Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance (original) (raw)
Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis
PLOS ONE, 2021
Background The proposed sequential and combinatorial algorithm, suggested as a standard tool for assessing, exploring, and reporting heterogeneity in the meta-analysis, is useful but time-consuming particularly when the number of included studies is large. Metaplot is a novel graphical approach that facilitates performing sensitivity analysis to distinguish the source of substantial heterogeneity across studies with ease and speed. Method Metaplot is a Stata module based on Stata’s commands, known informally as "ado". Metaplot presents a two-way (x, y) plot in which the x-axis represents the study codes and the y-axis represents the values of I2 statistics excluding one study at a time (n-1 studies). Metaplot also produces a table in the ’Results window’ of the Stata software including details such as I2 and χ2 statistics and their P-values omitting one study in each turn. Results Metaplot allows rapid identification of studies that have a disproportionate impact on hetero...
PloS one, 2024
Orthodontic systematic reviews (SRs) use different methods to pool the individual studies in a meta-analysis when indicated. However, the number of studies included in orthodontic meta-analyses is relatively small. This study aimed to evaluate the direction of estimate changes of orthodontic meta-analyses (MAs) using different between-study variance methods considering the level of heterogeneity when few trials were pooled. Search and study selection: Systematic reviews (SRs) published over the last three years, from the 1 st of January 2020 to the 31 st of December 2022, in six main orthodontic journals with at least one MA pooling five or lesser primary studies were identified. Data collection and analysis: Data were extracted from each eligible MA, which was replicated in a random effect model using DerSimonian and Laird (DL), Paule-Mandel (PM), Restricted maximumlikelihood (REML), Hartung Knapp and Sidik Jonkman (HKSJ) methods. The results were reported using median and interquartile range (IQR) for continuous data and frequencies for categorical data and analyzed using non-parametric tests. The Boruta algorithm was used to assess the significant predictors for the significant change in the confidence interval between the different methods compared to the DL method, which was only feasible using the HKSJ method. 146 MAs were included, most applying the random effect model (n = 111; 76%) and pooling continuous data using mean difference (n = 121; 83%). The median number of studies was three (range 2, 4), and the overall statistical heterogeneity (I 2 ranged from 0 to 99% with a median of 68%). Close to 60% of the significant findings became non-significant when HKSJ
Network meta-analysis: application and practice using Stata
Epidemiology and health, 2017
This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions-similarity, transitivity, and consistency-should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from ...
Using the normal quantile plot to explore meta-analytic data sets
Psychological Methods, 1998
In a meta-analysis, graphical displays can be used to check statistical assumptions for numerical procedures and they can be used to discover important patterns in the data. The authors propose the normal quantile plot as a preferred alternative to the funnel plot for such purposes. The normal quantile plot, like the funnel plot, can be used to investigate whether all studies come from a single population and to search for publication bias. However, the normal quantile plot is easier to interpret than the funnel plot, especially when it includes 95% confidence bands. In addition, the normal quantile plot can be used to check the normality assumption for numerical procedures. The funnel plot cannot be used for this latter purpose.
The dilemma of heterogeneity tests in Meta-analysis: a challenge from a simulation study
Introduction After several decades’ development, meta-analysis has become the pillar of evidence-based medicine. However, heterogeneity is still the threat to the validity and quality of such studies. Currently, Q and its descendant I2 (I square) tests are widely used as the tools for heterogeneity evaluation. The core mission of this kind of test is to identify data sets from similar populations and exclude those are from different populations. Although Q and I2 are used as the default tool for heterogeneity testing, the work we present here demonstrates that the robustness of these two tools is questionable. Methods and Findings We simulated a strictly normalized population S. The simulation successfully represents randomized control trial data sets, which fits perfectly with the theoretical distribution (experimental group: p = 0.37, control group: p = 0.88). And we randomly generate research samples Si that fits the population with tiny distributions. In short, these data sets are perfect and can be seen as completely homogeneous data from the exactly same population. If Q and I2 are truly robust tools, the Q and I2 testing results on our simulated data sets should not be positive. We then synthesized these trials by using fixed model. Pooled results indicated that the mean difference (MD) corresponds highly with the true values, and the 95% confidence interval (CI) is narrow. But, when the number of trials and sample size of trials enrolled in the meta-analysis are substantially increased; the Q and I2 values also increase steadily. This result indicates that I2 and Q are only suitable for testing heterogeneity amongst small sample size trials, and are not adoptable when the sample sizes and the number of trials increase substantially. Conclusions Every day, meta-analysis studies which contain flawed data analysis are emerging and passed on to clinical practitioners as “updated evidence”. Using this kind of evidence that contain heterogeneous data sets leads to wrong conclusion, makes chaos in clinical practice and weakens the foundation of evidence-based medicine. We suggest more strict applications of meta-analysis: it should only be applied to those synthesized trials with small sample sizes. We call upon that the tools of evidence-based medicine should keep up-to-dated with the cutting-edge technologies in data science. Clinical research data should be made available publicly when there is any relevant article published so the research community could conduct in-depth data mining, which is a better alternative for meta-analysis in many instances.
Title Tools for the Analysis of Epidemiological Data Author
2016
April 7, 2017 Version 0.9-82 Date 2017-04-07 Title Tools for the Analysis of Epidemiological Data Author Mark Stevenson <mark.stevenson1@unimelb.edu.au> with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida, Geoff Jones, Sarah Pirikahu, Simon Firestone and Ryan Kyle Maintainer Mark Stevenson <mark.stevenson1@unimelb.edu.au> Description Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations. Depends R (>= 3.0.0), survival Imports BiasedUrn, methods ...
Odd man out: a graphical approach to meta-analysis
American Journal of Public Health, 1988
The areas of overlap of confidence intervals from several studies can be used to create summary confidence regions. These summary regions lend themselves to graphical display, have readily derivable statistical properties, and can carry information
Cross-hairs: a scatterplot for meta-analysis in R
Research Synthesis Methods, 2016
We describe a meta-analytic scatterplot that indicates precision of points for two variables paired within studies; this is equivalent in form to a 'cross-hairs' plot used to portray specificity and sensitivity in diagnostic testing. At the user's discretion, the plot also displays boxplots for each of the X and Y variable distributions, means for each of the variables, and the correlation between the two. The cross-hairs may be suppressed for dense point clouds. The program is written in R, so it can be modified by the user and can serve as a companion to existing meta-analysis programs. Some of the program's novel uses are described and illustrated with (1) independent effect sizes, (2) dependent effect sizes, and (3) shrunken estimates.
Iranian Journal of Public Health, 2021
Background: Nowadays, statistical methods are used frequently in research articles. This review study aimed to determine the statistical methods used in original articles published in Iranian journal of public health (IJPH). Methods: Original articles in the period 2015 to 2019 from volumes 44 to 48 and numbers 1 to 12 were reviewed by a 3-member committee consisting of a statistician and two health researchers. The statistical methods, sample size, study design and population, type of used software were investigated. Multiple response analysis (MRA), Kruskal–Wallis test and Spearman correlation coefficient were used to data analysis. All analyzes were performed in SPSS21 software. Significant level was set at 0.05. Results: Statistical population in most of the articles were related to human samples at the field level (36% and 297 articles). 66.6% (549 articles) had the sample size less than 500 cases. Study design in most of them were analytical observational 56.2% (464 cases). Ac...
International Statistical Review, 2008
SHORT BOOK REVIEWS Chapter 3 uses graphical methods to diagnose the distribution and seriousness of missing values, and to assess the results of imputation. Chapter 4 describes supervised classification, known also as discriminant analysis. Examples, with extensive use of graphs, give insight into the data and into results. Methods that are described are: linear discriminant analysis, trees, random forests, neural networks and Support Vector Machines. The discussion of random forests does not explain how the two-dimensional representation in Figure 4.11 was obtained. This will leave some readers puzzled. Cluster analysis methods described in Chapter 5 include hierarchical, projection pursuit, model-based methods, and self-organizing maps. Again, graphical presentations are used to good effect. Two distance measures are explicitly mentioned-Euclidean distance with equal variable weights, and a correlation based measure of difference in profile. The effect and common importance of transformation is not discussed. Chapter 6 takes up on several further topics-inference, network data and multi-dimensional scaling. For detail, including discussion of some of the traps (e.g., selection effects with very highdimensional data), readers will need to look elsewhere. This book is however a very useful brief overview of the insight that a powerful modern suite of graphics tools may offer. Data sets, and other supplementary materials, are available from the web site http://www. ggobi.org/book/.