How to handle discrepancies while you collect data for systemic review – pubrica

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How to Handle Discrepancies While you
Collect Data for Systemic Review
Dr. Nancy Agnes, Head, Technical Operations, Pubrica, sales@pubrica.com
In-Brief
Systematic reviews have studied rather than reports
as the unit of interest. So, multiple reports of the
same study need to be identified and linked together
before or after data extraction. Because of the
growing abundance of data sources (e.g., studies
registers, regulatory records, and clinical research
reports), review writers can determine which
sources can include the most relevant details for the
review and provide a strategy in place to address
discrepancies if evidence were inconsistent
throughout sources(1)
. The key to effective data
collection is creating simple forms and gathering
enough clear data that accurately represents the
source in a formal and ordered manner.
I. INTRODUCTION
The systematic review is designed to find all
experiments applicable to their research question and
synthesize data about the design, probability of bias,
and outcomes of those studies. As a result, decisions
on how to present and analyze data from these studies
significantly impact a systematic review. Data
collected should be reliable, complete, and available
for future updating and data sharing (2)
. The methods
used to make these choices must be straightforward,
and they should be selected with biases and human
error in mind. We define data collection methods
used in a systematic review, including data extraction
directly from journal articles and other study papers.
II. DATA EXTRACTION FOR SYSTEMIC
REVIEW
One scientist extracted the characteristics and
findings of the observational cohort studies. The
mainobjectives of each scientific analysis were also
derived, and the studies were divided into two groups
based on whether they dealt with biased reporting or
source discrepancies. When the published results
were chosen from different analyses of the same data
with a given result, this was referred to as selective
analysis reporting. When information was missing in
one source but mentioned in another, or when the
information provided in two sources was conflicting,
a discrepancy was identified. Another author double-
checked the data extraction. There was no masking,
and disputes were settled by conversation (3)
.
III. AVOIDING DATA EXTRACTION MISTAKES
1. Population specification error:The problem of
calculating the wrong people or definition rather
than the correct concept is known as a population
specification error. When you don't know who to
survey, no matter what data extraction tool you
use, the data analysis is slanted. Consider who
you want to survey. Similarly, having population
definition errors occurs when you believe you
have the correct sample respondents or
definitions when you don't.
2. Sample Error:When a sampling frame does not
properly cover the population needed for a study,
sample frame error occurs. A sample frame is a
set of all the objects in a population. If you
choose the wrong sub-population to decide an
entirely alien result, you'll make frame errors are
a few examples of sample frames. A good
sampling frame allows you to cover the entire
target community or population.
3. Selection Error:A self-invited data collection
error is the same as a selection error. It comes
even though you don't want it. We've all
prepared our sample frame before going out on
the field study. But what if a participant self-
invites or participates in a study that isn't part of
our study? From the outset, the respondent is not
on our research's syllabus. When you choose an
incorrect or incomplete sample frame, the
analysis is automatically tilted, as the name
implies. Since these samples aren't important to
your research, it's up to you to make the right
evidence-based decision.
4. Non-response Error:The higher the non-
response bias, the lower the response rate. The
field data collection error refers to missing data
rather than an data analysis based on an incorrect
sample or incomplete data. It can be not easy to
maintain a high response rate on a large-scale
survey. Environmental or observational errors
may cause measurement errors. It's not the same
as random errors that have no known cause (4)
.

They established and used three criteria to determine
methodological quality because there was no
recognized tool to evaluate the empirical studies'
organizational quality.
1. Self-determining data extraction by at least
two people
2. Definition of positive and negative findings.
3. Safety of selective reporting bias in the
empirical study
For each study, two authors independently evaluated
these things. Since the first author was personally
involved in the study's design, an independent
assessor was invited to review it. Any discrepancies
were resolved through a consensus discussion with a
third reviewer who was not concerned with the
included studies (5)
.
IV. CONCLUSION
Data extraction mistakes are extremely common. It
may lead to significant bias in impact estimates.
However, few studies have been conducted on the
impact of various data extraction methods, reviewer
characteristics, and reviewer training on data
extraction quality. As a result, the evidence base for
existing data extraction criteria appears to be lacking
because the actual benefit of a particular extraction
process (e.g. independent data extraction) or the
composition of the extraction team (e.g. experience)
has not been adequately demonstrated. It is
unexpected, considering that data extraction is such
an important part of a systematic review. More
comparative studies are required to gain a better
understanding of the impact of various extraction
methods. Studies on data extraction training, in
particular, are required because no such work has
been done to date. In the future, expanding one's
knowledge base will aid in the development of

successful training methods for new reviewers and
students (6)
.
REFERENCES
1. Richards, Lyn. Handling qualitative data: A
practical guide. Sage Publications Limited,
2020.
2. Muka, Taulant, et al. "A 24-step guide on how to
design, conduct, and successfully publish a
systematic review and meta-analysis in medical
research." European journal of
epidemiology 35.1 (2020): 49-60.
3. vanGinkel, Joost R., et al. "Rebutting existing
misconceptions about multiple imputation as a
method for handling missing data." Journal of
Personality Assessment 102.3 (2020): 297-308.
4. Borges Migliavaca, Celina, et al. "How are
systematic reviews of prevalence conducted? A
methodological study." BMC medical research
methodology 20 (2020): 1-9.
5. Lunny, Carole, et al. "Overviews of reviews
incompletely report methods for handling
overlapping, discordant, and problematic
data." Journal of clinical epidemiology 118
(2020): 69-85.
6. Pigott, Terri D., and Joshua R. Polanin.
"Methodological guidance paper: High-quality
meta-analysis in a systematic review." Review of
Educational Research 90.1 (2020): 24-46.

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