Description Usage Arguments Details Value Author(s) References Examples
Compute a MRS model to compare the risk factors between a reference and a relative population.
1 | MRsurvival(time.ref, status.ref, cov.rel, data.rel, cox.ref, cov.ref, init, B)
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time.ref |
The column name of the data frame |
status.ref |
The column name of the data frame |
cov.rel |
The column(s) name(s) of the data frame declared in |
data.rel |
A data frame with the variables (columns) of the individuals (raw) of the relative sample. |
cox.ref |
The results of the Cox model performed in the reference sample, i.e an object obtained by the |
cov.ref |
The column(s) name(s) of the data frame |
init |
A vector with the same length than |
B |
The number of iterations of the bootstrap resampling. |
We proposed here an adaptation of a multiplicative-regression model for relative survival to study the heterogeneity of risk factors between two groups of patients. Estimation of parameters is based on partial likelihood maximization and Monte-Carlo simulations associated with bootstrap re-sampling yields to obtain the corresponding standard deviations. The expected hazard ratios are obtained by using a PH Cox model.
matrix.coef |
A matrix containing the parameters estimations at each of the B iterations. |
estim.coef |
A numerical vector containing the mean of the previous estimation |
lower95.coef |
A numerical vector containing the lower bounds of the 95% confidence intervals. |
upper95.coef |
A numerical vector containing the upper bounds of the 95% confidence intervals. |
Y. Foucher <Yohann.Foucher@univ-nantes.fr>
K. Trebern-Launay <katygre@yahoo.fr>
Andersen P, Borch-Johnsen K, Deckert T, Green A, Hougaard P, Keiding N, Kreiner S. A cox regression model for the relative mortality and its application to diabetes mellitus survival data. Biometrics Dec 1985; 41(4):921-932.
K. Trebern-Launay, M. Giral, J. Dantal and Y. Foucher. Comparison of the risk factors effects between two populations: two alternative approaches illustrated by the analysis of first and second kidney transplant recipients. BMC Med Res Methodol. 2013 Aug 6;13:102.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # import and attach both samples
data(FTR.data)
data(STR.data)
# We reduce the dimension to save time for this example (CRAN policies)
STR.data <- STR.data[1:100,]
# Compute the Cox model in the reference sample (FTR)
cox.FTR<-coxph(Surv(Tps.Evt, Evt)~ ageR2cl + sexeR, data=FTR.data)
summary(cox.FTR)
# Compute the multiplicative relative model (STR)
mrs.STR <- MRsurvival(time.ref="Tps.Evt", status.ref="Evt",
cov.rel=c("ageR2cl", "Tattente2cl"),
data.rel=STR.data, cox.ref=cox.FTR, cov.ref=c("ageR2cl", "sexeR"),
init=c(0,0), B=5)
# Of course, choose B>>5 for real applications
# The values at each iteration
mrs.STR$matrix.coef
# The parameters estimations (mean of the values)
mrs.STR$estim.coef
apply(mrs.STR$matrix.coef, FUN="mean", MARGIN=2)
# The 95% confidence intervals
cbind(mrs.STR$lower95.coef, mrs.STR$upper95.coef)
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