The FlemingHarrington test for rightcensored data based on counting processes
Description
The FHtestrcc
function performs a test for rightcensored data based on counting processes. It uses the Gρ,λ family of statistics for testing the differences of two or more survival curves.
Usage
1 2 3 4 
Arguments
L 
Numeric vector of the left endpoints of the censoring intervals (exact and rightcensored data are represented as intervals of [a,a] and (a, infinity) respectively). 
R 
Numeric vector of the right endpoints of the censoring intervals (exact and rightcensored data are represented as intervals of [a,a] and (a, infinity) respectively). 
group 
A vector denoting the group variable for which the test is desired. If 
rho 
A scalar parameter that controls the type of test (see details). 
lambda 
A scalar parameter that controls the type of test (see details). 
alternative 
Character giving the type of alternative hypothesis for twosample and trend tests: 
formula 
A formula with a numeric vector as response (which assumes no censoring) or 
data 
Data frame for variables in 
subset 
An optional vector specifying a subset of observations to be used. 
na.action 
A function that indicates what should happen if the data contain 
... 
Additional arguments. 
Details
The appropriate selection of the parameters rho
and lambda
gives emphasis to early, middle or late hazard differences. For instance, in a given clinical trial, if one would like to assess whether the effect of a treatment or therapy on the survival is stronger at the earlier phases of the therapy, we should choose lambda= 0
, with increasing values of rho
emphasizing stronger early differences. If there were a clinical reason to believe that the effect of the therapy would be more pronounced towards the middle or the end of the followup period, it would make sense to choose rho = lambda > 0
or rho = 0
respectively, with increasing values of lambda
emphasizing stronger middle or late differences. The choice of the weights has to be made prior to the examination of the data and taking into account that they should provide the greatest statistical power, which in turns depends on how it is believed the null is violated.
Value
information 
Full description of the test. 
data.name 
Description of data variables. 
n 
Number of observations in each group. 
obs 
The weighted observed number of events in each group. 
exp 
The weighted expected number of events in each group. 
statistic 
Either the chisquare or Z statistic. 
var 
The variance matrix of the test. 
alt.phrase 
Phrase used to describe the alternative hypothesis. 
pvalue 
pvalue associated with the alternative hypothesis. 
call 
The matched call. 
Author(s)
R. Oller and K. Langohr
References
Fleming, T. R. and Harrington, D. P. (2005). Counting Processes and Survival Analysis New York: Wiley.
Harrington, D. P. and Fleming, T. R. (1982). A class of rank test procedures for censored survival data. Biometrika 69, 553–566.
Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data. New York: Wiley, 2nd Edition.
Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data. New York: Wiley, 2nd Edition.
See Also
FHtestrcp
Examples
1 2 3 4 5 6 7 8 9 10 11  ## Twosample tests
FHtestrcc(Surv(futime, fustat)~rx, data=ovarian)
FHtestrcc(Surv(futime, fustat)~rx, data=ovarian, rho=1)
## Trend test
library(KMsurv)
data(bmt)
FHtestrcc(Surv(t2, d3)~group, data=bmt, rho=1, alternative="decreasing")
## Ksample test
FHtestrcc(Surv(t2, d3)~as.character(group), data=bmt, rho=1, lambda=1)

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