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
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Information on data set to include in your description |
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Which variables are you investigating? |
Length of stay in hospital by gender (los) |
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Identify each variable as continuous/quantitative or categorical, and list the descriptive statistics that are used to describe that type of variable. |
Definite is gender, where incessant/quantitative is length of stay (los). For gender we will compute: N Mean Std. dev Min Max Q1 Q3 IQR Then we will compute them again by length of stay (los). |
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Compute these descriptive statistics for the variables you are investigating and present them here or in a separate table below. |
Descriptive statistics for the variables are presented in table 2 below. Created in StatCrunch (2014). |
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What does each statistic tell you about the data for the given variable? |
N – Represents the number of observations Mean – Represent the arithmetic average of the data Std. dev – Represent a measure of dispersion of the data Min- Represent the minimum days spent in the hospital Max- Represent the maximum days spent in the hospital Q1- Represent quartile is the first 25% of the Data Q3 – Represent quartile is the last 25% of the data as the three quartiles below it contains 75% of the data IQR – Represent the middle 50% of the data |
A. Assess the collected data. Use this section to lay out the source, parameters, and any limitations of your data. Specifically, you should:
1. Description of the key features of your data set
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
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n |
Mean |
Std. dev. |
Min |
Max |
Q1 |
Q3 |
IQR |
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All Participants |
100 |
0.35 |
0.48 |
0 |
1 |
0 |
1 |
1 |
|
Males |
65 |
6.32 |
3.34 |
1 |
17 |
4 |
7 |
3 |
|
Females |
35 |
7.8 |
8.92 |
3 |
56 |
4 |
8 |
4 |
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.