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idden bias: Do you know what factors can influence the results of observational studies
In fields such as epidemiology, social sciences, psychology, and statistics, observational studies are methods used to draw inferences from a sample to a whole. In this type of research, the independent variables are not under the control of the researchers, and randomized controlled trials are often not possible due to ethical considerations or practical operational limitations. While observational studies can provide valuable insights, they also present challenges, not least because many factors can influence a study's findings and introduce bias.
Observational studies cannot usually draw definitive conclusions about the safety, effectiveness, or efficacy of certain practices, but they can provide information about “real-world” use and practice.
Definition of Observational Studies
Observational studies can take many different forms, but a common example is a study of the effects of a treatment on participants. In this type of research, subjects are assigned to treatment or control groups in a process that is beyond the control of the researcher. In a randomized controlled trial (RCT), participants are randomly assigned to different groups so that valid comparisons can be made. However, observational studies lack such an allocation mechanism, which naturally makes them face difficulties in inferential analysis.
Motivations and limitations
Sometimes researchers cannot control the independent variable, which may be due to a variety of reasons. Here are some examples:
- Randomized experiments would violate ethical standards. For example, to study the link between induced abortion and breast cancer, a controlled experiment would require randomizing pregnant women into groups, which is ethically unacceptable.
- If researchers want to study the public health effects of a comprehensive ban on public indoor smoking in a community, they usually need the community or legislature to voluntarily implement the ban, a process over which the researchers have no control.
- In some cases, a randomized trial may not be practical, such as studying a drug with a rare side effect, where there may be a shortage of participants on the topic.
Types of Observational Studies
Observational studies come in many forms, including:
- Case-control study: In this type, researchers compare two groups with different outcomes.
- Cross-sectional study: Data are collected on a population or a representative subset at a specific point in time.
- Longitudinal studies: involve observing associations with the same variables over a long period of time.
Bias and how to compensate
One of the challenges of observational studies is overcoming various potential biases. Here are some common biases and their effects:
Multiple comparisons bias: When testing multiple hypotheses simultaneously, there is a chance that significant results will be obtained simply due to chance.
- Missing variable bias: Unaccounted for or unobservable variables may significantly affect study results.
- Selection bias: Researchers may inadvertently collect certain information during the observation process, which affects the interpretation of the results.
Quality and availability
Observational studies produce results similar to those of randomized controlled trials, according to a 2014 Cochrane review (updated to 2024), raising questions about how to eliminate or reduce bias in future research.
ConclusionWhen considering the use and interpretation of observational studies, researchers must be aware of potential biases and their impact on the results. As mentioned earlier, effective research involves more than just an examination of data; it also involves a transparent understanding of potential impacts. Of course, this is a challenge not only to academia, but also to all fields of research - how do we find the truth in this biased environment?
