USE OF REGRESSION ANALYSIS IN CLINICAL PRACTICE NURS 8201

USE OF REGRESSION ANALYSIS IN CLINICAL PRACTICE NURS 8201

Regression analysis is how we measure cause and affect relationships and determine if they are statistically sound or not. Correlation alone is not causation and that is why patterns and influence must be studied (Holmes, Illowsky, and Dean, 2017). If a regression analysis were done on BMI, there are many probable independent variables. The easiest one and most common to think of would be the patient’s diet. We could break this down and become more specific such as total cholesterol intact or total fat intact. Other variable to consider would be exercises or illnesses such as lipedema or lymphedema. Also, things such as COPD and CHF are important to consider. As mentioned in our lesson this week, correlation is not causation and conducting further experiments and statistics is needed to determine whether the results are based on influence or coincidence.

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In a study published in Environmental Health Perspectives blood pressure, heart rate, and cardiac biomarkers and the correlation with air pollution was studied. The dependent variable being the blood pressure, heart rate, and biomarkers, and the independent variable was exposure to air pollution. This study took place between 1995-2013. The results state “We observed some evidence suggesting distributional effects of traffic-related pollutants on systolic blood pressure, heart rate variability, corrected QT interval, low density lipoprotein (LDL) cholesterol, triglyceride, and intercellular adhesion molecule-1 (ICAM-1)”. There conclusion also uses subjective words such as “may effect” (Bind, Peters, Koutrakis, Coull, Vokonas, and Schwartz, 2016). With this in mind and the lack of knowledge of other factors related to the participants health I would say it is difficult to exclude the possibility of coincidence in this specific study.

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Brennaa Sullivan

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References:

Bind, M., Peters, A., Koutrakis, P., Coull, B., Vokonas, P., & Schwartz, J. (2016). Quantile Regression Analysis of the Distributional Effects of Air Pollution on Blood Pressure, Heart Rate Variability, Blood Lipids, and Biomarkers of Inflammation in Elderly American Men: The Normative Aging Study. Environmental Health Perspectives. https://ehp.niehs.nih.gov/doi/10.1289/ehp.1510044#:~:text=Results%20%20%20%20Outcomes%20%20%20,%20%20%20%2014%20more%20rows (Links to an external site.)
Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. OpenStax.

This is insightful Brennaa, both correlation and regression analysis are applied in the determination of the relationship that exist between variables. In both cases, the variables need to be normally distributed and possess a normal distribution. However, there is the difference between the two statistical approaches (Kasuya, 2019). While correlation is only used to measure the association between two continuous variables, regression analysis is used to determine relationship between one dependent variable and one or more independent variables. Additionally, regression analysis is how we measure cause and affect relationships and determine if they are statistically sound or not.

Correlation alone is not causation and that is why patterns and influence must be studied (Kasuya, 2019). Performing regression analysis in Body Mass Index (BMI) requires the consideration of different independent variables. Apart from diet and the rate of physical activities, another possible independent variable would be height of an individual or the study participants. Height is always considered in the computation of the BMI, therefore, it is one of the independent variables for the BMI. Also, the values of height are always continuous. However, data analyst need to ensure that there is a normal distribution.

Physical activities are known to reduce body mass index. In other words, continuous physical activities always aids in the breakdown of excessive body fast that contribute to the increase in BMI. Also, excessive or overeating and overconsumption of junk or fatty foods have been established as the major contributors to increase in BMI. Before undertaking correlation and regression analysis, there is always the need to undertake normality tests to ensure that both the dependent and independent variables meets the requirements for undertaking parametric tests or inferential statistical analysis.  

Reference

Kasuya, E. (2019). On the use of r and r squared in correlation and regression (Vol. 34, No. 1, pp. 235-236). Hoboken, USA: John Wiley & Sons, Inc. Retrieved from: https://esj-journals.onlinelibrary.wiley.com/doi/abs/10.1111/1440-1703.1011

Regression analysis provides the researcher with an opportunity to predict and explore future outcomes. Whether it is to determine prevention methods, promote opportunities for learning, or propose new treatments, looking towards the future can have a significant impact on patient care and sustained positive patient outcomes.

This week, you explore regression analysis, paying particular attention to linear regression. Linear regression is used to “estimate the value of a dependent variable based on the value of an independent variable” (Gray & Grove, 2020). In your Discussion, you will apply your understanding of this statistical technique as it concerns use in a research study.

Photo Credit: wutzkoh / Adobe Stock

For this Discussion, you will select an article on a study to examine the strengths and weaknesses in the use of linear regression. Consider how you might remedy the weaknesses associated with the application of linear regression and reflect on how the findings of the study that you selected might contribute to various areas of your practice.

_Reference:

_{Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.}

To Prepare:

• Review the articles in this week’s Learning Resources and evaluate their use of linear regression. Select one article that interests you to examine more closely in this Discussion.
• Critically analyze the article that you selected and consider the strengths and weaknesses described.
• Reflect on potential remedies to address these weaknesses, and how the findings from this study may contribute to evidence-based practice, the field of nursing, or society in general.

By Day 3 of Week 7

Post a brief description of the article that you selected, providing its correct APA citation. Critically analyze the article by addressing the following questions:

• What are the goals and purposes of the research study that the article describes?
• How is linear or logistic regression used in the study? What are the results of its use?
• What other quantitative and statistical methods could be used to address the research issue discussed in the article?
• What are the strengths and weaknesses of the study?

Then, explain potential remedies to address the weaknesses that you identified for the research article that you selected. Analyze the importance of this study to evidence-based practice, the nursing profession, or society. Be specific and provide examples.

Thanks for the great post. I really enjoyed reading your well written initial post. De Groot et al.’s (2020) study provides a critical examination of the use of electronic health records (EHRs) and standardized terminologies in nursing documentation. Their investigation into nursing staff’s experiences with EHRs in various healthcare settings is particularly noteworthy, as it highlights both the potential benefits and challenges associated with implementing these technologies. The study’s use of multiple linear regression to analyze the relationship between the use of standardized terminology in EHRs and the perceived support these systems provide to nursing staff is a significant contribution to understanding how EHRs impact nursing practice. However, the reliance on a non-validated questionnaire, albeit developed with expert consultation, introduces a degree of uncertainty regarding the findings’ validity. This is a critical consideration, as the accurate assessment of nursing staff experiences is essential for understanding the true impact of EHRs on nursing practice.

In addressing the study’s findings, it is also important to consider broader research in the field. For instance, Alpert et al. (2021) conducted a study on the efficiency of EHR systems in various healthcare settings, providing insights into how these systems can be optimized for better user experience and patient care. Additionally, research by Smith and Jones (2019) on the impact of EHRs on patient outcomes in nursing homes offers a complementary perspective, emphasizing the importance of EHRs in enhancing the quality of care. These studies and De Groot et al.’s (2020) work underscore the complexity of EHR implementation in nursing practice. They highlight the need for ongoing research and development to ensure these systems are as beneficial and user-friendly as possible for healthcare professionals.

By Day 6 of Week 7

Read a selection of your colleagues’ responses and respond to at least two of your colleagues on two different days in one or more of the following ways:

• Ask a probing question, substantiated with additional background information, evidence, or research.
• ·Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
• Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library.
• Validate an idea with your own experience and additional research.
• Suggest an alternative perspective based on additional evidence drawn from readings or after synthesizing multiple postings.
• Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.

Linear regression is one of the most commonly used type of predictive analysis in which estimates are used to explain the relationship between two things. The overall idea of linear regression is to examine if a set of predictor variables do a good job in predicting an outcome (dependent variable) or which variables in particular are significant predictors of the outcome variables and what they do (Statistic solution, 2020).  This form of analysis estimates coefficients of the linear equation, involving one or more independent variable that best predicts the value of the dependent variable.

As a result, they fit into a straight line or surface that minimizes the discrepancies between predicted and output values (Sathwick, 2020). Also, linear regressions help healthcare organizations collect massive data and use these data manage information. It would also provide better insights on patterns and relationships that would help understand its analytical connection. Since there is a new surge of Covid-19 almost every year for the past three years now, a study that I found interesting in conjunction with the Covid-19 is the associated stress that comes with this ongoing pandemic.

            Being a nurse in the ICU during the outbreak, I can clearly remember the days where not only were there many patients being admitted during the outbreak, but also the frontline healthcare teams were increasingly also catching the virus. This pandemic has prompted many nurses to retire and the unit short staffed every day. Accompanied with the staff shortage is the constant PPE and infection disease management policy changes. Dealing with the unknown, often puts people in a vulnerable risk for infection and psychological effects that should be monitored and understood. This would then assist in protecting the frontline workers and research ways to increase resilience amidst the pandemic outbreak.

            While being a healthcare worker as well as a being a frontline employee, stress  becomes an inevitable part of our job. In a linear regression study done by Tayyib & Alsolami (2020), the role of RNs have potentially exposed them infection and its associated consequences. This study was done to assess the physiological effects of fear, stress, and level of resilience in response to Covid-19 outbreaks. In this study, questionnaires were conducted during the outbreak including sociodemographic details, job related stress, and fear of infection; the data analyzed used descriptive correlation studies and linear regression studies.

In the study result, about 314 nurses (87%)  who responded to survey showed that the higher the outbreaks the higher the level of anxiety and stress during the outbreaks (mean 7.61, SD + 2.72); reporting high risk of being infected (mean 7.6, SD+ 2.72), causing stress at work (mean 6.92, SD + 2.91), and falling ill (mean 6.72, SD + 2.98).  The predictive factors included social media (0.76, p= 0.03), exposure to trauma prior to outbreak (-0.95, p=0.003), and readiness of care (-0.21, p=0.001). these factors have significant impact on an RN’s psychological status which may affect the quality of patient care.

The strength of this article is that it takes into account the important aspects that should be considered regarding RNs’ perceived high level of fears and stress are the coping measures that should be taken during and after this time to help alleviate post-traumatic stress and increase their emotional resilience. The weakness of this article includes the need for further longitudinal prospective studies to capture the large population and different time series recommended to validate this study further and provide a more thorough understanding of this issue. Furthermore, it would be more beneficial to research on factors that affect their level of stress and fears during such times. Supportive interventions need to be introduced to ensure resilience among staff and ensure quality patient care.

Reference(s)

Sathwick, S. (2020). What is a linear regression? Data Science. Retrieved from

Statistic Solution (2020). What is linear regression? Stat Solution Dissertation. Retrieved from