STAT 405 Biostatistics ~ Assignment #9 (102 points) – 1. Mayo Clinic Lung Cancer Data Description: Survival in patients with lung cancer at Mayo Clinic. Karnofsky performance scores rate how well the patient can perform usual daily activities Variable Names: (DO NOT USE IN YOUR ANALYSIS) inst: Institution code time: Survival time in days status: censoring status 0=censored, 1=dead age: Age in years sex: Male=1 Female=2 ph.ecog: ECOG performance trial (0=good 5=dead) ph.karno: Karnofsky performance score (bad=0-good=100) rated by physician pat.karno: Karnofsky performance score rated by patient meal.cal: Calories consumed at wt.loss: Weight loss in last six months Download Lung.txt from the course website and then it into R. Then do source the following to make sex a factor variable and fix the censoring variable so censor = 0 and dead = 1. locate the Lung.txt file in your directory. > source(file.choose()) > Lung$sex = as.factor(Lung$sex) > Lung$status = Lung$status 1 – a) Perform a test so see if the survival experience differs across gender. Obtain and plot the Kaplan-Meier estimates for both genders. Discuss. (4 pts.) Fit a using b) age, sex, ph.ecog, ph.karno, pat.karno, meal.cal, and Briefly summarize the model (don’t compute HR’s), which variables wt.loss. appear to be significant? (4 pts.) c) Test proportional hazards assumptions for the significant covariates (p < .10). Which variables appear to have a violation of proportional hazards assumption? Include plots from the function in support of the p-values from the PH test. cox.zph (4 pts.)
STAT 405 – Biostatistics ~ Assignment #9 (102 points) 1. Mayo Clinic Lung Cancer Data Description: Survival in patients with lung cancer at Mayo Clinic. Karnofsky performance scores rate how well the patient can perform usual daily activities Variable Names: inst: Institution code (DO NOT USE IN YOUR ANALYSIS) time: Survival time in days status: censoring status 0=censored, 1=dead age: Age in years sex: Male=1 Female=2 ph.ecog: ECOG performance (0=good 5=dead) ph.karno: Karnofsky performance score (bad=0-good=100) rated by physician pat.karno: Karnofsky performance score rated by patient meal.cal: Calories consumed at wt.loss: Weight loss in last six months Download Lung.txt from the course website and then source it into R. Then do the following to make sex a factor variable and fix the censoring variable so censor = 0 and dead = 1. > source(file.choose()) locate the Lung.txt file in your directory. > Lung$sex = as.factor(Lung$sex) > Lung$status = Lung$status – 1 a) Perform a test so see if the survival experience differs across gender. Obtain and plot the Kaplan-Meier estimates for both genders. Discuss. (4 pts.) b) Fit a using age, sex, ph.ecog, ph.karno, pat.karno, meal.cal, and wt.loss. Briefly summarize the model (don’t compute HR’s), which variables appear to be significant? (4 pts.) c) Test proportional hazards assumptions for the significant covariates (p < .10). Which variables appear to have a violation of proportional hazards assumption? Include plots from the cox.zph function in support of the p-values from the PH test. (4 pts.) trial
Biostatistics%20Assignment%209%20(F12).docx