D9 last milestone
nteasleyFollow all directions please
2 years ago
10
files (5)M1younger.docx
M3younger.docx
M2younger.docx
- M4younger.docx
- M9finaldirectionsyounger.pdf
M1younger.docx
Health Question
Precious Teasley
IHP-525-Q3469 Biostatistics 24TW3
Southern New Hampshire University
Professor Cecilia Younger
Does Age Affect the Survival (Follow-up Status) of MI Patients?
The question asks if age has an impact on the survival chances of MI patients. MI stands for Myocardial Infarction which is commonly referred to as heart attack (Institute of Medicine, 2010). When people grow older, their body organs become weaker, and their functionality drops. This implies that an older patient's heart is not as strong as a young person's and in case of a heart attack, the mortality rate for older people is higher. The long-term survival of patients aged 65 and above with acute myocardial infarction is 65% (Kappagoda & Greenwood, 2012). Age affects the survival of MI patients. Younger patients have greater survival rates than older ones. The younger population is generally healthier and stronger; therefore, they have better chances. Their immunity is also higher and this generally boosts their health (Morrow, 2016). On the contrary, the older population is weaker and has minimal chances of surviving a heart attack.
References
Institute of Medicine (2010). Cardiovascular disability: Updating the social security listings. The National Academies Press.
Kappagoda, C. & Greenwood, P. (2012). Long-term management of patients after myocardial infarction. Springer.
Morrow, D. (2016). Myocardial infarction: A companion to Braunwald’s heart disease. Elsevier Health Sciences.
M3younger.docx
The IHP 525 Milestone Three Assignment
Precious Teasley
Southern New Hampshire University
IHP-525-Q3469 Biostatistics 24TW3
Professor Cecilia Younger
March 28, 2024
The IHP 525 Milestone Three Assignment
Completed Table For Your Milestone Three Assignment
Question |
Answer |
What is your health (research) question? |
Does Age Affect the Survival (Follow-up Status) of MI Patients? |
What are the corresponding null and alternative hypotheses? |
Null hypothesis (H0): Age does not affect the survival (follow-up status) of MI patients. |
|
|
Alternative hypothesis (H1): Age affects the survival (follow-up status) of MI patients. |
List the descriptive statistics you will compute, using which |
For this analysis, we will compute descriptive statistics for the variable 'Age' to assess |
variable(s), to help answer your health question. |
its impact on survival rates. |
What is the name of the statistical test you will use to test |
We will use a statistical test called logistic regression to test the hypothesis and |
your hypothesis and answer your health question? |
understand the relationship between age and survival status among MI patients. |
What is the formula for your chosen statistical test? |
Logistic regression formula: Log(odds of survival) = β0 + β1(Age) |
Why is the statistical test you chose appropriate to answer |
Logistic regression is appropriate because it can model the probability of an outcome |
your health question? Be sure to be clear on how the two |
(survival status in this case) based on one or more predictor variables (age in our case) |
variables you described in Milestone Two are used to complete |
while accounting for potential confounding factors. Age is the main predictor variable |
this test. |
that we want to assess in relation to survival rates. |
Which graph(s) (histogram, stem and leaf, boxplot, bar graph, |
We will create a bar graph to visualize the survival rates (follow-up status) of MI |
scatterplot) will you use to visualize the answer to your health |
patients across different age groups. The x-axis will represent age groups, and the |
question? Be specific and include which variables will be used |
y-axis will show the percentage of patients in each age group who survived (follow-up |
and if the graph will be created for different subgroups of subjects. |
status = 1) or did not survive (follow-up status = 0). |
Explanation
The choice of statistical test and calculations aligns with the research question of whether age affects the survival (follow-up status) of MI patients. Logistic regression is appropriate because it allows us to model the relationship between a binary outcome variable (survival status) and one or more predictor variables (age in this case) while considering potential confounding factors. The logistic regression formula helps estimate the odds of survival based on age, providing valuable insights into how age impacts survival rates among MI patients.
Additionally, creating a bar graph to visualize survival rates across different age groups adds a graphical representation to our analysis. This graph will help stakeholders easily understand the relationship between age and survival status, making it easier to communicate the findings to non-technical decision-makers. Overall, these calculations and graphs are crucial in answering the health question comprehensively and providing evidence-based recommendations.
image1.jpeg
M2younger.docx
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
|
Information on data set to include in your description |
|
|
Which variables are you investigating? |
Length of stay in hospital by gender (los) |
|
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). |
|
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). |
|
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 |
Assess the collected data. Use this section to lay out the source, parameters, and any limitations of your data. Specifically, you should:
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.
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
|
|
n |
Mean |
Std. dev. |
Min |
Max |
Q1 |
Q3 |
IQR |
|
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.
