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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.
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Feedbackfor9-2FinalSubmissionofFinalProjectDataAnalysis-IHP-525-Q3469.pdf
Feedback for 9-2 Final Submission of Final Project Data Analysis
Submission Feedback
Overall Feedback
Rubric Name: IHP 525 Final Project Data Analysis Guidelines and Rubric
IHP 525 Final Project Data Analysis Guidelines and Rubric
Associated Learning Objectives
IHP-525-02-19TW3
Assessment Method: Score on Criteria - Introduction: Process
Required Performance: Proficient
Level Achieved: Proficient
IHP-525-02-19TW3
Assessment Method: Score on Criteria - Data Analysis: Graphs: Graph
Required Performance: Proficient
Level Achieved: Needs Improvement
IHP-525-02-19TW3
Assessment Method: Score on Criteria - Data Analysis: Test
Required Performance: Proficient
Level Achieved: Proficient
PT
Assignments View Feedback
Precious,
Nice start. It is unclear why the regression results are included in this final
submission. The tables are provided, but there is no explanation of the model or the
information in the tables. In addition, it is not mentioned in the section regarding
the best choice of test.
The graphs are not appropriate given the type of variable you are comparing. The
graphs from your previous submission were much better.
The t-test is fine. However, the discussion regarding the results are not consistent
with the information presented in the tables.
What is missing is the discussion of the key features and limitations.
Dr. Younger
IHP 525 Final Project Data Analysis Guidelines and Rubric Activity: 9-2 Final Submission of Final Project Data Analysis
Course: IHP-525-Q3469 Biostatistics 24TW3
Name: Precious Teasley
Total Score 72.499997 / 100
Criteria
Introduction: Health Question
Proficient 8 / 8
States overall health question in own words, capturing key elements of question while using language appropriate for a non-technical audience
Criterion Feedback
Excellent work stating your health question and summarizing the topic in accessible language.
Introduction: Data: Key Features
Not Evident 0 / 9
Does not describe key features of data set and assess how features affect analysis
Criterion Feedback
Be sure to include an explanation of the key variables, types of data being collected, and how features of the data (like outliers)
might affect the analyses.
Introduction: Data: Limitations
Not Evident 0 / 8
Does not analyze limitations of data set and how those affect findings
Criterion Feedback
Be sure to include specific examples of limitations of the key variables in the data set and how these affect your findings.
Remember that a limitation could be in the way data is collected (self-reported vs. measured). In week one, we learned that
“garbage in is garbage out.” So, data that isn’t accurate or reliable will not lend itself to accurate or reliable findings.
Introduction: Process
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IHP-525-02-19TW3
Assessment Method: Score on Criteria - Data Analysis: Best Choice
Required Performance: Proficient
Level Achieved: Exemplary
IHP-525-03-19TW3
Assessment Method: Score on Criteria - Introduction: Data: Key Features
Required Performance: Proficient
Level Achieved: Not Evident
IHP-525-03-19TW3
Assessment Method: Score on Criteria - Data Analysis: Analysis: Biostatistics
Required Performance: Proficient
Level Achieved: Needs Improvement
IHP-525-03-19TW3
Assessment Method: Score on Criteria - Data Analysis: Analysis: Statistical Inferences
Required Performance: Proficient
Level Achieved: Proficient
IHP-525-05-19TW3
Assessment Method: Score on Criteria - Introduction: Health Question
Required Performance: Proficient
Level Achieved: Proficient
IHP-525-05-19TW3
Assessment Method: Score on Criteria - Introduction: Data: Limitations
Required Performance: Proficient
Level Achieved: Not Evident
IHP-525-05-19TW3
Assessment Method: Score on Criteria - Conclusions: Findings
Required Performance: Proficient
Level Achieved: Needs Improvement
IHP-525-05-19TW3
Assessment Method: Score on Criteria - Conclusions: Recommendations
Required Performance: Proficient
Level Achieved: Exemplary
Score
126.87 / 175 - F
Feedback Date
May 1, 2024 1:33 PM
Assignment
9-2 Final Submission of Final Project Data Analysis
Proficient 8.099999 / 9
Proposes process of answering health question based on the data set provided
Criterion Feedback
Thank you for an explanation of the methods you chose to answer your health question. Although you use the correct statistical
test, the reasoning should be made clearer.
Data Analysis: Graphs: Graph
Needs Improvement 6.3 / 9
Creates a graph that gives a sense of the potential relationship between two variables that form the health question and discusses why this graph was selected over others, but graph is inappropriate, reasons are illogical, or response contains inaccuracies
Criterion Feedback
Be sure to review the type of graph you chose. Remember, the type of variables dictate the type of graph that is appropriate.
Data Analysis: Test
Proficient 8.099999 / 9
Conducts appropriate statistical test accurately to answer chosen health question
Criterion Feedback
Nice work running a statistical test to answer your health question!
Data Analysis: Best Choice
Exemplary 9 / 9
Meets “Proficient” criteria and makes cogent connections between the test and graph or data
Done
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Criterion Feedback
Thank you for the helpful explanation of why this test was an appropriate choice. Additionally, you have tied the results of the
graph to the test which helps the reader think through the analytic plan.
Data Analysis: Analysis: Biostatistics
Needs Improvement 6.3 / 9
Presents graph and statistical test results, including spreadsheet showing work or computer output, and explains what they mean, but response contains inaccuracies or omits key details
Criterion Feedback
Thank you for including the results of the test you ran. It appears there are some errors. It might help to return to Milestone Four to
review any feedback on your data analysis.
Data Analysis: Analysis: Statistical Inferences
Proficient 8.099999 / 9
Draws appropriate statistical inferences based on statistical hypothesis test results and graph and justifies response
Criterion Feedback
Nice work stating the conclusions of your test and graph and supporting these conclusions with p values, for example. Be sure to
use the descriptive statistics in your explanation to fully explain the results.
Conclusions: Findings
Needs Improvement 5.6 / 8
Assesses how findings help answer overall question, but does not use brief, non-technical language, or response contains inaccuracies
Criterion Feedback
Thank you for your explanation of how the findings answer your health question. Just remember that we want to summarize
findings in a way that is accessible even to people not in our own field.
Submission ID Submission(s) Turnitin® Similarity Date Submitted
42810252
milestone4TheIHP525FinalDataAnalysiscConclusion... (89.77 KB) 30 % Apr 25, 2024 11:34 AM
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Conclusions: Recommendations
Exemplary 8 / 8
Meets “Proficient” criteria and recommendations including what additional information would help better answer question
Criterion Feedback
Thank you for making clear recommendations for future research that is needed and specifically what additional information you
would collect to improve the current analysis.
Articulation of Response
Exemplary 5 / 5
Submission is free of errors related to citations, grammar, spelling, syntax, and organization and is presented in a professional and easy to read format
Criterion Feedback
Nice work presenting your final analysis! This was a joy to read.
Total Score 72.499997 / 100
Overall Score
Points earned out of 100
71 points minimum
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