Presentation PowerPoint

HOW TO EVALUATE BIAS IN

META-ANALYSIS WITHIN

META-EPIDEMIOLOGICAL

STUDIES?

An Academic presentation by

Dr. Nancy Agnes, Head, Technical Operations, Pubrica

Group: www.pubrica.com

Email: sales@pubrica.com

T O D A Y ' S D I S C U S S I O N

Outline

Introduction

Bias in meta-analysis within

meta-epidemiological studies

Conclusion

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.

Contd...

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 approach and aims

to understand the impact of certain factors on the outcome.

Thus, they try to confirm or nullify the hypothesis in question.

The object of analysis is a study and not a patient or an individual.

Contd...

Results of meta-epidemiological study might be directly related to exposure but

can also be a result of an alternative effect that might have impacted the overall

study outcome.

These alternative effects can be a random error, a bias that can produce

incorrect results(Steenland et al., 2020).

Due to these effects, sometimes an association is falsely accounted for in the

outcome when it is not present and on the other hand, sometimes an association

is overlooked even in its presence.

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.

Contd...

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.

Bias arising due to lack of proper outcome data- The exaggeration of the

intervention effect can arise when the data of outcomes are either not completely/

falsely reported.

There are some examples when there is overestimation and underestimation of the

intervention effect even when the outcome has been properly recorded.

Contd...

This is caused due to attrition, but the average bias reported due to attrition

could not be combined as the definition of attrition differs across studies.

Bias arising due to improper result selection- There has been reports of

bias when the outcomes are not properly generated due to discrepancies

between results and methods.

Bias arising due to incorrectly measuring outcomes- Due to lack of proper

outcome accessors, bias arises in properly measuring the outcomes.

This results in improper estimation of intervention effects.

Contd...

In most meta-epidemiological studies, a written

protocol for selecting the studies need to be framed

before conducting the meta 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.

Contd...

Fig 1: Flowchart for selection of 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”.

Contd...

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).

Meta epidemiological studies helps in overcoming the challenges of

systematic reviews.

Out of all, it focuses to get rid of publication bias.

Contd...

Publication bias is also an important type of bias that

stresses upon the fact that the data used in meta-

epidemiological studies should also be drawn upon

from unpublished study sources (Lin, 2020).

It is sometimes observed that few studies are not

accepted for publishing as they report negative

results.

Thus, missing out on these can enhance the risk of

bias and can give a false impression about the

effectiveness of the interpretation(Tan et al., 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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