Biology is the scientific study of life and living organisms. It is a broad natural science that encompasses a wide range of fields and unifying principles that explain the structure, function, growth, origin, evolution, and distribution of life. Central to biology are five fundamental themes: the cell as the basic unit of life, genes and heredity as the basis of inheritance, evolution as the driver of biological diversity, energy transformation for sustaining life processes, and homeostasis, the maintenance of internal stability.
From Greekβίος (bíos) 'life', (from Proto-Indo-European root *gwei-, to live) and λογία (logia) 'study of'. The compound appears in the title of Volume 3 of Michael Christoph Hanow's Philosophiae naturalis sive physicae dogmaticae: Geologia, biologia, phytologia generalis et dendrologia, published in 1766. The term biology in its modern sense appears to have been introduced independently by Thomas Beddoes (in 1799), Karl Friedrich Burdach (in 1800), Gottfried Reinhold Treviranus (Biologie oder Philosophie der lebenden Natur, 1802) and Jean-Baptiste Lamarck (Hydrogéologie, 1802).
The earliest of roots of science, which included medicine, can be traced to ancient Egypt and Mesopotamia in around 3000 to 1200 BCE. Their contributions shaped ancient Greek natural philosophy. Ancient Greek philosophers such as Aristotle (384–322 BCE) contributed extensively to the development of biological knowledge. He explored biological causation and the diversity of life. His successor, Theophrastus, began the scientific study of plants. Scholars of the medieval Islamic world who wrote on biology included al-Jahiz (781–869), Al-Dīnawarī (828–896), who wrote on botany.
Biology began to develop quickly with Anton van Leeuwenhoek's dramatic improvement of the microscope. It was then that scholars discovered spermatozoa, bacteria, infusoria and the diversity of microscopic life. Investigations by Jan Swammerdam led to new interest in entomology and helped to develop techniques of microscopic dissection and staining. Advances in microscopy had a profound impact on biological thinking. In
the early 19th century, biologists pointed to the central importance of
the cell. In 1838, Schleiden and Schwann
began promoting the now universal ideas that (1) the basic unit of
organisms is the cell and (2) that individual cells have all the
characteristics of life, although they opposed the idea that (3) all
cells come from the division of other cells, continuing to support spontaneous generation. However, Robert Remak and Rudolf Virchow were able to reify the third tenet, and by the 1860s most biologists accepted all three tenets which consolidated into cell theory.
Meanwhile, taxonomy and classification became the focus of natural historians. Carl Linnaeus published a basic taxonomy for the natural world in 1735, and in the 1750s introduced scientific names for all his species. Georges-Louis Leclerc, Comte de Buffon, treated species as artificial categories and living forms as malleable—even suggesting the possibility of common descent.
Serious evolutionary thinking originated with the works of Jean-Baptiste Lamarck, who presented a coherent theory of evolution. The British naturalistCharles Darwin, combining the biogeographical approach of Humboldt, the uniformitarian geology of Lyell, Malthus's
writings on population growth, and his own morphological expertise and
extensive natural observations, forged a more successful evolutionary
theory based on natural selection; similar reasoning and evidence led Alfred Russel Wallace to independently reach the same conclusions.
In 1953, the Miller–Urey experiment showed that organic compounds could be synthesized abiotically within a closed system mimicking the conditions of early Earth, thus suggesting that complex organic molecules could have arisen spontaneously in early Earth in the process of abiogenesis.
Cell biology is the branch of biology that studies the structure, function, and behaviour of cells.
Bioenergetics is a field in biochemistry and cell biology that concerns energy flow through living systems. This is an active area of biological research that includes the study of the transformation of energy in living organisms and the study of thousands of different cellular processes such as cellular respiration and other metabolic and enzymatic processes that enable the use of energy.
Evolutionary developmental biology compares the developmental processes of different organisms to infer how developmental processes evolved. The field grew from 19th-century beginnings, where embryology faced a mystery: zoologists did not know how embryonic development was controlled at the molecular level. Charles Darwin noted that having similar embryos implied common ancestry, but little progress was made until the 1970s. Then, recombinant DNA technology at last brought embryology together with molecular genetics. A key early discovery was that of homeotic genes that regulate development in a wide range of eukaryotes. The field explores deep homology, the finding that dissimilar organs such as the eyes of insects, vertebrates and cephalopod molluscs, long thought to have evolved separately, are controlled by similar genes from the evo-devo gene toolkit.[45]
Systematics
is the study of the diversification of living forms, both past and
present, and the relationships among living things through time.
Relationships are visualized as evolutionary trees, studied using phylogenetics, and creating a unified taxonomy of life.
Conservation biology is the study of the conservation of Earth's biodiversity with the aim of protecting species, their habitats, and ecosystems from excessive rates of extinction and the erosion of biotic interactions. It is concerned with factors that influence the maintenance, loss, and
restoration of biodiversity and the science of sustaining evolutionary
processes that engender genetic, population, species, and ecosystem diversity. The concern stems from estimates suggesting that up to 50% of all
species on the planet will disappear within the next 50 years, which has contributed to poverty, starvation, and will reset the course of evolution on this planet. Conservation biologists research the trends of biodiversity loss, species extinctions, and the negative effect these are having on our capabilities to sustain the well-being of human society.
Meta-analysis
is a method of synthesis of quantitative data from multiple independent
studies addressing a common research question. An important part of
this method involves computing a combined effect size across all of the
studies. As such, this statistical approach involves extracting effect sizes
and variance measures from various studies. By combining these effect
sizes the statistical power is improved and can resolve uncertainties or
discrepancies found in individual studies. Meta-analyses are integral
in supporting research grant proposals, shaping treatment guidelines,
and influencing health policies. They are also pivotal in summarizing
existing research to guide future studies, thereby cementing their role
as a fundamental methodology in metascience. Meta-analyses are often, but not always, important components of a systematic review.
History
The term "meta-analysis" was coined in 1976 by the statistician Gene V. Glass, who, in an article in Educational Researcher that year, stated: "meta-analysis refers to the analysis of analyses". Glass's work aimed at describing aggregated measures of relationships and effects. While Glass is credited with authoring the first modern meta-analysis, a 1904 paper by the statistician Karl Pearson published in the British Medical Journal collated data from several studies of typhoid inoculation; it is seen
as the first time a meta-analytic approach was used to aggregate the
outcomes of multiple clinical studies.Numerous other examples of early meta-analyses can be found, including in occupational aptitude testing and agriculture.
The first model meta-analysis was published in 1978 on the effectiveness of psychotherapy by Mary Lee Smith and Glass. After publication of their article, there was pushback on the
usefulness and validity of meta-analysis as a tool for evidence
synthesis. The first example of this was by Hans Eysenck, who, in a 1978 article in response to the work done by Smith and Glass, called meta-analysis an "exercise in mega-silliness". Later, Eysenck would refer to meta-analysis as "statistical alchemy". Despite these criticisms, the use of meta-analysis has only grown since
its introduction. By 1991, there were 334 published meta-analyses, had grown to 9,135 by 2014.
The field of meta-analysis expanded greatly after the 1970s
and touches multiple disciplines; including psychology, medicine, and
ecology. Further, the more recent creation of evidence synthesis communities has increased cross-pollination of ideas and methods and the creation of software tools across disciplines.
Literature search
One of the most important steps of a meta-analysis is data
collection. For an efficient database search, appropriate keywords and
search limits need to be identified. The use of Boolean operators and search limits can assist the literature search. A number of databases are available (e.g., PubMed, Embase, PsycINFO), but it is up to the researcher to choose the most appropriate sources for their research area. Indeed, many scientists use duplicate search terms within two or more databases to cover multiple sources. The reference lists of eligible studies can also be searched for eligible studies (i.e., snowballing). The initial search may return a large volume of studies. Quite often, the abstract or the title of the manuscript reveals that
the study is not eligible for inclusion, based on the pre-specified
criteria. These studies can be discarded. However, if the study appears eligible
(or even if there is some doubt), the full paper can be retained for
closer inspection. The reference lists of eligible articles can also be
searched for any relevant articles. These search results need to be detailed in a PRIMSA flow diagram, which details the flow of information through all stages of the review.
Thus, it is important to note how many studies were returned after
using the specified search terms, how many were discarded, and why. The search terms and strategy should be specific enough for a reader to reproduce the search. The date range of studies, along with the date (or date period) the search was conducted, should also be provided.
A data-collection form provides a standardized means of collecting data from eligible studies. For a meta-analysis of correlational data, effect size information is usually collected as Pearson correlation coefficient (r). Partial correlations are often reported in research; however, these may inflate relationships in comparison to zero-order correlations. Moreover, the partialed-out variables will likely vary from study to
study. As a consequence, many meta-analyses exclude partial correlations
from their analysis. As a final resort, plot digitizers can be used to scrape data points
from scatterplots (if available) for the calculation of Pearson's r. Data on important study characteristics that may moderate observed
effects, such as the mean age of participants, should also be collected. A measure of study quality can also be included in these forms to assess the quality of evidence from each study. There are more than 80 tools available to assess the quality and risk
of bias in observational studies, reflecting the diversity of research
approaches between fields. These tools usually include an assessment of how dependent variables
were measured, appropriate selection of participants, and appropriate
control for confounding factors. Other quality measures that may be more
relevant to correlational studies include sample size, psychometric
properties, and method reporting.
A final consideration is whether to include studies from the grey literature, which is defined as research that has not been formally published. This type of literature includes conference abstracts, dissertations, and pre-prints. While the inclusion of grey literature reduces the risk of publication
bias, the methodological quality of the work is often (but not always)
lower than formally published work. Reports from conference proceedings, which are the most common source of grey literature, are poorly reported, and data in the subsequent publication are often inconsistent, with differences observed in almost 20% of published studies.
Methods and assumptions
Approaches
In general, two types of evidence can be distinguished when performing a meta-analysis: individual participant data (IPD), and aggregate data (AD). The aggregate data can be direct or indirect.
AD is more commonly available (e.g. from the literature) and typically represents summary estimates such as odds ratios or relative risks. This can be directly synthesized across conceptually similar studies
using several approaches. On the other hand, indirect aggregate data
measures the effect of two treatments that were each compared against a
similar control group in a meta-analysis. For example, if treatment A
and treatment B were directly compared vs placebo in separate
meta-analyses, we can use these two pooled results to get an estimate of
the effects of A vs B in an indirect comparison as effect A vs Placebo
minus effect B vs Placebo.
IPD evidence represents raw data as collected by the study
centers. This distinction has raised the need for different
meta-analytic methods when evidence synthesis is desired, and has led to
the development of one-stage and two-stage methods. In one-stage methods the IPD from all studies are modeled
simultaneously whilst accounting for the clustering of participants
within studies. Two-stage methods first compute summary statistics for
AD from each study and then calculate overall statistics as a weighted
average of the study statistics. By reducing IPD to AD, two-stage
methods can also be applied when IPD is available; this makes them an
appealing choice when performing a meta-analysis. Although it is
conventionally believed that one-stage and two-stage methods yield
similar results, recent studies have shown that they may occasionally
lead to different conclusions.
Statistical models for aggregate data
Fixed effect model
Forest Plot of Effect Sizes
The fixed effect model provides a weighted average of a series of study estimates. The inverse of the estimates' variance is commonly used as study
weight, so that larger studies tend to contribute more than smaller
studies to the weighted average. Consequently, when studies within a meta-analysis are dominated by a
very large study, the findings from smaller studies are practically
ignored. Most importantly, the fixed effects model assumes that all included
studies investigate the same population, use the same variable and
outcome definitions, etc. This assumption is typically unrealistic as research is often prone to several sources of heterogeneity.
If we start with a collection of independent effect size estimates, each estimate a corresponding effect size we can assume that where denotes the observed effect in the -th study, the corresponding (unknown) true effect, is the sampling error, and . Therefore, the 's are assumed to be unbiased and normally distributed estimates of their corresponding true effects. The sampling variances (i.e., values) are assumed to be known.
Random effects model
Most meta-analyses are based on sets of studies that are
not exactly identical in their methods and/or the characteristics of the
included samples. Differences in the methods and sample characteristics may introduce variability ("heterogeneity") among the true effects. One way to model the heterogeneity is to treat it as purely random. The
weight that is applied in this process of weighted averaging with a
random effects meta-analysis is achieved in two steps:
Step 1: Inverse variance weighting
Step 2: Un-weighting of this inverse variance weighting
by applying a random effects variance component (REVC) that is simply
derived from the extent of variability of the effect sizes of the
underlying studies.
This means that the greater this variability in effect sizes (otherwise known as heterogeneity),
the greater the un-weighting and this can reach a point when the random
effects meta-analysis result becomes simply the un-weighted average
effect size across the studies. At the other extreme, when all effect
sizes are similar (or variability does not exceed sampling error), no
REVC is applied and the random effects meta-analysis defaults to simply a
fixed effect meta-analysis (only inverse variance weighting).
The extent of this reversal is solely dependent on two factors:
Heterogeneity of precision
Heterogeneity of effect size
Since neither of these factors automatically indicates a
faulty larger study or more reliable smaller studies, the
re-distribution of weights under this model will not bear a relationship
to what these studies actually might offer. Indeed, it has been
demonstrated that redistribution of weights is simply in one direction
from larger to smaller studies as heterogeneity increases until
eventually all studies have equal weight and no more redistribution is
possible. Another issue with the random effects model is that the most commonly
used confidence intervals generally do not retain their coverage
probability above the specified nominal level and thus substantially
underestimate the statistical error and are potentially
overconfident in their conclusions. Several fixes have been suggested but the debate continues on. A further concern is that the average treatment effect can sometimes be
even less conservative compared to the fixed effect model and therefore misleading in practice. One interpretational fix that has
been suggested is to create a prediction interval around the random
effects estimate to portray the range of possible effects in practice. However, an assumption behind the calculation of such a prediction
interval is that trials are considered more or less homogeneous entities
and that included patient populations and comparator treatments should
be considered exchangeable and this is usually unattainable in practice.
There are many methods used to estimate between studies
variance with restricted maximum likelihood estimator being the least
prone to bias and one of the most commonly used. Several advanced iterative techniques for computing the between studies
variance exist including both maximum likelihood and restricted maximum
likelihood methods and random effects models using these methods can be
run with multiple software platforms including Excel, Stata, SPSS, and R.
Most meta-analyses include between 2 and 4 studies and such a sample is more often than not inadequate to accurately estimate heterogeneity.
Thus it appears that in small meta-analyses, an incorrect zero between
study variance estimate is obtained, leading to a false homogeneity
assumption. Overall, it appears that heterogeneity is being consistently
underestimated in meta-analyses and sensitivity analyses in which high
heterogeneity levels are assumed could be informative. These random effects models and software packages mentioned above
relate to study-aggregate meta-analyses and researchers wishing to
conduct individual patient data (IPD) meta-analyses need to consider
mixed-effects modelling approaches.
Quality effects model
Doi and Thalib originally introduced the quality effects model. They introduced a new approach to adjustment for inter-study variability by
incorporating the contribution of variance due to a relevant component
(quality) in addition to the contribution of variance due to random
error that is used in any fixed effects meta-analysis model to generate
weights for each study. The strength of the quality effects
meta-analysis is that it allows available methodological evidence to be
used over subjective random effects, and thereby helps to close the
damaging gap which has opened up between methodology and statistics in
clinical research. To do this a synthetic bias variance is computed
based on quality information to adjust inverse variance weights and the
quality adjusted weight of the ith study is introduced. These adjusted weights are then used in meta-analysis. In other words, if study i
is of good quality and other studies are of poor quality, a proportion
of their quality adjusted weights is mathematically redistributed to
study i giving it more weight towards the overall
effect size. As studies become increasingly similar in terms of quality,
re-distribution becomes progressively less and ceases when all studies
are of equal quality (in the case of equal quality, the quality effects
model defaults to the IVhet model – see previous section). A recent
evaluation of the quality effects model (with some updates) demonstrates
that despite the subjectivity of quality assessment, the performance
(MSE and true variance under simulation) is superior to that achievable
with the random effects model. This model thus replaces the untenable interpretations that abound in
the literature and a software is available to explore this method
further.
Network meta-analysis methods
A
network meta-analysis looks at indirect comparisons. In the image, A
has been analyzed in relation to C and C has been analyzed in relation
to B. However the relation between A and B is only known indirectly, and
a network meta-analysis looks at such indirect evidence of differences
between methods and interventions using statistical method.
Indirect comparison meta-analysis methods (also called
network meta-analyses, in particular when multiple treatments are
assessed simultaneously) generally use two main methodologies. First is the Bucher method, which is a single or repeated comparison of
a closed loop of three-treatments such that one of them is common to
the two studies and forms the node where the loop begins and ends.
Therefore, multiple two-by-two comparisons (3-treatment loops) are
needed to compare multiple treatments. This methodology requires that
trials with more than two arms have two arms only selected as
independent pair-wise comparisons are required. The alternative methodology uses complex statistical modelling
to include the multiple arm trials and comparisons simultaneously
between all competing treatments. These have been executed using
Bayesian methods, mixed linear models and meta-regression approaches.
Bayesian framework
Specifying a Bayesian network meta-analysis model involves writing a directed acyclic graph (DAG) model for general-purpose Markov chain Monte Carlo (MCMC) software such as WinBUGS. In addition, prior distributions have to be specified for a number of
the parameters, and the data have to be supplied in a specific format. Together, the DAG, priors, and data form a Bayesian hierarchical model.
To complicate matters further, because of the nature of MCMC
estimation, overdispersed starting values have to be chosen for a number
of independent chains so that convergence can be assessed. Recently, multiple R software packages were developed to simplify the model fitting (e.g., metaBMA and RoBMA) and even implemented in statistical software with graphical user interface (GUI): JASP.
Although the complexity of the Bayesian approach limits usage of this
methodology, recent tutorial papers are trying to increase accessibility
of the methods. Methodology for automation of this method has been suggested but requires that arm-level outcome data are available, and this is
usually unavailable. Great claims are sometimes made for the inherent
ability of the Bayesian framework to handle network meta-analysis and
its greater flexibility. However, this choice of implementation of
framework for inference, Bayesian or frequentist, may be less important
than other choices regarding the modeling of effects (see discussion on models above).
Frequentist multivariate framework
On the other hand, the frequentist multivariate methods
involve approximations and assumptions that are not stated explicitly or
verified when the methods are applied (see discussion on meta-analysis
models above). For example, the mvmeta package for Stata enables network
meta-analysis in a frequentist framework. However, if there is no common comparator in the network, then this has
to be handled by augmenting the dataset with fictional arms with high
variance, which is not very objective and requires a decision as to what
constitutes a sufficiently high variance. The other issue is use of the random effects model in both this
frequentist framework and the Bayesian framework. Senn advises analysts
to be cautious about interpreting the 'random effects' analysis since
only one random effect is allowed for but one could envisage many. Senn goes on to say that it is rather naıve, even in the case where
only two treatments are being compared to assume that random-effects
analysis accounts for all uncertainty about the way effects can vary
from trial to trial. Newer models of meta-analysis such as those
discussed above would certainly help alleviate this situation and have
been implemented in the next framework.
Generalized pairwise modelling framework
An approach that has been tried since the late 1990s is
the implementation of the multiple three-treatment closed-loop analysis.
This has not been popular because the process rapidly becomes
overwhelming as network complexity increases. Development in this area
was then abandoned in favor of the Bayesian and multivariate frequentist
methods which emerged as alternatives. Very recently, automation of the
three-treatment closed loop method has been developed for complex
networks by some researchers as a way to make this methodology available to the mainstream research
community. This proposal does restrict each trial to two interventions,
but also introduces a workaround for multiple arm trials: a different
fixed control node can be selected in different runs. It also utilizes
robust meta-analysis methods so that many of the problems highlighted
above are avoided. Further research around this framework is required to
determine if this is indeed superior to the Bayesian or multivariate
frequentist frameworks. Researchers willing to try this out have access
to this framework through a free software.
Diagnostic test accuracy meta-analysis
Diagnostic test accuracy (DTA) meta-analyses differ
methodologically from those assessing intervention effects, as they aim
to jointly synthesize pairs of sensitivity and specificity values. These
parameters are typically analyzed using hierarchical models that
account for the correlation between them and between-study
heterogeneity. Two commonly used models are the bivariate random-effects model and the hierarchical summary receiver operating characteristic (HSROC) model. These approaches are recommended by the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and are widely used in reviews of screening tests, imaging tools, and laboratory diagnostics.
Beyond the standard hierarchical models, other approaches
have been developed to address various complexities in diagnostic
accuracy synthesis. These include methods that incorporate differences
in threshold effects, account for covariates through meta-regression, or
improve applicability by considering test setting and clinical
variation. Some frameworks aim to adapt the synthesis to reflect
intended use conditions more directly. These extensions are part of an
evolving body of methodology that reflects growing experience in the
field and increasing demands from clinical and policy decision-makers.
Aggregating IPD and AD
Meta-analysis can also be applied to combine IPD and AD.
This is convenient when the researchers who conduct the analysis have
their own raw data while collecting aggregate or summary data from the
literature. The generalized integration model (GIM) is a generalization of the meta-analysis. It allows that the model
fitted on the individual participant data (IPD) is different from the
ones used to compute the aggregate data (AD). GIM can be viewed as a
model calibration method for integrating information with more
flexibility.
Validation of meta-analysis results
The meta-analysis estimate represents a weighted average across studies and when there is heterogeneity
this may result in the summary estimate not being representative of
individual studies. Qualitative appraisal of the primary studies using
established tools can uncover potential biases, but does not quantify the aggregate effect of these biases on the
summary estimate. Although the meta-analysis result could be compared
with an independent prospective primary study, such external validation
is often impractical. This has led to the development of methods that
exploit a form of leave-one-out cross validation, sometimes referred to as internal-external cross validation (IOCV). Here each of the k included studies in turn is omitted and compared
with the summary estimate derived from aggregating the remaining k- 1
studies. A general validation statistic, Vn based on IOCV has been developed to measure the statistical validity of meta-analysis results. For test accuracy and prediction, particularly when there are
multivariate effects, other approaches which seek to estimate the
prediction error have also been proposed.
Challenges
A meta-analysis of several small studies does not always predict the results of a single large study. Some have argued that a weakness of the method is that sources of bias
are not controlled by the method: a good meta-analysis cannot correct
for poor design or bias in the original studies. 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. More recently, and under the influence of a push for open practices in
science, tools to develop "crowd-sourced" living meta-analyses that are
updated by communities of scientists in hopes of making all the subjective choices more explicit.
Publication bias: the file drawer problem
A
funnel plot expected without the file drawer problem. The largest
studies converge at the tip while smaller studies show more or less
symmetrical scatter at the base.A
funnel plot expected with the file drawer problem. The largest studies
still cluster around the tip, but the bias against publishing negative
studies has caused the smaller studies as a whole to have an
unjustifiably favorable result to the hypothesis.
Another potential pitfall is the reliance on the available
body of published studies, which may create exaggerated outcomes due to
publication bias, as studies which show negative results or insignificant results are less likely to be published. 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 file drawer problem
characterized by negative or non-significant results being tucked away
in a cabinet, can result in a biased distribution of effect sizes thus
creating a serious base rate fallacy,
in which the significance of the published studies is overestimated, as
other studies were either not submitted for publication or were
rejected. This should be seriously considered when interpreting the
outcomes of a meta-analysis.
The distribution of effect sizes can be visualized with a funnel plot which (in its most common version) is a scatter plot of standard error versus the effect size. It makes use of the fact that the smaller studies (thus larger standard
errors) have more scatter of the magnitude of effect (being less
precise) while the larger studies have less scatter and form the tip of
the funnel. If many negative studies were not published, the remaining
positive studies give rise to a funnel plot in which the base is skewed
to one side (asymmetry of the funnel plot). 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. A negative or positive relation between standard error and effect size
would imply that smaller studies that found effects in one direction
only were more likely to be published and/or to be submitted for
publication.
Apart from the visual funnel plot, statistical methods for detecting publication bias have also been proposed. These are controversial because they typically have low power for
detection of bias, but also may make false positives under some
circumstances. For instance small study effects (biased smaller studies), wherein
methodological differences between smaller and larger studies exist, may
cause asymmetry in effect sizes that resembles publication bias.
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.
The problem of publication bias is not trivial as it is
suggested that 25% of meta-analyses in the psychological sciences may
have suffered from publication bias. 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.
Problems related to studies not reporting non-statistically significant effects
Studies often do not report the effects when they do not reach statistical significance. For example, they may simply say that the groups did not show
statistically significant differences, without reporting any other
information (e.g. a statistic or p-value). Exclusion of these studies would lead to a situation similar to
publication bias, but their inclusion (assuming null effects) would also
bias the meta-analysis.
Problems related to the statistical approach
Other weaknesses are that it has not been determined if
the statistically most accurate method for combining results is the
fixed, IVhet, random or quality effect models, though the criticism
against the random effects model is mounting because of the perception
that the new random effects (used in meta-analysis) are essentially
formal devices to facilitate smoothing or shrinkage and prediction may
be impossible or ill-advised. The main problem with the random effects approach is that it uses the
classic statistical thought of generating a "compromise estimator" that
makes the weights close to the naturally weighted estimator if heterogeneity
across studies is large but close to the inverse variance weighted
estimator if the between study heterogeneity is small. However, what has
been ignored is the distinction between the model we choose to analyze a given dataset, and the mechanism by which the data came into being. A random effect can be present in either of these roles, but the two
roles are quite distinct. There's no reason to think the analysis model
and data-generation mechanism (model) are similar in form, but many
sub-fields of statistics have developed the habit of assuming, for
theory and simulations, that the data-generation mechanism (model) is
identical to the analysis model we choose (or would like others to
choose). As a hypothesized mechanisms for producing the data, the random
effect model for meta-analysis is silly and it is more appropriate to
think of this model as a superficial description and something we choose
as an analytical tool – but this choice for meta-analysis may not work
because the study effects are a fixed feature of the respective
meta-analysis and the probability distribution is only a descriptive
tool.
Problems arising from agenda-driven bias
The most severe fault in meta-analysis often occurs when the person or persons doing the meta-analysis have an economic, social, or political agenda such as the passage or defeat of legislation. People with these types of agendas may be more likely to abuse meta-analysis due to personal bias. For example, researchers favorable to the author's agenda are likely to have their studies cherry-picked
while those not favorable will be ignored or labeled as "not credible".
In addition, the favored authors may themselves be biased or paid to
produce results that support their overall political, social, or
economic goals in ways such as selecting small favorable data sets and
not incorporating larger unfavorable data sets. The influence of such
biases on the results of a meta-analysis is possible because the
methodology of meta-analysis is highly malleable.
A 2011 study done to disclose possible conflicts of
interests in underlying research studies used for medical meta-analyses
reviewed 29 meta-analyses and found that conflicts of interests in the
studies underlying the meta-analyses were rarely disclosed. The 29
meta-analyses included 11 from general medicine journals, 15 from
specialty medicine journals, and three from the Cochrane Database of Systematic Reviews. The 29 meta-analyses reviewed a total of 509 randomized controlled trials
(RCTs). Of these, 318 RCTs reported funding sources, with 219 (69%)
receiving funding from industry (i.e. one or more authors
having financial ties to the pharmaceutical industry). Of the 509 RCTs,
132 reported author conflict of interest disclosures, with 91 studies
(69%) disclosing one or more authors having financial ties to industry.
The information was, however, seldom reflected in the meta-analyses.
Only two (7%) reported RCT funding sources and none reported RCT
author-industry ties. The authors concluded "without acknowledgment of
COI due to industry funding or author industry financial ties from RCTs
included in meta-analyses, readers' understanding and appraisal of the
evidence from the meta-analysis may be compromised."
For example, in 1998, a US federal judge found that the United States Environmental Protection Agency
had abused the meta-analysis process to produce a study claiming cancer
risks to non-smokers from environmental tobacco smoke (ETS) with the
intent to influence policy makers to pass smoke-free–workplace laws.
Comparability and validity of included studies
Meta-analysis may often not be a substitute for an adequately powered primary study, particularly in the biological sciences.
Heterogeneity of methods used may lead to faulty conclusions. For instance, differences in the forms of an intervention or the
cohorts that are thought to be minor or are unknown to the scientists
could lead to substantially different results, including results that
distort the meta-analysis' results or are not adequately considered in
its data. Vice versa, results from meta-analyses may also make certain
hypothesis or interventions seem nonviable and preempt further research
or approvals, despite certain modifications – such as intermittent
administration, personalized criteria and combination measures
– leading to substantially different results, including in cases where
such have been successfully identified and applied in small-scale
studies that were considered in the meta-analysis.Standardization, reproduction of experiments, open data and open protocols may often not mitigate such problems, for instance as relevant factors and criteria could be unknown or not be recorded.
There is a debate about the appropriate balance between
testing with as few animals or humans as possible and the need to obtain
robust, reliable findings. It has been argued that unreliable research
is inefficient and wasteful and that studies are not just wasteful when
they stop too late but also when they stop too early. In large clinical
trials, planned, sequential analyses are sometimes used if there is
considerable expense or potential harm associated with testing
participants. In applied
behavioural science, "megastudies" have been proposed to investigate
the efficacy of many different interventions designed in an
interdisciplinary manner by separate teams. One such study used a fitness chain to recruit a large number
participants. It has been suggested that behavioural interventions are
often hard to compare [in meta-analyses and reviews], as "different
scientists test different intervention ideas in different samples using
different outcomes over different time intervals", causing a lack of
comparability of such individual investigations which limits "their
potential to inform policy".
Weak inclusion standards lead to misleading conclusions
Meta-analyses in education are often not restrictive
enough in regards to the methodological quality of the studies they
include. For example, studies that include small samples or
researcher-made measures lead to inflated effect size estimates. However, this problem also troubles meta-analysis of clinical trials.
The use of different quality assessment tools (QATs) lead to including
different studies and obtaining conflicting estimates of average
treatment effects.
Applications in modern science
Graphical summary of a meta-analysis of over 1,000 cases of diffuse intrinsic pontine glioma and other pediatric gliomas, in which information about the mutations involved as well as generic outcomes were distilled from the underlying primary literature
Modern
statistical meta-analysis does more than just combine the effect sizes
of a set of studies using a weighted average. It can test if the
outcomes of studies show more variation than the variation that is
expected because of the sampling of different numbers of research
participants. Additionally, study characteristics such as measurement
instrument used, population sampled, or aspects of the studies' design
can be coded and used to reduce variance of the estimator (see
statistical models above). Thus some methodological weaknesses in
studies can be corrected statistically. Other uses of meta-analytic
methods include the development and validation of clinical prediction
models, where meta-analysis may be used to combine individual
participant data from different research centers and to assess the
model's generalisability, or even to aggregate existing prediction models.
Meta-analysis can be done with single-subject design as well as group research designs. This is important because much research has been done with single-subject research designs. Considerable dispute exists for the most appropriate meta-analytic technique for single subject research.
Meta-analysis leads to a shift of emphasis from single
studies to multiple studies. It emphasizes the practical importance of
the effect size instead of the statistical significance of individual
studies. This shift in thinking has been termed "meta-analytic
thinking". The results of a meta-analysis are often shown in a forest plot.
Results from studies are combined using different
approaches. One approach frequently used in meta-analysis in health care
research is termed 'inverse variance method'. The average effect size across all studies is computed as a weighted mean,
whereby the weights are equal to the inverse variance of each study's
effect estimator. Larger studies and studies with less random variation
are given greater weight than smaller studies. Other common approaches
include the Mantel–Haenszel method and the Peto method.
Seed-based d mapping
(formerly signed differential mapping, SDM) is a statistical technique
for meta-analyzing studies on differences in brain activity or structure
which used neuroimaging techniques such as fMRI, VBM or PET.
Different high throughput techniques such as microarrays have been used to understand Gene expression. MicroRNA
expression profiles have been used to identify differentially expressed
microRNAs in particular cell or tissue type or disease conditions or to
check the effect of a treatment. A meta-analysis of such expression
profiles was performed to derive novel conclusions and to validate the
known findings.
Meta-analysis of whole genome sequencing studies provides
an attractive solution to the problem of collecting large sample sizes
for discovering rare variants associated with complex phenotypes. Some
methods have been developed to enable functionally informed rare variant
association meta-analysis in biobank-scale cohorts using efficient
approaches for summary statistic storage.
Sweeping meta-analyses can also be used to estimate a
network of effects. This allows researchers to examine patterns in the
fuller panorama of more accurately estimated results and draw
conclusions that consider the broader context (e.g., how
personality-intelligence relations vary by trait family).
