How to handle discrepancies while you collect data for systemic review – Pubrica
                     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
Outlin
e In-Brief  
Introductio
n
Defining Active Implantable Medical 
Devices  Data Extraction for Systemic 
Review  Avoiding Data Extraction 
Mistakes
Conclusion
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.
Introductio
The systematic review is designed to find all  
experiments applicable to their research question and n
 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  
Implantab
An active medical device operates by using le  
and  converting a large amount of energy.
Medical  
Except for gravitational and direct human energies,  Devices
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  
One scientist extracted the characteristics and findings 
Extraction   of the observational cohort studies.
for 
The main objectives of each scientific analysis were  
Systemic  also derived, and the studies were divided into two  
Review 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.
Another author double-checked the data extraction. There was no masking,
and  disputes were settled by conversation.
1. POPULATION SPECIFICATION ERROR: Avoiding 
The problem of calculating the wrong people or  Data  
definition rather than the correct concept is known 
 as a population specification error. Extraction  
Mistakes
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...
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 before
frame  going out on the field study.
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...
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.
1.Self-determining data extraction by at least two  
people
2. Definition of positive and negative findings.
Contd...
3. Safety of reporting bias in the
selective  empirical 
study
authors independently
For each study,
two  evaluated these 
tShiincges .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.
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.
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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