA) can support the planning of randomized controlled trials (RCTs) assessing medical interventions. Conditional power is the probability that updating existing inconclusive evidence in NMA with additional trial(s) will result in conclusive evidence, given assumptions regarding trial design, anticipated effect sizes, or event probabilities. Methods The present work aimed to estimate conditional power for potential future trials on antidepressant treatments. Existing evidence was based on a published network of 502 RCTs conducted between 1979-2018 assessing acute antidepressant treatment in major depressive disorder (MDD). Primary outcomes were efficacy in terms of the symptom change on the Hamilton Depression Scale (HAMD) and tolerability in terms of the dropout rate due to adverse events. The network compares 21 antidepressants consisting of 231 relative treatment comparisons, 164 (efficacy) and 127 (tolerability) of which are currently assumed to have inconclusive evidence. Results Required sample sizes to achieve new conclusive evidence with at least 80% conditional power were estimated to range between N = 894 - 4190 (efficacy) and N = 521 - 1246 (tolerability). Otherwise, sample sizes ranging between N = 49 - 485 (efficacy) and N = 40 - 320 (tolerability) may require stopping for futility based on a boundary at 20% conditional power. Optimizing trial designs by considering multiple trials that contribute both direct and indirect evidence, anticipating alternative effect sizes or alternative event probabilities, may increase conditional power but required sample sizes remain high. Antidepressants having the greatest conditional power associated with smallest required sample sizes were identified as those on which current evidence is low, i.e., clomipramine, levomilnacipran, milnacipran, nefazodone, and vilazodone, with respect to both outcomes. Conclusions The present results suggest that conditional power to achieve new conclusive evidence in ongoing or future trials on antidepressant treatments is low. Limiting the use of the presented conditional power analysis are primarily due to the estimated large sample sizes which would be required in future trials as well as due to the well-known small effect sizes in antidepressant treatments. These findings may inform researchers and decision-makers regarding the clinical relevance and justification of research in ongoing or future antidepressant RCTs in MDD.

scholarly journals A meta-analytical answer to the crisis of confidence of Psychology

2019 ◽  
Vol 35 (2) ◽  
pp. 350-356 ◽  
Author(s):  
Juan Botella ◽  
Juan I. Durán
Keyword(s):  
Publication Bias ◽  
Methodological Quality ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Effect Sizes ◽  
Natural Unit ◽  
Valuable Contribution ◽  
Analytical Perspective ◽  
Object Of Study ◽  
Personal Interests

Meta-analysis is a firmly established methodology and an integral part of the process of generating knowledge across the empirical sciences. Meta-analysis has also focused on methodology and has become a dominant critic of methodological shortcomings. We highlight several problematic issues on how we research in psychology: excess of heterogeneity in the results and difficulties for replication, publication bias, suboptimal methodological quality, and questionable practices of the researchers. These and other problems led to a “crisis of confidence” in psychology. We discuss how the meta-analytical perspective and its procedures can help to overcome the crisis. A more cooperative perspective, instead of a competitive one, can shift to consider replication as a more valuable contribution. Knowledge cannot be based in isolated studies. Given the nature of the object of study of psychology the natural unit to generate knowledge must be the estimated distribution of the effect sizes, not the dichotomous decision on statistical significance in specific studies. Some suggestions are offered on how to redirect researchers' research and practices, so that their personal interests and those of science as such are better aligned.

Can Reliance be Placed on a Single Meta-Analysis?

1990 ◽  
Vol 24 (3) ◽  
pp. 405-415 ◽  
Author(s):  
Nathaniel McConaghy
Keyword(s):  
Literature Review ◽  
Effect Size ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Effect Sizes ◽  
Control Groups ◽  
Consistent Finding ◽  
Placebo Controls ◽  
Effect Of Treatment ◽  
Meta Analyses

Meta-analysis replaced statistical significance with effect size in the hope of resolving controversy concerning evaluation of treatment effects. Statistical significance measured reliability of the effect of treatment, not its efficacy. It was strongly influenced by the number of subjects investigated. Effect size as assessed originally, eliminated this influence but by standardizing the size of the treatment effect could distort it. Meta-analyses which combine the results of studies which employ different subject types, outcome measures, treatment aims, no-treatment rather than placebo controls or therapists with varying experience can be misleading. To ensure discussion of these variables meta-analyses should be used as an aid rather than a substitute for literature review. While meta-analyses produce contradictory findings, it seems unwise to rely on the conclusions of an individual analysis. Their consistent finding that placebo treatments obtain markedly higher effect sizes than no treatment hopefully will render the use of untreated control groups obsolete.

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.

Tighter nets for smaller fishes: Mapping the development of statistical practices in consumer research between 2011 and 2018

2020 ◽  
Author(s):  
Antonia Krefeld-Schwalb ◽  
Benjamin Scheibehenne
Keyword(s):  
Social Science ◽  
Consumer Research ◽  
Marketing Research ◽  
Consumer Psychology ◽  
Effect Sizes ◽  
Sample Sizes ◽  
P Values ◽  
Central Hypothesis ◽  
Empirical Social Science ◽  
Over Time

Following vital discussion around the replicability of published findings, researchers demanded increased efforts to improve research practices in empirical social science. Consequentially, journals publishing consumer research implemented new measures to increase the replicability of published work. Nonetheless, no systematic empirical analysis on a large sample has investigated whether published consumer research has changed along with the discussion. To address this need, we surveyed three indicators for the replicability of published consumer research over time. We used text mining to quantify sample sizes, effect sizes, and the distribution of published p-values from a sample of N = 923 articles published between 2011 and 2018 in the Journal of Marketing Research, the Journal of Consumer Psychology, and the Journal of Consumer Research. To test the developments over time, we focused on a subsample of hand-coded articles and identified central hypothesis tests herein. Results show a trend toward increased sample sizes and decreased effect sizes across all three journals in the subset as well as the entire set of articles.

Unsuccessful Treatments of “Writer's Block”: A Meta-Analysis

2014 ◽  
Vol 115 (1) ◽  
pp. 276-278 ◽  
Author(s):  
Derrick C. McLean ◽  
Benjamin R. Thomas
Keyword(s):  
Meta Analysis ◽  
Small Sample ◽  
The Body ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Writer's Block ◽  
Unsuccessful Treatment ◽  
Group Treatments ◽  
Writer’S Block ◽  
Small Sample Sizes

A wide literature of the unsuccessful treatment of writer's block has emerged since the early 1970's. Findings within this literature seem to confer generalizability of this procedure; however, small sample sizes may limit this interpretation. This meta-analysis independently analyzed effect sizes for “self-treatments” and “group-treatments” using number of words in the body of the publication as indication of a failure to treat writer's block. Results of the reported findings suggest that group-treatments tend to be slightly more unsuccessful than self-treatments.

Power Statistics for Meta-analysis: Tests for Mean Effects and Homogeneity

2013 ◽  
Author(s):  
Marc J. Lajeunesse
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Experimental Sample ◽  
Sample Sizes ◽  
Homogeneity Tests ◽  
Sufficient Power ◽  
Experimental Effect ◽  
The Common

The common justification for meta-analysis is the increased statistical power to detect effects over what is obtained from individual studies. For ecologists and evolutionary biologists, the statistical power of meta-analysis is important because effect sizes are usually relatively small in these fields, and experimental sample sizes are often limited for logistic reasons. Consequently, many studies lack sufficient power to detect an experimental effect should it exist. This chapter provides a brief overview of the factors that determine the statistical power of meta-analysis. It presents statistics for calculating the power of pooled effect sizes to evaluate nonzero effects and the power of within- and between-study homogeneity tests. It also surveys ways to improve the statistical power of meta-analysis, and ends with a discussion on the overall utility of power statistics for meta-analysis.

scholarly journals A Systematic Review and Meta-Analysis of the Effects of Food Safety and Hygiene Training on Food Handlers

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1169
Author(s):  
Andrea Insfran-Rivarola ◽  
Diego Tlapa ◽  
Jorge Limon-Romero ◽  
Yolanda Baez-Lopez ◽  
Marco Miranda-Ackerman ◽  
...  
Keyword(s):  
Systematic Review ◽  
Food Safety ◽  
Effect Size ◽  
Meta Analysis ◽  
Practice Effect ◽  
Effect Sizes ◽  
Food Handlers ◽  
Training Interventions ◽  
Randomized Control ◽  
Knowledge Attitude And Practice

Foodborne diseases are a significant cause of morbidity and mortality worldwide. Studies have shown that the knowledge, attitude, and practices of food handlers are important factors in preventing foodborne illness. The purpose of this research is to assess the effects of training interventions on knowledge, attitude, and practice on food safety and hygiene among food handlers at different stages of the food supply chain. To this end, we conducted a systematic review and meta-analysis with close adherence to the PRISMA guidelines. We searched for training interventions among food handlers in five databases. Randomized control trials (RCT), quasi-RCTs, controlled before–after, and nonrandomized designs, including pre–post studies, were analyzed to allow a more comprehensive assessment. The meta-analysis was conducted using the random-effects model to calculate the effect sizes (Hedges’s g) and 95% confidence interval (CI). Out of 1094 studies, 31 were included. Results showed an effect size of 1.24 (CI = 0.89–1.58) for knowledge, an attitude effect size of 0.28 (CI = 0.07–0.48), and an overall practice effect size of 0.65 (CI = 0.24–1.06). In addition, subgroups of self-reported practices and observed practices presented effect sizes of 0.80 (CI = 0.13–1.48) and 0.45 (CI = 0.15–0.76) respectively.

Efficacy of interventions used by physiotherapists for patients with headache and migraine—systematic review and meta-analysis

Cephalalgia ◽  
2015 ◽  
Vol 36 (5) ◽  
pp. 474-492 ◽  
Author(s):  
Kerstin Luedtke ◽  
Angie Allers ◽  
Laura H Schulte ◽  
Arne May
Keyword(s):  
Systematic Review ◽  
Pain Intensity ◽  
Statistical Significance ◽  
Small Sample ◽  
Risk Of Bias ◽  
Effect Sizes ◽  
Sensitivity Analyses ◽  
Controlled Trials ◽  
Sample Sizes ◽  
Meta Analyses

Aim We aimed to conduct a systematic review evaluating the effectiveness of interventions used by physiotherapists on the intensity, frequency and duration of migraine, tension-type (TTH) and cervicogenic headache (CGH). Methods We performed a systematic search of electronic databases and a hand search for controlled trials. A risk of bias analysis was conducted using the Cochrane risk of bias tool (RoB). Meta-analyses present the combined mean effects; sensitivity analyses evaluate the influence of methodological quality. Results Of 77 eligible trials, 26 were included in the RoB assessment. Twenty trials were included in meta-analyses. Nineteen out of 26 trials had a high RoB in >1 domain. Meta-analyses of all trials indicated a reduction of TTH ( p < 0.0001; mean reduction −1.11 on a 0–10 visual analog scale (VAS); 95% CI −1.64 to −0.57) and CGH ( p = 0.0002; mean reduction −2.52 on a 0–10 VAS; 95% CI −3.86 to −1.19) pain intensity, CGH frequency ( p < 0.00001; mean reduction −1.34 days per month; 95% CI −1.40 to −1.28), and migraine ( p = 0.0001; mean reduction −22.39 hours without relief; 95% CI −33.90 to −10.88) and CGH ( p < 0.00001; mean reduction −1.68 hours per day; 95% CI −2.09 to −1.26) duration. Excluding high RoB trials increased the effect sizes and reached additional statistical significance for migraine pain intensity ( p < 0.00001; mean reduction −1.94 on a 0–10 VAS; 95% CI −2.61 to −1.27) and frequency ( p < 0.00001; mean reduction −9.07 days per month; 95% CI −9.52 to −8.62). Discussion Results suggest a statistically significant reduction in the intensity, frequency and duration of migraine, TTH and CGH. Pain reduction and reduction in CGH frequency do not reach clinically relevant effect sizes. Small sample sizes, inadequate use of headache classification, and other methodological shortcomings reduce the confidence in these results. Methodologically sound, randomized controlled trials with adequate sample sizes are required to provide information on whether and which physiotherapy approach is effective. According to Grading of Recommendations Assessment, Development and Evaluation (GRADE), the current level of evidence is low.

scholarly journals A Systematic Review and Meta-Analysis on the Relationship between Body Dissatisfaction and Morbid Exercise Behaviour

2021 ◽  
Vol 18 (2) ◽  
pp. 585
Author(s):  
Manuel Alcaraz-Ibáñez ◽  
Adrian Paterna ◽  
Álvaro Sicilia ◽  
Mark D. Griffiths
Keyword(s):  
Body Dissatisfaction ◽  
Longitudinal Research ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Sampling Errors ◽  
Exercise Behaviour ◽  
Moderate Effect Size ◽  
Causal Nature ◽  
The Relationship ◽  
Bare Bones

Background: The present study aimed to quantify the relationship between body dissatisfaction and morbid exercise behaviour (MEB). Methods: The electronic databases MEDLINE, PsycINFO, Web of Science, SciELO, and Dissertations & Theses Global were searched from inception to September 2020. Pooled effect sizes corrected for sampling errors (r+) were computed using a bare-bones meta-analysis. The robustness of the results was examined by influence analyses. The presence of moderators was examined by inspection of the variance in r+ attributable to sampling errors and 80% credibility intervals, followed by subgroup analysis and univariable/multivariable meta-regressions. Publication bias was examined by visual inspection of funnel plot symmetry, cumulative meta-analysis, and Egger’s test. Results: A total of 41 effect sizes from 33 studies (n = 8747) were retrieved. Results showed a significant and near to moderate effect size (r+ = 0.267, 95% CI = 0.226 to 0.307), and this did not differ by gender, BMI, age, percentage of Whites, study quality, or MEB measure. Conversely, effect sizes were found to be stronger in published and more recently conducted studies. Conclusion: The findings indicate that body dissatisfaction is one of the likely causes underlying MEB. This suggests the need for further longitudinal research aimed at confirming the potential causal nature of this relationship.

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