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## Survival Analysis assignment Project

### Assignment Instructions

##### Assignment ID: FG133135143

Project – Survival Analysis assignment

Assessment Description

It is important to have experience analyzing data where time to a given event is the dependent variable. The task will be to compare the survival distribution between groups, estimate the survival distribution, and model the effect of explanatory variables on the outcome variable. Then, differentiate between the use of the Poisson distribution to estimate the rate of event occurrence and survival analysis to estimate time lapse between events.

For this activity, perform the following:

Access the “Echocardiogram Data Set,” located in the topic Resources.

Using the topic Resources, familiarize yourself with the differences between survival analysis and the Poisson distribution, with an emphasis on parameter estimation (e.g. λ = E(X)).

Explore the data, perform initial descriptive statistics tests, and explain and visualize your findings. Use the kable() function to visualize the data.

Explore the following R packages: devtools, survival, tidyverse, survminer, survival, and load them into your project.

Formulate a question that can be answered by performing survival analysis.

Calculate the Kaplan-Meier Estimator:

1. Explore the Surv() function and build a Surv object using the response variable.
2. Print the Surv object you generated.
3. Analyze the data with the survfit()

4. Genereate a KM curve.

Visualize Survival Data:

1. Familiarize yourself with ggsurvplot().
2. Create a detailed, visually appealing plot of the data using ggsurvplot(fit = yourSurvObject, data =…., etc.,)

Generate K-M Curves with the Predictor:

1. Stratify the analysis into standard vs experimental, using survfit().
2. Display the strata using summary().
3. Plot the strata using ggsurvplot().
4. Plot the overall survival, stratified by the appropriate variable.

Perform Log-Rank Analysis:

1. Familiarize yourself with the survdiff()
2. Test the difference between survival curves using survdiff(Surv(time = )).
3. Examine and interpret the log-rank output.
4. Plot the K-M curve and include the log-rank test.

Question 1: Were you able to answer the question you formulated? If yes, state the answer and explain. If no, continue the analysis until you can answer the question.

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