NURS 8201 Use of Regression Analysis in Clinical Practice

Regression analysis is one of the statistical models used in estimating the relationship between variables. The researcher has the ability to determine the effect that an independent variable has on the dependent variable (Willis & Riley, 2017). For example, an increase in one or more values on the independent variable would have an effect on the dependent variable. This paper examines regression analysis was used by an author including its weaknesses and strengths.

Article Summary

The article authored by Hatakeyama et al., (2019) aimed at finding the relationship between quality of clinical practice guideline (CPGs) and overall assessment scores. This study considered the previous studies that had been done and published between 2011 and 2015. These selected studies were subjected through an independent valuation using AGREE II. The author analyzed the results using a regression analysis. For instance, the analysis included the effect that the six domains and 23 items has on the overall assessment. The study collected a total of 206 CPGs and correlated all the domains to the items on the overall assessment to determine the strength of the relationship before taking the regression analysis on the proposed items.

Use of Regression on the Article

The author decided to subject domain 3, domain 4, domain 5, and domain 6 of the regression analysis. Domain three represented rigor of development, domain four was for clarity of presentation, domain five was for applicability and finally domain 6 was for editorial independence. The analysis was majoring on how these domains influence the overall assessment (Hatakeyama et al., 2019). The analysis showed that all the domains had a significant relationship with the overall assessment. The author also found that four different items on AGREE II, which were item 8, 15, 19 and 22 had an effect on overall assessment. The regression analysis showed that the change in one unit of the items above had a significant change on the overall assessment which in this case acted as the dependent variable (Hatakeyama et al., 2019). Therefore, the improvement of overall assessment dependent on the increase and decrease of the items that acted as independent variables in this case.

Other statistical analysis that could have been used in the study is ANOVA analysis because it shows the strength of the relationship between the items selected. Besides, it allows the researcher to determine the effect that each dependent variables have on each other and how the relationship between the dependent variables can influence the study (Fontaine et al., 2019). Use of ANOVA tests in this study could have strengthened and relayed more information on the collection of items that could have a great impact on the overall assessment.

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The strength of the regression analysis is on the ability of the author to examine more than one dependent variable. According to the study the author was interested in 22 items and their effect on overall assessment. The study is able to report on the influence of 22 items more easily as compared to other methods that could have been complex (Hatakeyama et al., 2019). Despite the strength that regression analysis has on the study, the method also has its weakness it lacks the ability to examine the relationship between the independent variables considered in the study.

Conclusion

Regression analysis is a powerful tool in assessing the relationship between dependent and independent variables. The author in the selected the study has the ability to evaluate which of the 22 items have a high or low effect on the overall assessment.

References

Fontaine, G., Cossette, S., Maheu-Cadotte, M. A., Deschênes, M. F., Rouleau, G., Lavallée, A., … & Mailhot, T. (2019). Effect of implementation interventions on nurses’ behaviour in clinical practice: a systematic review, meta-analysis and meta-regression protocol. Systematic reviews, 8(1), 1-10. https://doi.org/10.1186/s13643-019-1227-x

Hatakeyama, Y., Seto, K., Amin, R., Kitazawa, T., Fujita, S., Matsumoto, K., & Hasegawa, T. (2019). The structure of the quality of clinical practice guidelines with the items and overall assessment in AGREE II: a regression analysis. BMC health services research, 19(1), 1-8. https://doi.org/10.1186/s12913-019-4532-0

Willis, B. H., & Riley, R. D. (2017). Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice. Statistics in medicine, 36(21), 3283-3301. https://doi.org/10.1002/sim.7372

Yeom, H. E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian, 28(1), 48-56. https://doi.org/10.1016/j.colegn.2020.04.007

This article was authored by Yeom (2020) and focuses on hypertension among Korean patients. According to a study, Korean patients with hypertension tend to believe that their high blood pressure is caused by factors such as heredity, stress, and aging, and as a result, they are less likely to engage in self-care behaviors such as monitoring their blood pressure and eating a healthy diet. The study also found that participants who believed that hypertension was due to controllable factors were more likely to engage in self-care behaviors (Yeom, 2021). This suggests that educational interventions which focus on increasing people’s understanding of the controllable causes of hypertension may be effective at encouraging them to adopt healthier lifestyles.

Goals and Purpose of the Research

The purpose of this study was to explore the causal beliefs about hypertension and self-care behavior in Korean patients. A total of 267 participants completed a questionnaire that assessed their causal beliefs about hypertension, as well as their self-care behavior (Yeom, 2021). The results of the study showed that the most common belief about hypertension was that it is caused by stress. The second most common belief was that hypertension is hereditary. Participants who believed that hypertension is caused by stress were more likely to engage in self-care behaviors, such as monitoring their blood pressure and eating healthy foods. The main goal of the research was to determine if there is causal beliefs about hypertension and self-care behavior among Korean patients. The purpose of this discussion is to analyze the selected article and determine the use of regression analysis in clinical practice.

How Linear or Logistic Regression was used in The Study

Linear regression was used in the study to examine the relationship between causal beliefs and self-care behavior. From the linear regression analysis, the study found that, overall, patients with more linear beliefs about the cause of hypertension were more likely to engage in self-care behaviors (Wongsuriyanan et al., 2020). This suggests that educating patients on the causes of hypertension may help encourage them to better manage their condition.

Other Quantitative and Statistical Methods Could Be Used To Address the Research Issue Discussed In the Article

It is possible to test causal beliefs on self-care intention and medication compliance by using ANOVA instead of linear regression. This would involve creating a model that includes both the independent and dependent variables, as well as a measure of the strength of the relationship between them (i.e., the correlation coefficient) (Liang et al., 2020). Doing this would allow you to determine whether there is a significant relationship between the two variables, after accounting for the variance in both.

Strengths and Weaknesses of the Study

One of the strengths of the study is that it was conducted in a real-world setting with a large sample size. However, the study has several weaknesses, including that it did not control for dietary intake or other lifestyle factors that could have influenced self-care behavior. Additionally, the study did not measure blood pressure or biomarkers of hypertension, which would have been useful to confirm the relationship between causal beliefs and self-care behavior (Yeom, 2021). One of the remedies to the above weakness is the formulation of the control study to confirm the correlation between causal beliefs and self-care. Another remedy is to accurately measure and record hypertension among patients that have been selected for the study.

The results of this study can help inform evidence-based practice by providing information on the most important factors that influence self-care behavior among patients with hypertension. This study is important because it provides empirical evidence for the importance of causal beliefs about hypertension and self-care behaviour (Yeom, 2021). For example, the findings of this study can be used to help inform evidence-based practice in the management of hypertension.

Conclusion

The purpose of this study was to explore the causal beliefs about hypertension among Korean patients and to examine the relationships between these causal beliefs and self-care behaviors. Regression analysis was used to determine the relationship between the variable. The results of this study can help inform evidence-based practice by providing information on the most important factors that influence self-care behaviour among patients with hypertension.

References

Liang, J., Bi, G., & Zhan, C. (2020). Multinomial and ordinal Logistic regression analyses with multi-categorical variables using R. Annals of Translational Medicine, 8(16). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475459/

Wongsuriyanan, C., Phattharayuttawat, S., & Ratta-apha, W. (2020). The Prevalence of Type D Personality and Correlations between Medication Self-Efficacy and Self-Care Behavior in Patients with Hypertension. https://doi.org/10.21203/rs.2.20328/v1

Yeom, H. E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian, 28(1), 48-56. https://doi.org/10.1016/j.colegn.2020.04.007

NURS 8201 Use of Regression Analysis in Clinical Practice