milestone 4

The IHP 525 Milestone Two Assignment

Precious Teasley

Southern New Hampshire University

IHP-525-Q3469 Biostatistics 24TW3

Professor Cecilia Younger

March 12, 2024

The IHP 525 Milestone Two Assignment

Question: Does Age Affect the Survival (Follow-up Status) of MI Patients?

The IHP 525 Milestone Two Table

A. Assess the collected data. Use this section to lay out the source, parameters, and any limitations of your data. Specifically, you should:

The dataset under analysis originates from the esteemed Worchester Heart Attack Study (Kappagoda & Greenwood, 2012), which offers a comprehensive insight into myocardial infarction cases. Spanning across a period of 13 years from 1975 to 2001, this dataset encompasses 100 meticulously gathered observations, each encapsulating crucial variables pertinent to the study. Situated within the confines of hospitals in the Worcester, Massachusetts, Standard Metropolitan Statistical Area, this dataset provides a rich reservoir of information regarding patients' experiences with myocardial infarctions (Kappagoda & Greenwood, 2012).

For the purpose of this individual analysis, two key variables were selected for scrutiny: gender and length of stay. Gender, a fundamental aspect of human biology, was dichotomously categorized into male (coded as 0) and female (coded as 1). Meanwhile, the length of stay variable (coded as los) denotes the duration for which an individual remained hospitalized post-myocardial infarction. It is worth noting that the examination revealed a noteworthy discrepancy in the standard deviation between males and females, as evidenced in Table 2. Additionally, the minimum and maximum length of stay values, reflective of the extremes within each gender group, exhibited a significant disparity, as delineated in Table 2.. This is where you want to say where the data came from analysis and description of the sample and how the data was collected. Next, define each of your variables what do they measure about the subjects? Then describe the distribution of each of your variables using the descriptive statistics you computed. Be sure to assess how these features affect your analysis.

2. Analysis of data limitations

Despite the invaluable insights provided by the dataset, several limitations warrant acknowledgment. Chief among these limitations is the relatively modest sample size comprising only 100 participants. A larger sample size would undoubtedly yield a more robust understanding of the gender-related disparities in hospital stays post-myocardial infarction. Furthermore, the classification of participants based on gender introduces an inherent imbalance, with 35 female participants compared to 65 male participants, potentially skewing the resultant analyses. Notably absent from the dataset is a variable accounting for complications encountered during the hospital stay, a factor that could significantly influence the duration of hospitalization post-myocardial infarction (Gerstman, 2015). Inclusion of such data would offer invaluable insights, particularly from a nursing perspective, facilitating a more nuanced understanding of the factors contributing to prolonged hospital stays in this context.

Summary Statistics Calculations

Table 2: Descriptive of gender by length of stay

References

Gerstman, B. B. (2015). Basic biostatistics: Statistics for public health practice (2nd ed.). Jones

Kappagoda, C. & Greenwood, P. (2012). Long-term management of patients after myocardial infarction. Springer.