which is appropriate to use fixed-effect or random effect statistical model while conducting...
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A meta-analysis is a statistical method for combining quantitative data from various studies that address the same or similar research question. Fixed-effect and random-effect methods help in measuring the summary effect of a meta-analysis. Both these approaches are very distinctive. Continue Reading: https://bit.ly/3t6r0ze For our services: https://pubrica.com/services/research-services/meta-analysis/ Why Pubrica: When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts. Contact us: Web: https://pubrica.com/ Blog: https://pubrica.com/academy/ Email: [email protected] WhatsApp : +91 9884350006 United Kingdom: +44- 74248 10299TRANSCRIPT
Copyright © 2021 pubrica. All rights reserved 1
Which is Appropriate to use Fixed-Effect or Random Effect Statistical Model
while Conducting Meta-Analyses
Dr. Nancy Agnes, Head,
Technical Operations, Pubrica
Keywords:
Meta analysis, fixed effect model, random
effect model, statistical model, statistical
analysis, sources of heterogeneity, null
hypothesis
I. INTRODUCTION
Meta-analysis is the statistical analysis of
data and an essential aspect of systematic
reviews. Here, the study is conducted based
on mathematical models. Meta-analyses are
applicable for a number of purposes,
including synthesizing data on the results of
interventions and supporting evidence-based
policy and practice. A meta-analysis is a tool
with multiple applications in various fields
like medicine, healthcare, pharmaceuticals,
psychology, ecology, education,
criminology and business (Borenstein 2021).
The two most popular models used in
conducting meta-analyses are fixed-effect
and random-effect models. This review
focuses on and compares both these models
based on recent studies.
II. META-ANALYSES - DIFFERENT
COMPONENTS
A meta-analysis is a statistical method for
combining quantitative data from various
studies that address the same or similar
research question (Schober 2020). A number
of steps are involved in conducting a meta-
analysis. These include - question framing,
formation of search strategy, the search of
literature database, selection of articles, data
extraction, examination of quality of the
articles, test for heterogeneity, estimation of
summary effect, evaluation of the sources of
heterogeneity, assessment of publication
bias and finally, presentation of results
(Wang 2021).
Fixed-effect and random-effect methods
help in measuring the summary effect of a
meta-analysis. Both these approaches are
very distinctive.
III. FIXED AND RANDOM EFFECT
MODELS
In the fixed-effect model, it is assumed that
there is one real effect that underpins all of
the studies in the research, and that all
variations in observed results are due to
sampling error in the fixed-effect model. It
is also known as the common-effect model.
In this model, all variables that could affect
the effect size is the same across all studies,
and therefore the true effect size is the same
across all studies(Borenstein 2021). The
pooled or summary effect in a fixed-effect
meta-analysis estimates this typical true
effect size (Schober 2020). Two conditions
must be satisfied in order for a fixed-effect
model to be applied. To begin, one must be
confident in the similarity of all studies
Copyright © 2021 pubrica. All rights reserved 2
included in the meta-analysis and that
synthesizing the data is appropriate. Next,
calculation of common-effect size is
considered that is only applicable to the
meta-analysis population (Spineli 2020a).
Random effect models, on the other hand,
presume a different underlying effect for
each sample and treat this as a random
source of variance (Wang 2021). In this
model, It is often assumed that true effects
are normally distributed, or they differ from
study to study (Borenstein 2021).
IV. THE COMPARISON
Both of these widely used meta-analysis
models have their own set of limitations.
When the heterogeneity of studies cannot be
neglected, the common-effect model can
produce misleading results. When the
number of studies is small, the CI
(confidence interval) for the mean effect
based on the random-effect model can be too
large to be helpful. Since a large portion of
meta-analyses includes numbers of studies
with non-negligible variability, these
limitations are significant roadblocks in
practice (Lin 2020). Another point to note is,
as we compare the weighting schemes of
these two models, we can observe that as we
move from a fixed-effect to a random-
effects model, larger studies tend to lose
influence, and smaller studies tend to gain
influence (Spineli 2020 *(a)).
Furthermore, the different assumptions for
fixed-effect and random-effect models
indicate different meanings of the variance
(resulting in distinguished computations of
the meta-analysis results due to varying
weighting schemes) and the distinguished
null hypothesis of no linear correlation
determinations. The only source of error in a
fixed-effect model is within-study variance
and, the null hypothesis states that the
typical true effect size is unrelated to the
covariate of interest. Contrastingly in a
random-effects model, both within-study
and between-study variances are sources of
error and, the null hypothesis states that the
mean of the true effect size is unrelated to
the covariate of interest (Spineli 2020 *(b)).
V. CONCLUSION
To conclude, a fixed-effect model can only
be applied if there are potential factors that
indicate that the studies involved are
identical for all intents and purposes
(Borenstein 2007). However,
implementation of the fixed-effect model is
rarely possible in practice. The effect size
varies from study to study in real-world
synthesis. It is due to a variety of factors like
differences in participant mixes and
intervention implementation. As a result, a
random-effects model appears to be
sufficient and efficient in most meta-
analyses (Spineli 2020 *(a)).
Fig. 1: Comparison of fixed and random effect
statistical models
Copyright © 2021 pubrica. All rights reserved 3
The question of which model matches the
distribution of effect sizes and takes into
account the appropriate source(s) of error
must be the sole consideration when
choosing a model (Borenstein 2021).
Finally, the best model to use depends
highly on the type of study conducted and
the nature of the goals that it wants to
achieve.
REFERENCES
1. Borenstein, M., Hedges, L., & Rothstein, H. (2007).
Meta-analysis: Fixed effect vs. random effects. Meta-
analysis. Com.
2. Borenstein, M., Hedges, L. V., Higgins, J. P., &
Rothstein, H. R. (2021). Introduction to meta-analysis.
John Wiley & Sons.
3. Lin, E., Tong, T., Chen, Y., & Wang, Y. (2020).
Fixed-effects model: the most convincing model for
meta-analysis with few studies. arXiv preprint
arXiv:2002.04211.
4. Schober, P., & Vetter, T. R. (2020). Meta-Analysis in
Clinical Research. Anesthesia and analgesia, 131(4),
1090–1091.
5. Spinelii, L. M., & Pandis, N. (2020). The importance
of careful selection between fixed-effect and random-
effects models. American journal of orthodontics and
dentofacial orthopedics, 157(3), 432-433. *(a)
6. Spineli, L. M., & Pandis, N. (2020). Fixed-effect
versus random-effects model in meta-regression
analysis. American journal of orthodontics and
dentofacial orthopedics, 158(5), 770-772. *(b)
7. Wang, X. M., Zhang, X. R., Li, Z. H., Zhong, W. F.,
Yang, P., & Mao, C. (2021). A brief introduction of
meta-analyses in clinical practice and research. The
journal of gene medicine, e3312. Advance online
publication.