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Based on the initial question: Cox Baseline Hazard

I would like to plot survival curves by gender for this model using the kidney dataset. Following the example codes, I subset the dataset by gender, and have 2 separate hazard rate for each gender. But when I compare my plot of exp(-h0_male) and exp(-n0_female), with survfit.plot, they are very different. Please explain.

fit   <- coxph(Surv(time, status)~sex, data=kidney)
df1   <- subset(kidney, sex==1)
df2   <- subset(kidney, sex==2)
tab_1 <- data.frame(table(df1[df1$status == 1, "time"])) 
x_1   <- as.numeric(levels(tab_1[, 1]))[tab_1[, 1]] 
d_1   <- tab_1[, 2] 
tab_2 <- data.frame(table(df2[df2$status == 1, "time"])) 
x_2   <- as.numeric(levels(tab_2[, 1]))[tab_2[, 1]] 
d_2   <- tab_2[, 2] 

##-- Male --
h0_male <- rep(NA, length(x_1))
for(l in 1:length(x_1)) {
   h0_male[l] <- d_1[l] / sum(exp(df1[df1$time >= x_1[l], "sex"] * betaHat))
}

##-- Female --
h0_female <- rep(NA, length(x_2))
for(l in 1:length(x_2)) {
   h0_female[l] <- d_2[l] / sum(exp(df2[df2$time >= x_2[l], "sex"] * betaHat))
}

enter image description here

enter image description here

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  • $\begingroup$ My guess is that when you plot from coxph(), you assume proportional hazards. Hence, the plot obtained by survfit() is with the survival curves under the proportional hazards constraint. When you split the data frame (which is the same as using +strata() in coxph) you obtain unconstrained estimates of the hazards. From what I see you are doing a sort of mix of both in calculating h0male and h0female. What is betaHat? $\endgroup$ – Theodor Mar 1 '16 at 10:47

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