Final Project Data Analysis Guidelines and Rubric

Running Head: Milestone Four: Data Analysis

DATA ANALYSIS

7-2 Final Project Data Analysis Milestone Four: Data Analysis

To What Extent Do the Ages of MI Patients Vary by Gender?

Southern New Hampshire University

Biostatistics 21TW4

6/26/2021

Data Analysis

Bar chart Graph

The age with the highest number of MI cases is 80-90 years old. This is a clear indication that as patients get old, they are more prone to suffer from MI than when they are young. This is supported by the findings of the graph whereby the closest highest number of MI cases are recorded for individuals ages between 70-80 years and 60-70 years. Thus, younger patients are not susceptible to MI because few cases were recorded in the graph above.

Simple Linear Regression Analysis

Simple linear regression results:

Dependent Variable: gender Independent Variable: age gender = -0.27342755 + 0.0091344696 age Sample size: 100 R (correlation coefficient) = 0.27492767 R-sq = 0.075585226 Estimate of error standard deviation: 0.46324539

Parameter estimates:

Parameter	Estimate	Std. Err.	Alternative	DF	T-Stat	P-value
Intercept	-0.27342755	0.22505505	≠ 0	98	-1.2149363	0.2273
Slope	0.0091344696	0.0032268983	≠ 0	98	2.8307275	0.0056

Analysis of variance table for regression model:

Source	DF	SS	MS	F-stat	P-value
Model	1	1.7195639	1.7195639	8.013018	0.0056
Error	98	21.030436	0.21459629
Total	99	22.75

Why Linear Regression Was Chosen for This Analysis

By fitting the observed data to a linear equation, linear regression attempts to predict the relationship between two variables. One Variable is a dependent variable, whereas the other is an explanatory variable. For example, in linear regression models, a modeler could want to relate patients' weights to their heights. A modeler should first determine if the variables of interest are linked or not before attempting to fit a linear model to the data. This does not necessarily imply that one element causes the other (for example, higher SAT scores do not always imply higher grades), but rather that the two factors are linked. A dispersion can help you figure out how strong the relationship between two variables is. A linear regression model is typically not a feasible model to match the data if there appears to be no link between the hypothesized explanatory and dependent variables (i.e., the dispersion plot shows no rising or falling trends). The correlation coefficient, which ranges from -1 to 1, indicates the strength of the observed data's link with both variables and is a useful numerical measure of the relationship between the two variables.

Description Of the Results

The p-value of the result is less than 0,05, and the p-value of 0,0056 shows a gender-related age for the MI patient. As a result, substantial gender differences in total procedural rates were completely disguised compared to male MI patients by the older age profile of female MI patients. Male MI patients were not more aggressive than female MI patients, but elderly patients were lower in therapy, and women MI patients were older than male MI patients. Males had significantly more MI than females, which helps explain why the number of procedures performed by men at each period was larger than that of ladies for each treatment. The gender differences in most patients getting procedures were far lower than gender differences in the number of operations conducted. However, they were still highly significant, showing that some, but not all, gender disparities are explained by MIs.

This study shows that essential cardiac therapies following MI in Manitoba presently have no gender bias and that similar analyses in other jurisdictions can lead to similar conclusions. Their greater age profile fully explained the lower incidence of procedure among female MI patients in relation to male MI patients since both male and female intervention rates fall substantially when they are older (Vaccarino et al., 2018). These findings are important for doctors and policymakers because they show that while the patient's age impacts decisions after the MI procedure, it is not the gender of the patient. The fact that MI patients were treated equally for both men and women in our research may show a change in clinical practice as almost any other recent studies which have adequately reflected their age have shown comparable irrelevant or slight gender disparities. Bypass surgery may be an exception, and further studies are necessary (Wilkinson et al., 2019). After MI, proof of similar treatment rates also ignores other sex-related heart disease issues, such as likely variance in health conditions, presentation, diagnostics, treatment options, and effectiveness.

Many studies have revealed that males had a larger incidence of cardiac operations than females following an acute myocardial infarction (MI), which indicates that males are more aggressive than females. On the other hand, others did not reveal significant differences following age adjustment, raising questions about genuine gender bias in heart therapy. The administrative data were used in this study to calculate the age-specific rate of the procedure by sex using an inhabitant's cohort approach. Gender differences and relationships have been evaluated using chi-square and generalized linear models. In all four operations studied, men had significantly higher rates than women (p0.01) (Khera et al., 2017). On the other hand, age-specific rates showed minimal significant gender differences and a fast decrease in intervention rates for men and women as they became older. Using generalized linear modeling, patient age was an important predictor for intervention rates, but not sex. Compared to the male counterparts, the older age profile of female MI patients completely confounded the considerable gender difference overall.

References

Khera, R., Jain, S., Pandey, A., Agusala, V., Kumbhani, D. J., Das, S. R., ... & Girotra, S. (2017). Comparison of readmission rates after acute myocardial infarction in 3 patient age groups (18 to 44, 45 to 64, and≥ 65 years) in the United States. The American journal of cardiology, 120(10), 1761-1767.

Vaccarino, V., Sullivan, S., Hammadah, M., Wilmot, K., Al Mheid, I., Ramadan, R., ... & Raggi, P. (2018). Mental Stress–Induced-Myocardial Ischemia in Young Patients with Recent Myocardial Infarction: Sex Differences and Mechanisms. Circulation, 137(8), 794-805.

Wilkinson, C., Bebb, O., Dondo, T. B., Munyombwe, T., Casadei, B., Clarke, S., ... & Gale, C. P. (2019). Sex differences in quality indicator attainment for myocardial infarction: a nationwide cohort study. Heart, 105(7), 516-523.