1. Population specification error:
2. Sample error:
3. Selection error:
4. Non- response error:
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PowerPoint Presentation
HOW TO HANDLE DISCREPANCIES WHILE YOU COLLECT DATA FOR SYSTEMIC
REVIEW
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Pubrica
Group:www.pubrica.com
Email: [email protected]
Today's Discussion
In-Brief Introduction
Defining Active Implantable Medical Devices Data Extraction for
Systemic Review Avoiding Data Extraction Mistakes
Conclusion
Outline
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. 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.
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 .
Contd...
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.
Defining Active Implantable Medical Devices
Anactivemedicaldeviceoperatesbyusingand converting a large
amount of energy.
Except for gravitational and direct human energies, active
devices can use any energy.
Active medical devices, as defined by the Therapeutic Goods
(Medical Devices) Regulations 2002, can be broadly classified into
two categories:
Contd...
Data Extraction for Systemic Review
One scientist extracted the characteristics and findings of the
observational cohort studies.
The main objectives 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.
Contd...
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.
Anotherauthordouble-checkedthedataextraction.Therewasnomasking,and
disputes were settled by conversation.
Avoiding Data Extraction Mistakes
The problem of calculating the wrong people or definition rather
than the correct concept is known as a population specification
error.
1. 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.
Contd...
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.
Contd...
before
It comes even though you don't want it.
We'veallpreparedoursampleframe going out on the field study.
Agoodsamplingframeallowsyoutocoverthe entire target community or
population.
3. SELECTION ERROR:
A self-invited data collection error is the same as a selection
error.
Contd...
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.
Contd...
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.
Contd...
It's not the same as random errors that have no known cause.
They established and used three criteria to determine
methodological quality because there was no recognized tool to
evaluate the empirical studies' organizational quality.
Self-determining data extraction by at least two people
Definition of positive and negative findings.
Contd...
authors
independently
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.
reportingbiasinthe
3.Safetyofselective empirical study
Foreachstudy,two evaluated these things.
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
extractionteam(e.g.experience)hasnotbeen adequately
demonstrated.
Contd...
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.
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