Variability in animal growth from one population to another is of keen interest to population ecologists wishing to understand the inherent within species variability and explore meaningful environmental covariates. Yet most studies investigating growth bayesian population analysis using winbugs a hierarchical perspective pdf animals within a population are usually analyzed in isolation from, or at best, compared qualitatively across populations.
Growth in length at age is modeled using a nonlinear mixed effect model and we used Bayesian hierarchical meta-analysis as a natural approach to estimate parameters, investigate growth variability among populations and to elucidate meaningful biological covariates for this species. Finally, growth parameters were negatively correlated with latitude suggesting that population productivity most likely declines the higher in latitude a population is found for this species. Check if you have access through your login credentials or your institution. The basic tenet behind meta-analyses is that there is a common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies.
In addition to providing an estimate of the unknown common truth, meta-analysis has the capacity to contrast results from different studies and identify patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies. For instance, a meta-analysis may be conducted on several clinical trials of a medical treatment, in an effort to obtain a better understanding of how well the treatment works. Although this led to him being widely recognized as the modern founder of the method, the methodology behind what he termed “meta-analysis” predates his work by several decades. A meta-analysis is a statistical overview of the results from one or more systematic reviews. The precision and accuracy of estimates can be improved as more data is used.
This, in turn, may increase the statistical power to detect an effect. Inconsistency of results across studies can be quantified and analyzed. A meta-analysis of several small studies does not predict the results of a single large study. This would mean that only methodologically sound studies should be included in a meta-analysis, a practice called ‘best evidence synthesis’. Other meta-analysts would include weaker studies, and add a study-level predictor variable that reflects the methodological quality of the studies to examine the effect of study quality on the effect size. However, others have argued that a better approach is to preserve information about the variance in the study sample, casting as wide a net as possible, and that methodological selection criteria introduce unwanted subjectivity, defeating the purpose of the approach. A funnel plot expected without the file drawer problem.
A funnel plot expected with the file drawer problem. For example, pharmaceutical companies have been known to hide negative studies and researchers may have overlooked unpublished studies such as dissertation studies or conference abstracts that did not reach publication. This is not easily solved, as one cannot know how many studies have gone unreported. This should be seriously considered when interpreting the outcomes of a meta-analysis. In contrast, when there is no publication bias, the effect of the smaller studies has no reason to be skewed to one side and so a symmetric funnel plot results. This also means that if no publication bias is present, there would be no relationship between standard error and effect size. Apart from the visual funnel plot, statistical methods for detecting publication bias have also been proposed.
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These are controversial because they typically have low power for detection of bias, but also may make false positives under some circumstances. However, small study effects may be just as problematic for the interpretation of meta-analyses, and the imperative is on meta-analytic authors to investigate potential sources of bias. A Tandem Method for analyzing publication bias has been suggested for cutting down false positive error problems. This Tandem method consists of three stages. Firstly, one calculates Orwin’s fail-safe N, to check how many studies should be added in order to reduce the test statistic to a trivial size. If this number of studies is larger than the number of studies used in the meta-analysis, it is a sign that there is no publication bias, as in that case, one needs a lot of studies to reduce the effect size. Secondly, one can do an Egger’s regression test, which tests whether the funnel plot is symmetrical.
As mentioned before: a symmetrical funnel plot is a sign that there is no publication bias, as the effect size and sample size are not dependent. Thirdly, one can do the trim-and-fill method, which imputes data if the funnel plot is asymmetrical. However, low power of existing tests and problems with the visual appearance of the funnel plot remain an issue, and estimates of publication bias may remain lower than what truly exists. Most discussions of publication bias focus on journal practices favoring publication of statistically significant findings. However, questionable research practices, such as reworking statistical models until significance is achieved, may also favor statistically significant findings in support of researchers’ hypotheses. It is not uncommon that studies do not report the effects when they do not reach statistical significance.