Introduction
Meta-analysis
is a type of statistical approach which synthesizes results from different
studies and the final result serves as a much stronger evidence than the one
collected from an individual study. It gives an estimate of the success of a
newly introduced treatment/ intervention or the risk factors associated with a
disease/ line of treatment(Hayden et
al., 2021).
Thus, it can serve as the best source for evidence-based clinical studies. The
studies used in meta-analysis can combine results from systematic review,
randomised controlled trials (RCT) etc. Meta epidemiological studies is a new
type of method which helps in closing the gap between trials and practice and
is a much improved version of systematic review(Page,
2020).
They adopt either systematic review or meta-analysis
approachand aims to understand the impact of certain factors on the
outcome. Thus, they try to confirm or nullify the hypothesis in question.
Bias in meta-analysis within meta-epidemiological studies
In some meta epidemiological
studies, the effect of interventions in RCT’s (Randomised Controlled
Trials) can be misunderstood leading to underestimation or overestimation of
the intervention (Christensen and Berthelsen, 2020). There can be several
reasons which have been elaborated bellow-
Bias arising due to randomisation-
The procedure of sequence generation or allocation concealment might vary the
effects of the introduced interventions. These two factors also affects the in
between heterogeneity.
Bias arising due to opting for unintended
interventions- This type of bias arises when the participant opts for an
intervention different from which they have been randomly allotted for.
In most meta-epidemiological studies, a written
protocol for selecting the studies need to be framed before conducting
themeta analysis. It is important to include all the related studies as
missing out on one can introduce bias and makes the study less effective (Pan et al., 2020). The protocol
must focus on the selection criteria (eligibility criteria, type of studies to
be included, etc.) of the studies to reduce section bias. Fig 1 depicts a
flowchart of selecting studies.
Alongside these, the other important points to be
included in the protocol are objectives of the study, hypothesis to be tested
etc(Steenland
et al., 2020).
According to some authors, it can be quite tricky to combine different study
designs of meta-epidemiological studies in a meta-analysis and thus have
stated “a meta-analysis may give a precise estimate of average bias, rather
than an estimate of the intervention’s effect” and that “heterogeneity
between study results may reflect differential biases rather than true
differences in an intervention’s effect”.In order to understand the amount of
bias that might have impacted the study outcome, it has been unanimously agreed
upon that all the non-randomized and observational studies included in the meta-analysis
should be assessed(Puljak et
al., 2020). But there has been no proper agreement on the guidelines of
assessing the risk of bias in different meta-analyses(Mathur and
VanderWeele, 2021).
Conclusion
The bias which
arises during different steps of the meta-analysis
must be addressed as this might report contradictory results. It must be noted
that false reports can impact medical research which can be fatal in few
aspects. The problem with meta-epidemiological study lies in
the fact that when the number of studies reduces, the statistical power also reduces.
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