hr.comp.meta {survcomp} | R Documentation |
This function compares two lists of hazard ratios computed from the same survival data by using the function hazard.ratio
. The statistical test is a Student t test for dependent samples.
hr.comp.meta(list.hr1, list.hr2, hetero = FALSE)
list.hr1 |
first list of D indices as returned by the hazard.ratio function. |
list.hr2 |
second list of D indices as returned by the hazard.ratio function. |
hetero |
if TRUE, a random effect model is use to compute the meta-estimators. Otherwise a fixed effect model is used. |
In meta-analysis, we estimate the statistic of interest in several independent datasets. It results a list of estimates such as list of hazard ratios. The two lists of hazrd ratios must be computed from the same samples (and corresponding survival data). The function computes a meta-estimator for the correlations between the two scores and uses a Student t test for dependent samples.
p.value |
p-value from the Student t test for the comparison hr1 > hr2. |
hr1 |
meta-estimator of the first D index. |
hr2 |
meta-estimator of the second D index. |
Benjamin Haibe-Kains
Cochrane, W. G. (1954) "The combination of estimates from different experiments", Biometrics, 10, pages 101–129.
Haibe-Kains, B. and Desmedt, C. and Sotiriou, C. and Bontempi, G. (2008) "A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?", Bioinformatics, 24, 19, pages 2200–2208.
#first dataset set.seed(12345) age <- rnorm(100, 50, 10) size <- rexp(100,1) stime <- rexp(100) cens <- runif(100,.5,2) sevent <- as.numeric(stime <= cens) stime <- pmin(stime, cens) h1.1 <- hazard.ratio(x=age, surv.time=stime, surv.event=sevent) h2.1 <- hazard.ratio(x=size, surv.time=stime, surv.event=sevent) #second dataset set.seed(54321) age <- rnorm(110, 53, 10) size <- rexp(110,1.1) stime <- rexp(110) cens <- runif(110,.55,2) sevent <- as.numeric(stime <= cens) stime <- pmin(stime, cens) h1.2 <- hazard.ratio(x=age, surv.time=stime, surv.event=sevent) h2.2 <- hazard.ratio(x=size, surv.time=stime, surv.event=sevent) hr.comp.meta(list.hr1=list("hr.age1"=h1.1, "hr.age2"=h1.2), list.hr2=list("hr.size1"=h2.1, "hr.size2"=h2.2))