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, [email protected]

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)

.

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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

https://pubrica.com/academy/systematic-review/what-data-to-extract-for-systematic-review/https://pubrica.com/academy/systematic-review/what-data-to-extract-for-systematic-review/https://pubrica.com/services/research-services/systematic-review/


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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