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Bias and confounding
When it comes to healthcare, bias and confounding are standard terms in the delivery of services related to the sector. They mainly come up in an epidemiological study case. Bias, for instance, in an error or a systematic mishap that happens due to incorrect presentation of estimates regarding an effect of the exposure on a particular interest. Necessarily, the kind of error may give a high or a low estimate regarding some actual value of items. It may also have some issues with the direction of the failure, as it might have been noted.
Essentially, it is hard to quantify such errors in terms of the reach and the magnitude. It happens in a way that makes the adjustments on the forms as well as the analysis of the necessary information (Sadhra, Kurmi, Sadhra, Lam, & Ayres, 2017). Considerations and the control of the data and control show that bias can be made at the initial stages of design and conduct and will go a long way to affect the results of the study.
On the other hand, confounding regards the provision of alternative or rather another explanation on the connection between some exposure and the outcome of such data. Some of the disclosure may not correlate with the risk factor. This may be connected in some way to the finding reported in such epidemiology; it is, therefore, essential to understanding the issues or the factors that have a direct link to t the disease as well as those that exist as proxy measures (Kim & Basu, 2016). Such actions are necessary to understand the unknown cases and causes of disease. Proxy causes can be issues of age as well as economic status and the like. Considering a variable as a confounder is essential such as associating some variable to the outcome or, on the other hand, something that lies in the pathway between the exposure and disease itself.
This is just a way of modification and includes a reduction of the potential of confounding by putting a random assignment that brakes any linkages between the subject and the confounders (Streeter et al., 2017). In such a response, it is possible to generate groups that can be compared with the resulting variable fairly.
American Psychological Association. Publication Manual of the American Psychological Association (6th Ed.). Washington, DC: Author.
Kim, D. D., & Basu, A. (2016). Estimating the medical care costs of obesity in the United States: systematic review, meta-analysis, and empirical analysis. Value in Health, 19(5), 602-613.
Sadhra, S., Kurmi, O. P., Sadhra, S. S., Lam, K. B. H., & Ayres, J. G. (2017). Occupational COPD and job exposure matrices: a systematic review and meta-analysis. International Journal of Chronic Obstructive Pulmonary Disease, 12, 725.
Streeter, A. J., Lin, N. X., Crathorne, L., Haasova, M., Hyde, C., Melzer, D., … & Henley, W. E. (2017). Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review. Journal of Clinical Epidemiology, 87, 23-34.
What is researcher bias and how to we control it?
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Researcher bias refers to the systematic errors or distortion of research findings that occur due to the researcher’s personal preferences, beliefs, or values influencing the design, conduct, or interpretation of the study. It can introduce unintended biases and compromise the validity and reliability of the research outcomes.
To control researcher bias, several strategies can be implemented:
1. Use of standardized protocols: Researchers should adhere strictly to standardized protocols and procedures to minimize bias. This includes following established research methodologies, using validated measurement tools, and maintaining consistency in data collection and analysis.
2. Blind and double-blind study designs: Employing blind or double-blind study designs can minimize bias by preventing the researcher and/or participants from influencing or being influenced by their biases. In a blind study, either the researcher or the participant is unaware of the intervention or group assignment, while in a double-blind study, both the researcher and the participant are unaware.
3. Randomization and allocation concealment: Randomization of participants into different study groups and allocation concealment of treatment assignments can help minimize bias by ensuring that participants are assigned to groups without any systematic preference or influence from the researcher.
4. Peer review and collaboration: Inviting other researchers or subject experts to review the study design, methodology, and findings can provide valuable perspectives and unbiased feedback. Collaboration with diverse research teams can also help reduce individual biases.
5. Transparency and disclosure: Researchers should be transparent and disclose any potential conflicts of interest or personal biases that may have an impact on the research. This includes reporting funding sources, affiliations, and any relevant personal or financial relationships.
6. Validation and triangulation of findings: Researchers should aim to cross-validate their findings using multiple methods or sources of data. Triangulating findings from different perspectives or approaches can help identify and minimize bias by providing a more comprehensive understanding of the research question.
7. Consistent evaluation and feedback: Regular evaluation of the researcher’s performance, including their adherence to protocols and potential biases, can provide feedback for improvement. This can involve peer evaluation, supervision, or mentorship to ensure research integrity.
By implementing these strategies, researchers can reduce the chances of bias and enhance the quality and reliability of their research outcomes.