Introduction
It is the goal of any researcher to obtain objective data regarding the subject of their study. However, it might be challenging to find and select appropriate testing groups for bias-sensitive forms of trials, as their specifics may be linked with additional, often unaccredited data among willing participants. This paper will analyze the differences in perceived outcomes between observational studies and randomized controlled trials (RCTs) on the example of Vitamin E effects on cardiovascular diseases (CVDs).
Observational Studies
The studies discussed in the presented case show an essential fact: all variables that affect the results cannot be adequately assessed in the majority of cases. Observational data presents a number of limitations, risks, and biases linked to it, despite having situational uses (McMurray, 2018). Moreover, this type of study often presents data from a short period in a highly localized community with similar additional variables (Corrao et al., 2021). There are numerous outside sources that affect the selection of non-randomized participants, even when researchers attempt to list all potentially relevant factors.
Observational studies may suggest invalid outcomes since they are prone to be biased. Real-world data often presents compelling arguments when observed from a position of a practitioner due to its close representation of a particular setting they are familiar with (Chatterjee et al., 2018). Such an approach to data collection does not provide a researcher with experimental data that will be applicable to a broader range of situations and settings. In the presented case, Vitamin E has been a missing component in daily nutrients among people who were visiting researchers who selected their statistics for calculating the occurrence of CVDs. However, RCTs have proven that it had no direct effect on their cardiovascular system.
Some fields of study are much more sensitive to data collection methods, especially when they take into consideration vast communities. Many severe diseases, such as CVDs, are a cause of multiple organs’ malfunctions, as well as detrimental behavior. The scope of common sense cannot grasp their full extent, making observational studies an improper way to judge the efficiency of such treatment (Fanaroff et al., 2020). Variables, such as the lower CVD risk among people with higher socioeconomic status due to better access to healthy food, are often being misrepresented in non-randomized trials (Jayedi et al., 2019). It is paramount for scientists to control the sources of data during studies, as statistics vary among different samples of the population, creating countless unseen variables that affect the final results (Moore et al., 2017). The lack of Vitamin E in patients’ diets is not direct proof of causation, as doctors did not analyze the population as a whole.
Healthy User Bias
The healthy user effect plays a vital role in the selection of participants for any healthcare-related study. It relates to the fact that people who are willing to take part in such tests often already take measures against a disease they are interested in (Monti et al., 2018). With the lack of diversity among participants’ socioeconomic status and attitudes towards healthy lifestyles, it is impossible to obtain observational findings that are guaranteed not to be disproven later on (McMurray, 2018). Merely reporting these factors will not resolve the issue, as there are numerous other underlying factors with unknown consequences for those who suffer from CVDs the most.
Similar effects can be detrimental for treatment studies that focus on people with particular behavioral traits or habits. For example, the Massachusets Nutrition Survey had to be excluded from data when the benefits of Vitamin E in protection from cardiovascular diseases were studied by researchers due to its abnormal consumption by participants (Jayedi et al., 2019). True randomization is essential, as simple observations are more likely to include a sample of patients representing the negative side of a topic being researched.
Conclusion
In conclusion, the apparent difference is linked with the bias during the selection of participants for each study, as observational studies tend to be less objective. In turn, RCTs provide the essential data regarding the overall impact of a particular treatment on an average person from a vast selection of samples. The issue with Vitamin E and its perceived protection against CVDs lies in the dietary habits of communities that are affected by this disease the most. Real-world data is beneficial for studies that focus on smaller groups, yet results of large-scale research will be rendered unusable if they are biased. This notion regarding the validity of non-randomized studies puts a question on the widespread usage of outcomes from non-RCTs.
Healthy user bias is one of the most critical considerations for healthcare researchers during trials. They are easy to miss due to researchers’ personal bias and improper population selection methods. Doctors who observe patients with CVDs are less likely to meet individuals who pay significant attention to their diet in an attempt to keep themselves as healthy as possible. The presence of any bias during studies lowers their generalizability by a significant portion, making healthcare studies inapplicable outside of a small sample of people whose data was used.
References
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