Science method

Meta-Analysis - Science method

Works consisting of studies using a quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc. It is often an overview of clinical trials. It is usually called a meta-analysis by the author or sponsoring body and should be differentiated from reviews of literature.
Questions related to Meta-Analysis
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I am conducting a meta analysis using mean change from baseline scores for BMD.
I have calculated the absolute mean difference for intervention and control groups for each study. I have calculated within-group SD from reported 95% CI according to Cochrane recommendations. For studies with missing data I imputed SD by calculating the correlation coefficient from studies that reported 95% CI.
I have then input my data (means/SDs/Ns) into Revman using a random effect model and generated forest plots. The statistical test for heterogeneity all show a Tau2/I2 statistic of 0% although Chi2 statistic, df and P values are all reported. This seems to be an error? Does anyone have an explanation for this or can offer any advice?
Thanks,
Alison
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There is nothing inherently wrong with having a tau-squared (or I-squared) of 0. A tau-squared of 0 indicates that based on your point estimate, the included studies are homogeneous, i.e., all observed variability is attributable to the sampling variance. In such a situation, the random-effects model is reduced to a fixed-effect model. (I-squared is the ratio of true heterogeneity-tau-squared- to the overall heterogeneity, so if tau-squared = 0, I-squared will also be 0.)
I can think of two recommendations:
1) it may be a good idea to report 95%CI for tau- and I-squared (Note that tau2 = 0 is just an estimate, and it may not be reasonable to assume that the studies are truly homogeneous, with absolutely no true variability). However, I doubt if RevMan provides you with CI, but package "metafor" does-if you are familiar with R.
2) As a sensitivity analysis, you can fix tau-squared to other sensible values to see how your results will change. (Again, I doubt that RevMan let you fix tau-squared to a specific value, but package "metafor" does.)
Good luck with your work.
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I am performing a meta-analysis where I have similar resultant variables from different studies. However, the resultant variables in the studies are based on different scenarios and the input/independent variables vary from study to study. For example, I have a resultant variable E in several studies I am summarizing through the meta-analysis. In one of the studies, I have input variables A and B, in another one B and D, Another one A, B, C, and D, and so on. Which machine learning model will help me understand the relation between these input variables A, B, C, D, and the resultant variable E?
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Dear Farah Naz These articles maybe useful. I am not an expert though
Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016). Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLoS computational biology, 12(7), e1004977. https://doi.org/10.1371/journal.pcbi.1004977
Ellis JL, Alaiz-Moretón H, Navarro-Villa A, et al. Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation. Animals (Basel). 2020;10(4):720. Published 2020 Apr 21. doi:10.3390/ani10040720
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Hello community,
I request help to figure out an issue in my meta analysis.
I am using correlation coefficients r to investigate the relationship between providing (A) and outcome (B). Papers are mostly cross-sectional surveys, so no RCTs.
(A) can be served in group sessions or individual sessions.
Studies usually provide one r for the connection. They either do not specify if it is a group / individual setting or they report an r for a collapsed version (receiving either or both).
Some studies report seperate r. So one r for providing A(group) and one r for providing A(individual). I assune that I am not able to just enter both r's for papers which report (A) group and individual seperatly because then I'll use the same population two times in the meta analysis. Is that correct? I also assume that I cannot just pick and run with one of the r's as it would leave out a substantial amount of the data provided.
Which options do I have here?
  • Can I integrate / collapse both r?
  • Do I have to exclude specific studies and go for 'just individual / just group / just both?
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Nicholas J. Parr - thank you very much for your responses in this thread - they helped me very much in my first attempt at metaanalysis. However I'm having some troubles in the interpretation of the moderation that I have a hope that maybe you could help with.
I have a metanalysis which explores the correlation between X and Y. However Y can be divided into three different categories Y1, Y2, Y3. I tried running the analysis using RVE (robumeta), with dummy coding Y1-Y3:
d1,d2,d3
Y1 = 1, 0, 0
Y2 = 0, 1, 0
Y3 = 0, 0, 1
where d1-d3 are the dummy coded variables. Next I've put it into robumeta using this code:
m <- robu(formula = yi ~ d1 + d2, data = dat,
studynum = Sample_ID, var.eff.size = vi,
rho = .8, small = F)
with yi being z-score transformed correlation effect, and vi is z-score transformed variation using the function bellow
dat_all <- escalc(measure = "ZCOR", ri = correlation, ni = N, data = data_all)
View(dat_all)
I'm getting this kind of an output, which I don't know how to interpret:
Estimate StdErr t-value dfs P(|t|>) 95% CI.L 95% CI.U Sig
1 X.Intercept. 0.270 0.0440 6.15 29 0.00000106 0.1805 0.360 ***
2 d1 0.175 0.0689 2.55 29 0.01648129 0.0345 0.316 **
3 d2 0.102 0.0607 1.68 29 0.10312719 -0.0220 0.226
Does this mean, that there is only partial moderation because there is significance only in the intercept and d1? And is the estimate provided is an effect size estimation? As in z-score for d3 (intercept) is .27, for d1 is .18, d2 is .10?
I' not sure of my intepretation, because data I'm using to understand the code are interpreted as there is a significant moderation.
So my main question is 1) how do I check for moderation (whether the used test is correct and how to interpret it) and 2) whether the subset analysis (diffrent RVE for d1, d2, d3) is the best way to interpret the direction of moderation.
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Hello, I'm trying to do a meta-analysis of 5 papers using the excess number of days a patient group stayed in the hospital. what would be the best measure to use? Thank you!
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Hi! you can take a look on the following article. "inety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75%). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HCAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms, and susceptibilities."
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I would like to present meta analysis data in different charts and/or maps.
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I'd recommend using NumeRe (https://en.numere.org). Can do many things, which the proprietary apps can do, but is freely available via GPLv3. Plotting is supported in 1D, 2D and 3D in different styles (see https://en.numere.org/tutorials/examples/plots for reference). Furthermore, it has a quite comfortable variant of the Levenberg-Marquardt curve fitting algorithm with more-or-less arbitrary function expressions. Is more table oriented than MATLAB, for example, and can read Excel files as well.
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I am conducting a meta analysis for which I need to pool studies with mean and SD. However, one study has reported HOMA-IR as median with Interquartile Range. Is there a way to convert this?
Best regards
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Do you know how to calculate the median from mean ? I have the average of the reaction times and I would like to transform these values into median.
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I am trying to add data for one of the studies into RevMan but it won't allow it. Am I doing something wrong?
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Hazard Ratios are on a log scale, which cannot handle negative values or zeros. What's almost certainly happening is your meta analysis is failing because the 0 is an illogical value.
This also means that there is something wrong (or non-standard) with the source that's giving you HR = 0. Perhaps the authors rounded down an extremely small HR. Or maybe the authors are taking an unorthodox approach and converting their HR to a linear scale by deriving its exponent.
If the authors are indeed using the linear scale for their HR, you could convert it back with a log function, which would give you HR = 1, indicating no effect, just as HR = 0 on a linear scale would (ignoring confidence intervals and p-values). If you use R, you can see this by entering "exp(0)" into the console, which will give you 1, or entering "log(1)", which will give you 0.
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I plan to conduct a three-level meta-analysis using the correlation r following Assink & Wibbelink, 2016 or Cheung, 2014's guidance, but both of the analysis required sampling variance of the observed effect size, which were not provided in almost all included studies. Is it any solution for me to calculate the needed sampling variance or standard error according to statistics reported in the article like SD, beta, t, r....? Thank you so much for your help.
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Ali Zia-Tohidi Your answers are so helpful! I think I know how to do it now, thank you so much!
Best,
Dannuo
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Different steps and procedure with complete example of a meta analysis.
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Hey. I've developed a shiny web-app for conducting a meta-analysis based on R packages meta & metafor: https://www.meta-mar.com.
Regarding your interest, it might be helpful.
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hi every one i have read some meta analysis publication and faced this question that how the author could calculate mean+SD data of 114 case which is 21.23(7.23-47.61) to mean value of 25.35?? is there any formula for that?
thank
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Mean alone dosnt give the scope picture of the value, but with sd, the picture will be very clear. Regards.
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I am looking to do a meta analysis on intervention RCTs but all of the papers have provided baseline and post intervention Mean (SD) for the groups. I have looked at the Cochrane page as I am using Revman ( https://handbook-5-1.cochrane.org/index.htm#chapter_16/16_1_3_2_imputing_standard_deviations_for_changes_from_baseline.htm )
However, I am not from a statistical background so this is getting quite complex. Just wanted to see if there are any resources to guide as this must be very common, and I would hope there are fairly simple ways to deal with it. I have come across using the SD from baseline or post-intervention as the SD for the change but obviously am hesitant of just going along with this.
As only baseline and post-intervention Mean and SD are reported for the majority of the studies, I am thinking I may need to leave it at the narrative synthesis and leave out the meta analysis. But given that the trials are all randomised with similar baseline characteristics and biomarker parameters, could I just enter the final measurements in both groups, rather than the mean change and SD from baseline?
Appreciate of any input.
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Piyush,
Please read the methodology of my paper:
you will see what you can do.
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Different steps and procedure with complete example of a meta analysis.
What is pooled prevalence?
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Dear Mirza Mienur Meher, I too think you need to take a general systematic review and meta-analysis course first. Maybe begin with easier software such as STATA and for more complicated meta-analyses learn R.
To reach the pooled prevalence, you extract the data regarding the prevalence of that condition from the published articles, and then pool them together and perform the meta-analysis to get to the pooled prevalence.
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I am new to meta analyses. Are there any introductory texts that will take me through the entire process by way of an example/s?
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While analyzing meta-analysis of RCTs, can we pool together the effect measures of studies that performed ITT and PPA?
Here. I am estimating the effect measures de novo from the sample size (baseline/endline), mean and s.d. of each study in Stata 17.0. While selecting the sample size, I have the option to either select baseline or endline for all the studies. So, is it right to select endline for all the studies, while a few studies provide mean based on ITT, and vice versa. I did not find it right, epidemiologically?
If so, what are the other ways of doing it?
- Can I estimate effect measures for individual studies based on ITT/PPA and use the pre-computed option to get a pooled estimate?
- Or can I do a sub-group analysis?
Thank you
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As pooling of effect sizes is a completely statistical procedure, it can be done. However, the effect size in PPA is shifted closer to the null value when compared to ITT. Hence, the observed pooled result will also be closer to null value compared to the true pooled effect size. Depending on the efficiency/ practical feasibility of the intervention, this shift might be small or large.
As far as I understand, there are multiple RCT's with baseline, an intervention and an endline , with few studies analysis being Intention to treat method and rest with per protocol.
I would suggest looking at the studies themselves first (Are there differences in the way outcome is measured in the baseline, and the intervention itself), and look at statistical heterogeneity. Then, you can pool all the RCT's and perform a sub-group analysis of ITT (would tell the real world pooled effect size) and PPA(would tell effect size in a controlled setting).
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I have this forest plot using the standardized mean difference, however, the confidence interval lines for each study got inside the study square because they have narrow values relevant to the scale, is there a way to change the scale? Or should I just do a sensitivity analysis to see if the study with the highest SMD would affect the overall estimated SMD?
I did the meta analysis using metacont using random-effect model and Cohen's method and plotted using forest.meta from meta package.
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The problem in the CIs is caused by the practically impossible outlier. As dear Gordon mentioned, an SMD of 15 does not happen. By including such an outlier, the forest function tried to generate a plot with an X-axis from -15 to +15 in a very limited space. So, on the plot, the CI lines will be very narrow.
Of note, the problematic outlier has caused other issues:
Your overall SMD is practically an unweighted average. Why? In the random-effects model, the weight is usually calculated as 1 / (t^2 + vi), and as your t^2 is that large (about 20), the influence of the variance of each effect size (which is usually less than 1) is almost nothing.
Also, such a t^2 is practically impossible. Its square root is about 4.5. We cannot expect such a standard deviation for the effect sizes.
I hope this clarifies the importance of the problematic SMD. I would follow Gordon's recommendation. If I could not find any error in data extraction, and I could not get an answer from the authors, I would exclude this study, and I would write in the manuscript something like "...this study was excluded, as the effect size was practically impossible (SMD = 15), indicating some errors in the report."
Finally, correcting or excluding this SMD will resolve the problem with the CI lines.
Good luck with your work.
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I have a meta-analysis project that needs to answer a question about the efficacy of some intervention. The trick is that intervention can be delivered by different modalities. Because the outcomes are the same eventually, I would need to include them all.
My question is: should I do a Network meta-analysis from the start, and cluster the studies by the modality used? OR Should I do a regular meta-analysis and, then, do a meta-regression to the modality type?
Note: the main objective is to investigate the efficacy of such treatment, BUT the effect of the modality used to deliver that treatment is also important.
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You can do meta-analysis with subgroups for your same intervention with different modalities
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I need to do a sensitivity analysis of recruited studies in a meta-analysis using RevMan 5. Can anyone guide me?
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@arghya pal you can do by trying to check out the tick beside the forest plot one by one. Then, look at the heterogeneity and determine which study make the greatest changes. This method is called leave-one-out method
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I am presently doing SLR and meta analysis/sem for a management/social science topic. We have done SLR but i am not so confident of meta analysis. Can anyone help me or collaborate with me or do it for a fees?
Thanks
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I am doing a meta-analysis and most studies are giving results for the given population as mean and standard deviation. However there are some studies that are giving the mean and standard deviation for subgroups of the population.
For eg- Topic - meta-analysis for blood pressure in metabolic syndrome (MS)
Study 1 mean for MS ; SD for MS; mean for control; SD for control
Study 2 mean for MS ; SD for MS ; mean for control; SD for control
Study 3 mean for diabetics; SD for diabetics ; mean for control; SD for control
mean for obese; SD for obese; mean for control; SD for control
What is the best way to include study 3?
1. carry out a separate meta-analysis to get a common mean and SD and include those values?
2. Include 2 separate data points for study 3?
3. Exclude study 3?
4. Other
Thank you, look forward to your responses.
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Hello,
To combine groups, you can use the formulas provided in the Cochrane handbook, Table 6.5.a (https://training.cochrane.org/handbook/current/chapter-06#section-6-5-2-10).
But, regarding which method is the best, combining the groups is easy. Including both effect sizes in one analysis may not be a bad idea, as the groups are different (there is no overlap between the experimental and control groups), but there is still some correlation among the effect sizes because they are drawn from one study (this is sometimes called hierarchical dependence). You can use a three-level model or use the robust variance estimation (RVE) method.
However, there is another issue that may require attention. You are using SMD as the outcome measure; the SDs you use for this study should be comparable to the SDs used for other studies, otherwise, they will be on different scales and should not be analyzed together.
With this regard, note that the within-subgroups SD is (almost) always smaller than the SD from the total sample, and this difference will be large if the group means are largely different. When the means are different, if the appropriate SD is the within-group SD, then using the combined SD will lead to a smaller SMD (downward bias); if the appropriate SD is the combined SD, then using within-group SDs will lead to a larger SMD (upward bias).
Good luck.
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I understand meta-analysis of RCTs has a level I evidence and case control studies have a level III. But what is the level of evidence from meta-analysis of well-conducted case-control studies; is it I or III or otherwise? Many thanks
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Thanks Suraj Kapoor. I do not see this on the table. If individual case-control studies are level VI, ot would not make sense for systematic review and meta-analysis of a group of these to be level V
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I want to do the meta analysis on concentration of C-reactive protein (CRP). I do have the value of mean and SD concentration in nmol/mg protein. How can I use this value and convert them to nmol/L or mg/L unit?
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Gordon L Warren Thank you very much
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Dear all,
I am conducting a systematic review of the diagnostic studies to identify vascular complications after liver transplantation in pediatrics. Among the findings, there were 4 studies where controls (x2) and comparisons between index/reference tests (x2) were found.
I still find it difficult to extract data from these studies and present it in a word document as a table or something. Is it handy to conduct a meta-analysis and/or standardization of the data somehow?
What is your point of view and suggestions on extracting relevant data + analyzing these?
My understanding of these is very basic.
Thanks,
Bader.
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These resources maybe useful (see screen shots) for meta-analysis
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I have 15 studies, with 9 different median and 6 different mean follow-up periods. I need to present one average follow-up period for the entire meta-analysis.
Am I just literally taking the average of them?
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In this case, before making a decision, I would personally try to understand which follow-up period is the most meaningful from a clinical point of view (1 day? 1 week? 1 year?). Then, I would include in the meta-analysis only relevant studies where the outcome of interest was sampled more or less at that time (and I would exclude the other studies from your quantitative synthesis). Otherwise, you may opt for a sub-group analysis in which you pool together the results of studies with the same follow-up period (for ex.: group 1 with studies where the outcome was sampled within 3 months after intervention, and group 2 with studies where the outcome was sampled after that time period). Never forget that a meta-analysis is not a research exercise, but a statistic tool to be used for a specific purpose, that is creating an evidence summary from multiple trials in order to guide clinical practice or policy making. So, before choosing a methodological strategy, always ask yourself if your results will be meaningful for clinical practice or not.
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In meta analysis & Forest plots, with a heterogeneity of 100% & asymmetric funnel plot, is it ok to carry on with the interpretation (p is 0)?
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I think your question should be more precise.
First of all, what types of meta-analysis such as meta-analyses of intervention studies or DTA review or proportion?
For intervention studies, your heterogeneity value could be problematic and you should stay solely on systematic review not undergoing meta-analysis. Otherwise the pooled outcome could be inconsistant.
Asymmetric funnel plot is subjective assessment. This also results from a few number of included studies.
The last thing is your p-value.
This p-value is belonged to effect size or heterogeneity?
In proportional meta-analysis, the heterogeneity is not a major issue.
Best of luck
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I am in the process of writing a meta analysis related to special educational needs. Being an amateur, I am still reading up on the process and came across writing a protocol for systematic reviews and meta analyses. Would love to understand more about how I should go about writing a protocol, the significance of it, and is PRISMA-P checklist a good place to start? I also found the Countway Protocol Template by Harvard interesting. What would be an ideal place to begin my meta analysis writing?
For starters, I have registered myself on PROSPERO.
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To quote Waugh (BMJ) there are many drawbacks but few advantages:
Firstly, most people will not register their own reviews, but
some may use it to find out what reviews on the same topic are
under way to get theirs published first. Some people may even
draw on ideas in registered reviews. Larger teams with more
resources could overtake smaller teams.
Secondly, people may register reviews but not get round to
doing them, while registration deters others from doing a review
of the same topic.
Thirdly, if the aim of PROSPERO is to reduce duplication, all
the database needs is detail of the topic, not methods. There are
too many mandatory fields.
Fourthly, if the first review registered took precedence, might
that sometimes mean that one from a single reviewer could take
precedence over one from a much better resourced group of
experts?
Fifthly, Siontis and colleagues show that editors publish reviews
despite previous publication of similar ones.So registration of
one review on PROSPERO would not prevent publication of overlapping reviews unless editors were in agreement. An editor
offered a good quality non-registered review that was ready
before the registered one is surely likely to publish it.
Finally, registration cannot be made compulsory for all. It is,
however, compulsory for those doing reviews commissioned
by the UK Health Technology Assessment (HTA) Programme,
which imposes an additional time cost. The HTA programme
and the funders of PROSPERO should examine its cost
effectiveness.
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I’m currently undertaking a systematic review comparing Digital Breast Tomosynthesis with Digital Mammography vs. Digital Mammography alone. There are 6 studies that are all prospective and the same
population undergo both DM+DBT and DM alone, 1 is a randomised controlled trial and the remaining 5 are cohort studies.
they all comment on Cancer detection rate, recall rate, positive predicitive value after recall and false positive predictive value after recall.
there is no mention of specificity.
I would greatly appreciate any approach statistical methods in regards to this.
can this data be put into forest plots?- if so how do I do this?
should risk ratios be adopted? How is this then placed into Stata.
I don’t believe I can produce meta-analysis due to heterogeneity.
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1- Berwanger, Otávio et al. Como avaliar criticamente revisões sistemáticas e metanálises?. Revista Brasileira de Terapia Intensiva [online]. 2007, v. 19, n. 4 [Acessado 8 Maio 2022] , pp. 475-480. Disponível em: <https://doi.org/10.1590/S0103-507X2007000400012>. Epub 22 Jan 2008. ISSN 1982-4335. https://doi.org/10.1590/S0103-507X2007000400012.
2- Legramanti Rodrigues C, Klarmann Ziegelmann P. Metanálise: Um Guia Prático. Clin Biomed Res [Internet]. 2011Jan.11 [cited 2022May8];30(4). Available from: https://www.seer.ufrgs.br/index.php/hcpa/article/view/16571
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Can it be done with just (mean difference ,95% CI of mean difference)?
Can estimated(projected) result be pooled in meta-analysis?
How to get standard error of log(odds ratio)?
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Yes, I think it can be done by using bootstrapping. See this paper "doi:10.1016/j.eja.2010.05.008"
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Dear Community,
I am conducting a meta-analysis, where I identified a significant asymmetry in my funnel plot. After excluding the extreme studies, no asymmetry is observed anymore. So far so good. Now I would like to test for the influence of some moderator variables. What studies do I now include into the analysis? The whole set of studies, for which I initially found the funnel plot asymmetry or the subset without the extreme studies?
I am looking forward to your feedback!
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In general, visual inspection of funnel plots is difficult and can lead to wrong conclusions. For an overview of methods, which can be used to address publication bias see for example the following article: Carter, E. C., Schönbrodt, F. D., Gervais, W. M., & Hilgard, J. (2019). Correcting for Bias in Psychology: A Comparison of Meta-Analytic Methods. Advances in Methods and Practices in Psychological Science, 115–144. https://doi.org/10.1177/2515245919847196
Excluding studies is generally discouraged. A very good justification is needed (e.g., bad quality of data, or the excluded study is based on an unusual sample).
I would recommend using other methods to assess publication bias (see mentioned article) and conduct moderator analyses based on the full data set.
If your are worried, that the quality of some studies is not good, then you could conduct a moderator analysis (study quality as a moderator). Alternatively, one could always make some kind of sensitivity analysis (e.g., does the mean effect size change, when you exclude some studies?).
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Hy everyone, thanks in advance.
I have a meta-analysis of odds ratios and I performed a subgroup analysis of three subgroups (A, B and C).
I get the Q test to compare the differences between A, B and C.
The question is: which test do you use to make pairwise comparisons?
For example:
A vs B
B vs C
A vs (B and C).
Thank you again!
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Detailed description on the subject is attached as pdf
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Dear Colleagues,
We are currently conducting a qualitative meta-analysis and looking for qualitative research that is focused on transgender, non-binary, and gender diverse people’s experiences of gender identity development and how their gender functions to support coping with minority stress as well as increasing resilience and flourishing. We are looking specifically at articles that focus on participants in the United States. We are contacting scholars to ask if they have authored or are aware of studies that may meet our inclusion criteria, particularly any new or unpublished studies. Our team will screen the articles to determine if they meet our inclusion criteria. If you know of any studies that may be relevant to our qualitative meta-analysis, please contact or send them to Kelsey Kehoe at kelsey.kehoe001@umb.edu.
Thank you!
Heidi Levitt & Kelsey Kehoe
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Thank you!
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Dear Experts,
Although I have started to learn some techniques related to meta-analysis article, I am not sure what are the key principles or tips about choosing topics. I humbly invite experts to discuss topics about meta-analysis in social science. What are the principles researchers should know before start writing? What are the tips in choosing topic (how to choose a meaningful topic that can make contribution)? What is the potential of meta-analysis in future scholarship development and theoretical contribution?
Best regards,
Thanks
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Dear Siwei Sun,
Take a look at some auxiliary info:
How do you tell if a paper is a meta-analysis?
Within the Publication Type search box, scroll down until you see Meta-Analysis. High light Meta-Analysis by clicking on it, and then continue scrolling until you see “Systematic Review.” Hold down the “Ctrl” key on a PC, or the "Command" key on a Mac and click “Systematic Review”.
How many studies do you need for a meta-analysis?
two studies
Finally, the authors take up the question "How many studies do you need to do a meta-analysis?" and show that, given the need for a conclusion, the answer is "two studies," because all other synthesis techniques are less transparent and/or are less likely to be valid.
What data do I need for a meta-analysis?
The two summary statistics commonly used for meta-analysis of continuous data are the mean difference (MD) and the standardized mean difference (SMD). Other options are available, such as the ratio of means.
When would researchers choose to conduct a meta-analysis?
If we are working with a fixed-effect model, then it makes sense to perform a meta- analysis as soon as we have two studies, since a summary based on two or more studies yields a more precise estimate of the true effect than either study alone.
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Imagine I have study 1 with results from location A. And study 2 with results from locations B,C and D. I want to compare all of them with a random effect meta-analysis. Is it possible to create 1 table entrance to study 1, and 3 table entrances to study 2?
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You need to account for the dependency between ES that come from the same study (typically with a multilevel meta-analysis): https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/multilevel-ma.html
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Hello all,
We are currently preparing a manuscript including “review and meta-analysis”.
It would be great if you could tell us what journals can accept “review and meta-analysis” manuscript?
Thank you,
Yujiro
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My suggestion would be to choose a journal that is in line with your subject. Journals often choose articles based on the topic rather than the article type.
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I wasn't able to find any articles that would attempt to however partially answer this question* so I will appreciate being linked to any kind of evidence.
*Apart from those cited by Huber & Kuncel, 2016 () which only study the impact on critical thinking
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As a theoretical physicist, I came to the firm belief that philosophy allows me to do better physics. I tried to explain the reasons in the preface of my book A Philosophical Approach to Quantum Field Theory (Cambridge University Press, Cambridge, 2017): "Consciously or unconsciously, the construction of any image of the real world relies on personal beliefs. I hence try to identify and justify my own personal beliefs thoroughly and in various ways. Sometimes I rely on philosophical ideas, for example, about space, time, infinity, or irreversibility; as a theoretical physicist, I have a limited understanding of philosophy, but that should not keep me from trying my best to benefit from philosophical ideas. Often I rely on successful physical theories, principles or methods, such as special relativity, quantum theory, gauge invariance or renormalization. Typically I need to do some heuristic mathematical steps to consolidate the various inputs adopted as my personal beliefs. All these efforts ultimately lead to an image of nature, in the sense of a mathematical representation, but they are not part of this image. The final mathematical representation should convince by its intrinsic logical clarity, mathematical rigor, and natural beauty. Emphasis on the importance of beliefs, even if they are justified by a variety of philosophical and physical ideas, may irritate the physicist. The philosopher, on the other hand, is used to the definition of knowledge as true justified belief. But how can one claim truth for one's justified beliefs? In physics, this happens by confronting an image of nature with the real world. According to Pierre Duhem, known to thermodynamicists from the Gibbs-Duhem relation, and the analytic philosopher Willard Van Orman Quine, only the whole image rather than individual elements or hypotheses should be tested. The confrontation of a fully developed image with the real world depends on all its background assumptions or an even wider web-of-belief, including the assumed logics (confirmation holism). Following Boltzmann's approach of deductive representation, the present book makes an attempt to show how such a testable whole image of fundamental particle physics can be constructed within the framework of quantum field theory. The focus of this book is on conceptual issues, on the clarification of the foundations of quantum field theory, and ultimately even on ontological questions. For our intuitive approach, we choose to go back to the historical origins of quantum field theory. In view of the severe problems that had to be overcome on the way to modern quantum field theory, that may seem to be naive to the experts. However, with the present-day knowledge and with philosophical guidance, the intuitive origins can nicely be developed into a perfectly consistent image of the real world. On the one hand, there is a price to pay for this: practical calculations, in particular perturbation methods, are less elegant and more laborious than in other approaches. Symbolic computation is the modern answer to this challenge. On the other hand, there is a promising reward: a new stochastic simulation methodology for quantum field theory emerges naturally from our approach. Hopefully, the present book motivates physicists to appreciate philosophical ideas. Epistemology and the philosophy of the evolution of science often seem to lag behind science and to describe the developments a posteriori. As philosophical arguments here have a profound influence on the actual shaping of an image of fundamental particles and their interactions, this book should stimulate the curiosity and imagination of physicists. Students of physics can use this book as a reliable companion whenever their standard textbooks focus on pragmatic calculations and fail to clarify important conceptual issues; philosophers and physicists interested in the epistemological foundations of particle physics can use it as a thought-provoking monograph. The benefits of an approach resting on philosophical foundations is twofold: the reader is stimulated to critical thinking and the entire story flows very naturally, thus removing the mysteries from quantum field theory."
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We are planning a pre/post study of an intervention and want to calculate required sample size based on effect size based on a pair sample t-test. A recent meta-analysis of our outcome of interest for this intervention was completed so we have a nice overall effect size (cohen's d). I am familiar with a number of programs to calculate (e.g., G*Power). However, none seem to calculate based on known effect size/cohen's d. Any suggestions on a program/calculator to calculate required sample size for a paired sample t-test when you have an effect size and not the pre/post means/SD or mean difference/SD? We'd like to use the overall effect calculated from the meta-analysis vs just one single study that does provide the pre/post means/SD.
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G*Power allows you to use the effect size directly in your sample size calculation ("Effect size dz" for a paired samples t test in G*Power). In addition, it lets you compute the effect size from the descriptive statistics via the "Determine" button. See the attached screenshot.
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Is it legitimate to transform “fold of change” or “percent change“?in a meta-analysis involving SMD?
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The above may be helpful. Good luck.
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Using PRISMA guideline 2020 is desirable.
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Use Rev-Man or R or Jamovi or BioEstat to do your meta-analyses
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The options for Cohen's d require the confidence limit, standard error, or variance, but one of the studies only provides Cohen's d, nothing else.
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Dear Mark,
Since all you have is Cohen's d from your target articles, your options are clearly limited. Basically, you can do one of two things. The simplest would be to compute the estimated mean of Cohen's d and put confidence intervals around it to the desired level of confidence. The other alternative to to conduct a test of hypothesis about Cohen's d if you have a predetermined value you are looking at.
Good luck,
Jim McLean
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Dear colleagues,
I am currently working with a mentor to conduct a systematic review and meta-analysis for a prevalence and risk of a specific condition in a specific duration. The purpose is to evaluate cross-sectional studies. The problem is that there is plenty of different risk of bias assessments to assess the risk of bias of cross-sectional studies, and I am confused about which assessment is the best for my study. Therefore, can you guide me to solve this problem?
I am looking forward to your help. Thank you in advance.
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Fatimah Albahrani you can use Jonna Briggs Methodology and use their critical appraisal tools for assessing risk of bias of studies. Please see link below:
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I have 5 individual trial datasets with three factors and one continuous variable as an outcome. The details of three-factors are below and I would like to compare the outcome with Dose and Lesion.
  • Control and Treated group(Unt/Trt)
  • Doses of the Treatment above(0/0.6/1)
  • Lesion Severity(Untreated/Acute/Chronic)
The problem of this case is each trial doesn't contain all cases. For example, trial 1 only contains Untreated lesion data. Trial 2 only contains Acute lesion data.... so on.
Please help me.
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Dahee Kim Sorry for the delayed response. If you want to take such an approach, the first thing is to calculate the effect size for your units of analysis, i.e., the Hedges' g for each of these nine categories in each study. Then, if you have enough effect sizes, you can meta-analytically combine these effect sizes, to obtain overall SMDs for each category (9 pooled SMDs), which then may be compared to one another.
However, based on what you mentioned that each study has reported data on some of these categories, then the evidence you will gather is based on between-study differences (i.e., the difference between SMD for Category 1 from one study and Category 2 from another study). Such evidence is not strong, as the studies are not usually similar, and it is difficult to assume that the only difference between these studies only related to the differences in the categories they assessed. Of note, these limitations cannot be addressed statistically, as they are inherent in the data you have with respect to the questions you want to answer. (Before you put much effort to analyze and interpret the data, you may need to consult a methodologist to see whether your data is suitable for your research question.)
There is another issue here. I am not sure why you prefer the SMD over the raw mean difference. If the outcome is measured by the same tool, analyzing the raw scores may be better (note that with estimating the SMD, you are adding the errors that are associated with the estimates of standard deviations to your effect size estimates.)
However, if the outcome is measured using the same tool, another possibly simpler way to analyze your data is you use a linear mixed effect model and analyze the individual data (which I assumed is available, as you mentioned that you have the dataset from these studies) while accounting for the hierarchical data structure (i.e., that the individual-level data are nested within 5 studies).
Again, given the complicated situation with several problematic issues (some of which I have not mentioned above such as the dependency of the effect sizes that are drawn from one study), it is probably better to discuss your situation with a methodologist in detail.
Bests.
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We want to conduct meta-analysis on papers that report a % change in vaccination coverages. But I am not sure if at all a metanalysis is possible using % change.
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If you mean relative % change eg increase from 50% coverage to 60% coverage is 10/50 = 20% increase then you could just calculate the mean of these % increases to get an overall effect measure.
If you mean absolute differences in % vaccinated in populations, eg in above example difference in % vaccinated = +10% ,to get an overall effect measure you would need population sizes .
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what problem suitable to conducct meta analysis? what conditions should meet to conduct meta analysis?
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Yes, It recommended but you have to aware and make stronger on the methods. it need attention more detail.
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Hello everyone,
We perfomed a multilevel meta-analysis in R. But now, we want to analyse a moderation analysis through meta-regression approach. How to conducted this analysis taking account that our meta-analysis is a multilevel?
Thank you.
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I assume you most likely used metafor for this and its included rma.mv function.
You can add fixed covariates using the mods argument to rma.mv - this is completely separate to the multi-level error structure. For example, using one of the example datasets built into metafor:
library(metafor)
##multilevel model without covariates
model1 <- rma.mv(
yi,
vi,
random = ~ 1 | district,
data = dat.konstantopoulos2011
)
##add fictitious covariate
data <- dat.konstantopoulos2011
data$mod <- rep(c("A", "B"), times = 28)
##model with covariate
model2 <- rma.mv(
yi,
vi,
mods = ~ mod,
random = ~ 1 | district,
data = data
)
You will see then that the output for model 2 will contain the regression coefficient for the moderator, its standard error and corresponding p-value.
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For the three-level meta-analysis, the package "dmetar" provides a function that calculates the I^2 (dmetar::var.comp()). But, this function only works for three-level models. Is there any package or function that allows for calculating I^2 for, let's say, a five-level model?
Also, for such models with one or more moderators, are there any tools for calculating the R^2 for the effect of the moderator(s)?
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You cannot really estimate a true R^2 for a multilevel meta-analysis. You can calculate a pseudo R^2 for fixed covariates in mixed models, but I would generally avoid inferring much from it. If you absolutely need R^2, you can calculate a very crude version based on the difference between models with/without the fixed covariate like so:
##using fictitious variables
##model with no predictors
model1 <- rma.mv(yi, vi, random = ~ 1 | group, data = data)
##model with predictors
model2 <- rma.mv(yi, vi, mods = ~ moderator, random = ~1 | group, data = data)
##calculate pseudo R2
var.model1 <- sum(model1$sigma2)
var.model2 <- sum(model2$sigma2)
r2 <- var.model1 - var.model2 / var.model1
It is possible to estimate I2 as described. I would personally suggest interpreting the raw variance for each level of the factor (sigma2) and considering how much of the total variance (sum of sigma2) is accounted for by each level. To me, this makes more sense than interpreting the proportion of I2 accounted for by each level, given that I2 is so frequently misinterpreted.
I'm not sure if there is any tool that estimates I2 at each level for you, but here is how to do it yourself in R - I assume you are using R:
##First create your model using the rma.mv function. Call the model "m"
##Estimate your total sampling variance:
a <- (m$k.eff - 1) * sum(1/diag(m$V))
b <- sum(1/(diag(m$V)))^2 - sum(1/(diag(m$V))^2)
sample.var <- a/b
##Estimate the total variance
total.var <- sum(m$sigma2) + sample.var
##Extract each variance component
prop.level5 <- m$sigma2[4]
prop.level4 <- m$sigma2[3]
prop.level3 <- m$sigma2[2]
prop.level2 <- m$sigma2[1]
##calculate each level of I2. Recall that I2 is the proportion of total variability attributable to sampling variance. Therefore:
i2.level1 <- (sample.var/total.var)*100
i2.level2 <- (prop.level2/total.var)*100
i2.level3 <- (prop.level3/total.var)*100
i2.level4 <- (prop.level4/total.var)*100
i2.level5 <- (prop.level5/total.var)*100
i2.total <- i2.level1 + i2.level2 + i2.level3 + i2.level4 + i2.level5
You can use the same method for as many levels as you wish. I2 for the overall model will always be estimated in the same manner (sampling variance/total variance). The variance component for each level of random effects will always be stored as a vector in the $sigma2 list item for the rma.mv model.
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Dear all,
I would like to update the yield strength value in a UMAT subroutine in Abaqus. This would happen when a load is applied on an element, the deformation strain would be queried from a meta-model database, and depending on the deformation strain combinations returned by abaqus, the yield strength of the material in the function PROPS() would be updated from the database. I am very new to writing subroutines in ABAQUS. I was wondering if there is a way to implement such an algorithm to update the material properties in each time step. If so, how can I start tackling this issue?
Any help is greatly appreciated. Thanks.
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¿Can a meta-analysis include data from the same study twice, that is, in the same forest plot? I have seen this in a recently published investigation that included data with two different cutoff points, but I think it induces bias and affects heterogeneity. Is there a bibliography on the matter?
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Hi Diana. What are the objectives of the meta-analysis study?
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I have to design a bioinformatics study to perform meta analysis of mirna. How should I approach it, what databases should I use and if I need any biocomputation approaches.
Any help is appreciated.
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I agree with Mohammad Sholeh .
There are different databases for different diseases. so you can use multiple gene expression datasets of that particular disease on which you are working and make a report of the expression pattern and clinical associations, and you may call it a meta-analysis, or you can also do a literature-based meta-analysis. Depends.
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I am doing a meta-analysis to assess the effect of serious games on memory. Some included studies use more than one measure to assess the memory. All measures are relevant to the paper. My question is which measures to use? Is it possible to run a meta-analysis including all measures? if so, how? I am using RevMan to conduct meta-analyses, can I use it to sort out this issue?
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Multiple outcome measurements can not be combined in a single forest plot because it will duplicate the participants and create a unit-of-analysis issue. The best approach would be to create a hierarchical list of measures that you have and extract the highest measure on that list from each study. Different measures can be combined using standardized mean difference (if continuous data).
Another approach would be to use a multilevel meta-analysis to account for correlation between the effect sizes. In that case, you can use all measures from each study to combine into one analysis but that is more complex and RevMan can not run that. STATA 17 or R can be used for that instead.
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Hello there!
I am performing a systematic review regarding the effect of a specific surgical procedure on a common orthopaedic condition. All identified studies are case-series with no control group.
For these studies the outcome is pre- vs post-surgery change in Visual Analogue Scale (VAS).
Is it possible to perform a meta-analysis of pre- vs post-surgery change in VAS given there is no control group?
Any thoughts would be greatly appreciated!
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In my practice, I’ve been able to perform meta-analysis of pre/post op PROM scores in two manners:
1. Performing a pairwise meta-analysis of the continuous data, taking the preop score as one “arm” and the postop scores as the other “arm”.
2. Using the formulae for subtracting means and standard deviations listed in the cochrane handbook to caluclate the difference in pre/postop scores and performing a single-arm meta-analysis of the change value.
Method 1 would be preferrable if you are unable to access software such as RStudio or STATA, as it can be done on Revman.
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Hi everyone,
I calculated a meta-analysis in STATA 16 using the DerSimonian-Laird random effects model (single-arm meta-analysis). In my results summary and forest plot, I noticed that I get a significant p-value every time the CI crosses 1, but it is non-significant when the p-value does not cross 1. That seems to be reversed to what we would expect, right?
Does anyone have any input on why this might be? The STATA manuals and examples they give are all reporting it the correct way, so I am not sure why in my analysis it gets reversed.
An input would be appreciated.
Thank you very much!
best,
Sarah
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I think you defined the effect size incorrectly
Use the command as follows
meta esize n11 n12 n21 n22, esize(lnoratio)
I also recommend posting questions related to stata in statalist.org
There you can share your code
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I have made a selection of studies that are focused on a specific research question in a meta-analysis for a master thesis. However, it is my first meta-analysis, and it is unclear to me how to do data abstraction, test homogeneity of the studies, and test publication bias. I am using the software RevMen 5 and SPSS statistics. What are the steps to proceed after the selection of the studies? Thank you in advance.
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Writing a protocol is as previously mentioned a key step of the systematic review preparation before starting it. You could define with your team the inclusion and exclusion criteria (based on the question you are trying to answer, the protocol may differ), the number of person involved in each step of the inclusion/exclusion criteria as well as for data extraction. This would also be very helpful to a priori state on the data you want to extract from the studies, the type of ROB tool you need to use as well as in specifying the analyses that are planned.
The Cochrane collaboration site is a very good ressource as indicated. It can be complemented and adapted to your specific research area if you also look for what has been previously reported on your specific field.
Statistical analysis should also be planed a priori (with all possible covariates that may impact the results) avoiding analyses done after seeing the data (associated with a high risk of false discovery rate).
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Meta-analysis is a good method to estimate the average correlation between two variables based on previous findings. When examining the associations between two variables, we also want to figure out the direction of the relationship (or in other words, who influences who). Is it possible for us to infer the causal direction by examining the moderating effect of the measurement order of two variables in a meta-analysis (i.e., comparing the correlations measured between two variables when one was measured before or after the other, or when they were measured concurrently)? The cross-lagged panel model states A to be the likely cause of B when the correlation between A measured at an earlier point and B measured at a later point is larger than the correlation between B measured at an earlier point and A measured at a later point (Kearney, 2017). Following this logic, can we say that, in a meta-analysis, if the correlation was more negative when A was measured after B than before or concurrently, A may be the cause of B and play a dominant role in the relation? If yes, are there some published examples? Thank you very much!
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No, cause-and-effect conclusions cannot be made from subgroup analyses or meta-regression analyses of potential moderator variables. These analyses are done across studies, not within studies where cause-and-effect conclusions can be made (e.g., treated group vs. untreated group).
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Chi-square test, Tau or I-squared? I guess I shouldn't need to use them all, but for meta-analysis, with relatively few studies (less than five per set) which would be most important?
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The best way to interpret heterogeneity in meta-analysis is to compare tau squared to its empiric distribution. The distribution of tau squared for all Cochrane meta-analyses has been published for continuous outcomes by Rhodes et al. (https://www.jclinepi.com/article/S0895-4356(14)00348-5/fulltext) and for binary outcomes by Turner et al. ( ). You can look at the distribution for the relevant outcome to your analysis, based on the type of outcome. The authors further categorised their results by the exact effect measure, as less objective effect measures will be expected to have greater inconsistency. Compare your distribution for tau squared to the distributions here and decide whether that seems high or not high by comparison. For example, you may decide that heterogeneity is high if it is in the fourth quartile by comparison.
Contrary to popular belief, I squared does not quantify heterogeneity and can be particularly misleading when there is a small number of studies as you have here. Tau2 represents the absolute between-study heterogeneity and is a better/truer index of heterogeneity.
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Hello!
I would really appreciate some wider knowledge on my issue.
I am running a meta-analysis to look at the risk ratios associated with the risks of incidence/mortality of breast or ovarian cancers in BRCA2+ and BRCA1/2+ carriers. Due to how I must split the data into groups (BRCA2+ and OC risk for example), I get lots of small groups.
My issue is, some of the sets only contain 2/3 studies. I tried random effects using Revman but because I have a low number of studies I am unsure whether it can be accurate. When I used fixed effects I couldn't produce the charts the same way which was what initially prompted me to use random effects.
Really at a loss just now as to whether I can keep this as random effects but explain the different weights of my results...how would you approach this?
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Hello!
It seems that you are familiar with the concepts of the fixed- and random-effects models. Then, you probably have considered the random-effects model to be more appropriate, because having one true effect size is rarely a logical assumption. However, in your situation, it seems that the random-effects model may not be appropriate. With three effect sizes, your estimate of the true variance (tau-squared) will have very poor precision. But can you use the fixed-effect model instead? Yes. However, you should interpret the results as descriptive statistics (i.e., the weighted average of the effect sizes you have). Although you are willing to estimate the population effect size (in the random-effects model, the distribution of the population's true effect sizes), your data do not allow for such an inference.
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Dear researchers,
I am conducting a meta-analysis using a random-effects model in which some studies have a single effect size, while other studies have 2 or more effect sizes (2 or more subgroups). For example, study A has OR for overweight and obesity combined, while study B has separate results for overweight and obesity. These are my studies (fictional):
A overweight+obesity OR=1.4
B overweight OR=1.1
B obesity OR=1.8
When conducting a meta-analysis on such studies it is advisable to combine B overweight and B obesity using a fixed-effects model (since they have the same control group) and then to perform a meta-analysis using a random-effects model (i.e. conducting meta-analysis using the study as a unit of analysis). Can anyone please tell me how to perform that in R?
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The conventional models used for dose-response meta-analyses only consider the variation for the response variable. I am trying to find a model for dose-response meta-analysis (both the dose and the response variables are continuous) which also takes the variation (SD) in the dose (exposure) variable and possible confounding variables (for adjustment in the model) into account. I will be thankful if you also guide me about the statistical package that I should use for such an analysis.
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Dear
Michael Stein
,
Not yet. In fact, the DRMETA package in STATA lets you work with continuous response variables. However, I need a model which lats variation for the dose variable. None of them worked for me.
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For publication bias study in a meta-analysis by funnel plot test, there should be at least 10 studies, fewer studies might not give sufficient power to the test and may not detect real asymmetry. Is this statement correct?
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You can use the Doi plot to check for publication bias if there are less than 10 studies. It has greater power and sensitivity than the funnel plot. It is implemented in MetaXL.
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Hello everyone
Can you share your experience with me about the literature review? How do you record the studies? How do you create a research matrix and classification the studies? What tools do you use for this purpose? , etc.
Regards
Vahid
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Dear,
You can define the research keywords, then using google scholar you look for articles that combine these keywords and extract the citation.
In writing the literature review, the categorization of similar articles is recommended based on methods, time, spatial, etc.
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I’ve managed to plot multiple sub-group analyses in one forest plot on RevMan. However, I would like one of the subgroups to be analysed with a fixed effects model due to low heterogeneity, yet it is not possible to use different effect models for different subgroups in RevMan.
is there any way to do this (i.e. to plot subgroups on one combined forest plot with fixed and random effects) in RevMan or R?
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Interpretation of statistical heterogeneity should never be a reason for choosing between fixed-effect and random-effects models. Choosing between the two models should be based on the reviewers' understanding/justified assumptions on how differences in effect estimates between included studies occur. Besides, the lower the statistical heterogeneity, the more similar the effect size and dispersion estimates are from the two models. A tau-squared of 0, in fact, leads to the fixed-effect and random-effects models giving the exact same estimates from the same set of data.
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I'm working on a meta-analysis and I'm having trouble deciding whether to use a random effect or a fixed effect model based on significant heterogeneity between studies. What p-value should I use to declare significant heterogeneity?
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The decision to use random-effects or fixed-effect model SHOULD NOT be based on the detection of statistically significant heterogeneity when the collected data from included studies are combined. Doing so is a wrongly "post hoc" approach or, in a way, creates a problematic "circular justification" devoid of context that should stem from the reviewers' understanding of the phenomemon/outcome/intervention that they are studying. Choosing one model over the other should be based on how you think, beforehand, the potential differences in the effect estimates between studies exist. While the result from both models will exactly be the same when the tau-squared is 0, the use of fixed-effect model in the presence of substantial heterogeneity is possible if this can be justified sufficiently (which I think would be difficult). Please read: https://www.meta-analysis.com/downloads/Meta-analysis%20Fixed-effect%20vs%20Random-effects%20models.pdf.
For introductory purposes, statistically significant heterogeneity is considered when the the Chi-squared test for heterogeneity p-value is </= .1. Utilizing the I^2 formula, the rough interpretation of "substantial" heterogeneity is used when I^2 >/= 50%. Again, do not use these thresholds to decide on the model to use.
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We intend to write a review paper with data from various studies around the world, but the data collection in the various studies was done using different methods and intensities. So, in this case, how should the data set be standardized in order to run some analysis?
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I'm not sure if this can be done even when the raw data for all included studies is available to you; the impact of sampling type/effort in the collected data may be hard to disentangle even with the use of statistical assumptions that are unlikely to have been validated previously.
If you really think that sampling type and sampling effort are really important factors to consider in the phenomenon you are interested in, you may consider separating the pooled analyses per these factors. You may also opt to not do quantitative analyses and focus on qualitative synthesis.
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Dear community,
I am wondering how to synthesize different types of experimental findings in a meta-analysis. In particular, if you are interested in the effect-strength of a specific treatment across multiple studies how do you code studies which have a split sample or multiple interaction effects? For example, one study shows that the treatment is significant for a group of experts, but not significant for a group of laypeople. Intuitively, I would have included both effect sizes separately, because I need to aggregate the effect of the treatment across all experiments. Is there any literature on this problem?
Many thanks!
Ekaterina
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I have been part of a study where we ran into that problem, specifically with different subgroups used (e.g. IVF naïve, vs non naive). What we did was to include them separately (as a and b), weighted by sample size of course. The trick is to make sure there is no overlap between the groups.
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So far I have found - 6
1998 - Blazevich & Jenkins
2002 - Blazevich & Jenkins
2011 - KLIMENTINI et al
2012 - Natalia Romero-Franco et al
2013 - Fernandez et al
2013 - Kamandulis et al
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Interesting
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Many articles have given me pre and post Mean and SD, however, they have not given me the mean change or mean SD change. From my understanding, it is the latter that I need to undertake a MA. With only means and SD from pre and post how can I calculate the mean SD. Many thanks in advance
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Hi all, currently doing a systematic review and unfortunately a paper I'm extracting data from has provided median and SD but not the mean. They have not provided the range of values or IQR - is there any way to calculate the mean from just the median and SD?
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In addition to what Debopam Ghosh said (which, unfortunately, is unlikely to be the case as normality tests are seldom reported or even performed), try to get in touch with the corresponding author for the values you are looking for or for the raw data so you can compute them yourself.
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For example, let say someone has registered a meta-analysis in PROSPERO back in 2018 but the study is not completed even in 2022. So, in such case can a different person conduct the same meta-analysis on his own? What do the research ethics says to this?
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Could you please provide any tips or ideas you have for creating the finest meta analysis review on cancer? Rather than writing a review article, I'd like to begin working on a meta analysis.
regards
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I totally agree with Timo Van Canegem that the word by itself "cancer" is such a broad term and I would narrow down to a particular sub-type and/or treatment. My best suggestion for this would be if you are uncertain as to which type to focus on to look at what type or types are treated or investigated in your organisation. For example, where I work currently, they are particularly interested in lung cancers and where I was working before has a special focus on bowel and breast cancers.
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I'm currently working on a systematic review including more than 150 studies. However, a large amount of experimental studies haven't reported sufficiently means and SDs for the outcome measures. Is there any alternative way to include them in the meta-analysis?
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I am performing a systematic review of prevalence studies. I have pooled prevalence rates using the program MetaXL (double arcsine transformation, random effects model) which gives me a point estimate and confidence intervals for each subgroup. I am trying to figure out how to perform a statistical test for a difference between subgroups.
The problem I am trying to solve is similar to what is presented in this paper:
Subgroup 1 Prevalence: 18.58% [95%CI:12.26-25.8] (pooled 11 studies, n=699)
Subgroup 2 Prevalence: 20.18% [95%CI: 16.64-23.96] (pooled 37 studies, n=6453)
Conclusion from paper is that the difference between subgroups is non-significant (p=0.71), however I can't figure out how they calculated the p-value.
I would also like to be able to perform a test for subgroup differences where there are more than 2 subgroups. For this study, when evaluating more than 2 subgroups, they did not report a p-value.
I have tried creating a 2x2 table and performing a chi-square test but the p-value I get (0.32) is very different.
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The paper describes use of a double arcsin transformation . I'm not sure how they did significance test.
Using a t test would give SE of difference:
sqrt ( 0.0345 x 0.0345 + 0.0187 x 0.0187) = sqrt 0.00154 = 0.0392
and t46 = (0,2018 - 0.1858) / 0.0392 = 0.016/0.0392 = 0.408 ;P about 0.68 , which is pretty close to 0.71.
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I am conducting a systematic review and meta-analysis. However, my outcome of interest (a specific procedural complication) tends to be reported in absolute numbers and is not analyzed using any statistical methods (e.g. odds ratio between groups, etc.) even though the absolute numbers of events and participants in each group is clear. However, some studies do actually calculate a RR or OR as well as provide absolute numbers of patients. I am just unclear how I should handle these studies (i.e. should i include or exclude them). I really appreciate everyone's help.
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What software do you use automated meta-analysis? I am interested in AI-based software but other suggestions will be welcomed. Thank you.
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How to proceed with Sensitivity Analysis? Can it be done on RevMan5 or we need to export files to some other software?
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You need to select the studies to be included in the sensitivity analysis e.g. If you have to exclude low quality studies from the total number of studies included earlier in analysis. It depends on the purpose of sensitivity analysis, else it is the same method by just including/excluding the eligible studies for that particular outcome
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How to convert a mean and SD reported in minutes, to a mean and SD in seconds?
One study reported a mean and SD of a procedure in minutes, I want these results in seconds, I can simply convert them by multiplying them by 60? or there is a different way?
Note: This data will be used for a meta-analysis
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Dear all,
I am kindly asking you for help. My systematic search has resulted in three trials and two of them measured the outcomes in 24 weeks and one in 12 weeks. Is it appropriate to combine these three under the one meta-analysis despite the difference in duration of trial?
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