Summary of the Study
Colorectal cancer (CRC) is one of the common cancerous ailments leading to premature deaths. The prerequisite to lowering its prevalence is determining its primary cause and eliminating it to reduce the risk. Antibiotics are commonly used to fight infections in humans and are highly used despite their side effects.
One side effect of antibiotics is a fluctuating gut microbiota. The study aims to confirm the relationship between antibiotics and CRC risk. The significant results confirmed a direct relationship between CRC and antibiotics, as previously reported (Lu et al., 2022). The study concludes that constant use of antibiotics leads to colorectal carcinogenesis, jeopardizing the quality of life. Further, antibiotics had a different impact on women.
Critical Appraisal of the Methods Used
Context and Objectives
The matched case-control method was selected to investigate the relationship between the disease and antibiotic use. The main context behind the study is to determine the statistical relationship between CRC prevalence and antibiotic exposure. The context of the study was to inform the audience that a person taking antibiotics is at a higher risk of contracting colorectal cancer than patients not taking antibiotics.
The study’s context aligns with its research objectives. The study aims to determine how antibiotic use increases CRC risk. Since the study investigates the cause of a disease, a healthy person is used as a control to match all the factors that cause the disease. The method used is one of the best because it offers a direct relationship between the use of antibiotics and the risk of contracting cancer.
The exposure of interest is antibiotics, and the people using the drugs were used as a sample to match the risk factors. Forty thousand five hundred forty-five cases of CRC and 202,720 controls were used to determine the relationship. The results showed 95% confidence, indicating greater accuracy in the relationship determined (Lu et al., 2022).
Accuracy
The main strength of the method is that it balances the number of cases, consequently reducing variance and increasing accuracy in the discourse. It further strengthens control over confounding and meets researchers’ expectations. Before the analysis began, the measures to avoid confounding included restriction, randomization, and statistical control. By restricting the confounding variables, the method improves the study’s accuracy.
Confirmation Bias
Confirmation bias was evident in the study because the researcher had pre-existing beliefs and opinions on the relationship between CRC and antibiotics. The large sample population confirms a higher relationship between the variables and the risk factors. Previous research is confirmed using the study’s findings, and the conclusion is key to the general outcome.
The population characteristics introduced increased bias, and the outcome may be predetermined. As a result of the bias, a robust association between antibiotics and cancer was determined. Therefore, confirmation bias was readily detected in the research, as the researcher could easily recall previous inferences to achieve a better outcome in the discourse.
Recall Bias
The main limitation of the method is recall bias, as participants tend to recall past outcomes and guess the study’s outcome. The recall bias is likely to affect the general outcome of the study. Case-control studies are retrospective, which can lead to spurious correlations between exposure and outcomes. The results are quickly affected by the composition of the control group. The researchers will also likely miss confounding factors, leading to inaccurate inferences. The biases and limitations of the study design influenced the results by predefining outcomes, thereby affecting the validity of the output.
Data Analysis and Confounding
A confounder is a variable in statistics that influences both the dependent and independent variables, thereby affecting the outcome. It is imperative to note that confounding causes spurious associations and cannot be relied on to establish an association or correlation. Since the previous experience with correlation would have led the researchers to a predetermined outcome. The two primary methods for eliminating confounders in analysis are multivariate analysis and stratification. One of the strengths of the research is that it applied two primary methods to control for confounding and achieve an accurate outcome.
Stratification Method
The method controls for confounding by creating more than two variables that are unaffected by the relationship. The researcher ensured that, in addition to comparing the use of antibiotics and the CRC in a direct relationship, a new variable for women was identified. The case selection was minimal compared to the control to promote randomization. The stratification method was appropriately used to introduce randomization, thereby reducing bias. Consequently, the accuracy of the final results increased, and the outcome was more reliable than in previous research, which had fewer variables.
Multivariate Method
A multivariate analysis design determines how accurately the outcome is estimated by removing confounding factors. The study used multivariate analysis, which allowed multiple outcomes to be determined simultaneously. Since previous research had established a direct relationship between CRC and antibiotic use, the analysts introduced a third variable whose effect was still to be determined. All variables’ effects are considered to avoid recall bias and other elements of confirmation bias that might arise over time (Aho et al., 2019). It is paramount to note that controlling for confounding was sufficient, as all confounding and potential barriers were eliminated to achieve a better outcome.
Measures of Association and Statistical Stability
Measures of association are necessary in research because they help determine how two variables relate to one another. When the coefficients are found, it becomes easier to understand the relationship and find an antidote to solve the problem. The method of association used was a relative risk analysis, which showed the impact of antibiotics on a person’s cancer risk. Some experiments’ results are transient and cannot be sustained, jeopardizing the outcomes. Statistical stability measures help determine whether the research outcome can be sustained.
The researcher successfully controlled the error, resulting in an accurate outcome in due course (Aho et al., 2019). The research design used a confidence interval to assess statistical stability and improve outcomes. A 95% confidence interval is used in the research to indicate the acceptable levels of error in the outcome (Lu et al., 2022). A higher confidence interval is key to ensuring the results are as accurate as possible.
Data Interpretation
Results and analysis are the most critical steps in research because decisions are made from their integration. It is therefore essential for the researcher to improve the accuracy of the findings. The research on the relationship between antibiotics and CRC inferred that antibiotics led to a higher probability of proximal colon cancer.
Further, there was an inverse association between antibiotics and colon cancer in women. The results were not only accurate but also key to the formulation of results for a better outcome. The concluding statement, however, states that the outcome strengthens the findings from previous research, which confirms the presence of confirmation bias in the research.
The non-differential misclassification helps determine how strongly the evidence points toward the conclusion. The direction of the bias was away from the bias because the relationship between antibiotics and the CRC condition was positive. The magnitude is greater because most controlled studies have shown that antibiotics directly affect cancer status.
However, the study’s limitations were not adequately addressed, and the confirmation bias from previous research was present in the analysis and the conclusion. However, the author’s main conclusion was accurate and backed by the findings and previous research. Antibiotics are the primary cause of CRC, and medical doctors and other healthcare practitioners must find a way to modify the antibiotics to reduce the cancer risk.
The results can be generalized to a large population because the sample size was large enough. The research further informs what should be considered in future research, especially when determining a long-term relationship. Other side effects of the antibiotics must be determined through additional research.
References
Aho, V. T., Pereira, P. A., Voutilainen, S., Paulin, L., Pekkonen, E., Auvinen, P., & Scheperjans, F. (2019). Gut microbiota in Parkinson’s disease: temporal stability and relations to disease progression. EBioMedicine, 44, 691-707.
Lu, S. S. M., Mohammed, Z., Häggström, C., Myte, R., Lindquist, E., Gylfe, Å. Guelpen, B. V. & Harlid, S. (2022). Antibiotics use and subsequent risk of colorectal cancer: a Swedish nationwide population-based study. JNCI: Journal of the National Cancer Institute, 114(1), 38–46.