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QUESTION
critical thinking assignment
Module 000: Critical Thinking Assignment
1) Using the Survival Curve dataset tab located in the Framingham Heart Study dataset,
2) perform a Cox Proportional Hazards Regression Analysis to determine the survival time (risk of dying) for the Survival Curve data where the patients are divided into treatment groups.
3) Upload the Excel spreadsheet into R Studio or perform the Cox Proportional Hazards Regression Analysis in Excel.
H0 The risk of dying is not related to the patient treatment group. (Null Hypothesis)
H1 The risk of dying is related to the patient treatment group. (Alternative Hypothesis)
4) Present your findings as a Survival Time chart in a Word document, with following:
a) introduction explaining why you would conduct a survival analysis.
b) a discussion where you interpret the meaning of the survival analysis.
c) conclusion should be included.
d) Your submission should be 3 pages to discuss and display your findings.
5) Provide support for your statements with in-text citations from a minimum of 5 scholarly, peer-reviewed articles.
6) The Saudi Digital Library is a good place to find these sources and should be your primary resource for conducting research
| Subject | Nursing | Pages | 5 | Style | APA |
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Answer
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Cox Proportional Hazards Regression Analysis
Introduction
There are several clinical situations where known quantities or covariates potentially influence patient prognosis, a phenomenal concern which has evoked incessant research in the health discipline with the evolution of immune diseases and has inclined the importance of survival analysis (Crowther, & Lambert, 2017). Makar, et al. (2020) further examine the importance of survival analysis with regards to cancer, based on treatments. The need to predict prognosis based on initial conditions and measurable covariates has been enhanced by such models, hence eventually enhancing outcome-based healthcare practice and general improvement in management of disease, especially in the case of terminal lifestyle diseases (Huebner, et al., 2017). To examine the length of time patients take before the event of death based on treatments, survival analysis is used in this paper. Intrinsically, the cox proportional-hazards model is used to explore the association between prognosis factors and death risk, as it is advantageous over the Kaplan-Meier method in that it allows for analysis of risk effects of the factors (Lee, & Lee, 2017). This paper presents a comparison of survival between patients on chemotherapy and those on placebo treatments over a period of 3.5 years.
Data Analysis
The Kaplan Meier survival time chart is as shown below
The cox proportional regression analysis results are as in the outputs below.
The tests that check the overall model significance, the C-test shows us that two-thirds of the model is concordant, while the log-rank test shows that there is no significant difference in survival between the two treatment groups (chemotherapy and placebo) as the null hypothesis of no difference was not rejected.
The summary of the model is presented in the table below:
The results indicate that the patients on placebo treatment have a hazard ratio of 3.42, implying that at any given time, a patient on placebo treatment is 3.42 times as (242% more) likely to die as (than) their counterparts on chemo therapy. Further, there is 95% confidence that those patients on placebo will have a likelihood of dying ranging from 0.79 to 14.84 as compared to those patients on chemotherapy.
Additionally, the exponentiated negative coefficient (0.29) means that the patients on chemotherapy have about 0.3 times chance of dying as compared to their counterparts on placebo treatment.
The survival probability comparison plot between the two treatment groups is presented below.
However, there is no significance, at 95% confidence level, of association between treatment and mortality (p-value>0.05) in the univariate cox proportional regression model Khanal, Sreenivas, & Acharya, 2018).
Conclusion
In this study, the null hypothesis is not rejected at 95% confidence level. Therefore, there is sufficient evidence in the data to conclude that the risk of dying is not related to the patient treatment group.
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References
Crowther, M. J., & Lambert, P. C. (2017). Parametric multistate survival models: flexible modelling allowing transition‐specific distributions with application to estimating clinically useful measures of effect differences. Statistics in medicine, 36(29), 4719-4742.
Huebner, M., Wolkewitz, M., Enriquez-Sarano, M., & Schumacher, M. (2017). Competing risks need to be considered in survival analysis models for cardiovascular outcomes. The Journal of Thoracic and Cardiovascular Surgery, 153(6), 1427-1431.
Khanal, S. P., Sreenivas, V., & Acharya, S. K. (2018). Cox Proportional Hazards Model for Identification of the Prognostic Factors in the Survival of Acute Liver Failure Patients in India. Nepalese Journal of Statistics, 2, 53-74.
Lee, T., & Lee, M. (2017). Analysis of stage III proximal colon cancer using the Cox proportional hazards model. Journal of the Korean Data and Information Science Society, 28(2), 349-359.
Makar, G. S., Makar, M., Obinero, C., Davis, W., Gaughan, J. P., & Kwiatt, M. (2020). Refusal of cancer-directed surgery in patients with colon cancer: risk factors of refusal and survival data. Annals of surgical oncology, 1-11.