Coursework Two for APH415 Survival Analysis
Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit
Coursework Two for APH415 Survival Analysis
Abstract
This is the abstract of the project report
1. Introduction
Lung adenocarcinoma (LUAD) represents a major subtype of non-small cell lung cancer and remains a leading cause of cancer-related mortality worldwide …
2. Methods
2.1. Data Source and Study Population
The analysis utilized the GSE72094 dataset…
2.2. Variables
Main (Secondary) outcome; Predictors…
2.3. Statistical Analysis
Descriptive statistics; Survival models; Diagnostic tools, etc.
All analyses were performed using R software (version x.x.x) with the xxx packages.
3. Results
3.1. Patient Characteristics
Summarize key findings from Table 1. The cohort comprised xxx patients with a mean age of xxx years
Table 1: Participant demographic and clinical characteristics.
|
XXX |
XX |
XX |
XX |
XX |
XX |
|
Age, n(%) |
|
<0.001 |
Differentiation, n(%) |
|
<0.001 |
|
<60 years |
xx (xx) |
|
Well or moderately |
xx (xx) |
|
|
>60 years |
xx (xx) |
|
poor or undifferentiated |
xx (xx) |
|
|
Gender, n(%) |
|
0.3 |
Vascular invasion, n(%) |
|
<0.001 |
|
Male |
xx (xx) |
|
Positive |
xx (xx) |
|
|
Female |
xx (xx) |
|
Negative |
xx (xx) |
|
|
Tstage, n(%) |
|
<0.001 |
Perineural invasion, n(%) |
|
<0.001 |
|
T1/T2 |
xx (xx) |
|
Positive |
xx (xx) |
|
|
T3/T4 |
xx (xx) |
|
Negative |
xx (xx) |
|
|
Differentiation, n(%) |
|
<0.001 |
Score 3 |
xx (xx) |
|
Figure 1: xxxxx
3.2. Survival Analysis Results
Describe the findings from the analysis…
3.3. Model Diagnostics
Report diagnostic results. E.g., "Testing of xxx provided no strong evidence …
4. Discussion and Conclusion
This study identified xxx and xxx as significant independent prognostic factors, …
In conclusion, this survival analysis confirms …
References
In APA style (italic in journal, volume (issue): number), e.g.,
Song, Z., Zhang, Y., Chen, Z., and Zhang, B. (2021). Identification of key genes in lung adenocarcinoma based on a competing endogenous RNA network. Oncology Letters, 21(1):60.
2025-11-05