valorate (version 1.0-5)

valorate.survdiff: ESTIMATES THE P-VALUE AND STATISTICS OF THE LOG-RANK TEST

Description

Estimates the p-value using the VALORATE calculation.

Usage

valorate.survdiff(vro, clusters, p.func)

Value

the estimated p-value.

Arguments

vro

the valorate object.

clusters

a numerical or logical vector representing the two survival groups encoded in 1/TRUE for those 'mutated' (in the group of interest) or 0/FALSE for those who not. Basically this value is the 'x' vector in the VALORATE re-formulation. See references.

p.func

the function that provides the estimation. The default is valorate.p.value.sampling. See valorate.p.value.

Author

Victor Trevino

Details

The main function to estimate the p-value of the difference of two survival curves under the VALORATE algorithm. Because the definition of the survival group as '1' or '0' is arbitrary, the actual calculation is performed over the less frequent group. From clusters and s=sum(clusters), n1 is determined as the min(s, length(clusters)-s). Then a call for prepare.n1 is performed and finally the p-value estimations are made. The statistics are added as attributes (LR:log-rank statistic, Z:z-normalized log-rank statistic, X2:X2-normalized log-rank statistic, LR.unweigthed: raw log-rank statistic which can be different to LR if a weighting scheme was used, pZ, pX2: corresponding p-values using the Z and X2 distribution, and var: variance).

References

Trevino et al. 2016 https://bioinformatics.mx/index.php/bioinfo-tools/

See Also

new.valorate. valorate.p.value. valorate.plot.empirical.

Examples

## Create a random population of 100 subjects 
## having 20 events
subjects <- numeric(100)
subjects[sample(100,20)] <- 1
vo <- new.valorate(rank=subjects, sampling.size=100000, verbose=TRUE)

groups <- numeric(100)
groups[sample(100,4)] <- 1  # only 4 subjects are within the 'mutated' group
pvr <- valorate.survdiff(vo, groups) 
print(pvr)