R survival analysis : surv_pvalue vs fit.coxph for log-rank-test pvalue
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Entering edit mode
2.6 years ago
ZheFrench ▴ 460

I have different value for test$pval and and other.pval that should return the same log-rank test p-value, no ? surv_object <- Surv(time = as.numeric(final_group_annotated[,time]), event = final_group_annotated[,opt$endPoint] )it1 <- survfit(surv_object ~ group, data = final_group_annotated)

test=surv_pvalue(fit1, final_group_annotated)
print(test$pval)  0.3427874 fit.coxph <- coxph(surv_object ~ group, data = final_group_annotated) print(summary(fit.coxph)) coxph(formula = surv_object ~ group, data = final_group_annotated) n= 188, number of events= 30 coef exp(coef) se(coef) z Pr(>|z|) groupgroup2 -0.3482 0.7060 0.3689 -0.944 0.345 exp(coef) exp(-coef) lower .95 upper .95 groupgroup2 0.706 1.417 0.3426 1.455 Concordance= 0.538 (se = 0.049 ) Likelihood ratio test= 0.87 on 1 df, p=0.4 Wald test = 0.89 on 1 df, p=0.3 Score (logrank) test = 0.9 on 1 df, p=0.3 other.pval <- coef(summary(fit.coxph))[,5] print(other.pval)  0.3452066 survival analysis R • 4.8k views ADD COMMENT 6 Entering edit mode 2.6 years ago The p-value returned by Surv() is the log-rank p-value from the score test; so, you should be comparing this to the log-rank p-value from the Cox PH model. I went over this recently in this thread: A: survfit(Surv()) P-value interpretation for 3 survival curves? ## ------------------------------------ The way that you are doing it, i.e.: coef(summary(fit.coxph))[,5]  This will return multiple p-values when there are multiple strata, one p-value representing a single stratum / coefficient compared to the reference stratum. To extract the log-rank p-value for the overall model, you need to use: summary(coxfit)$sctest[3]


Kevin