medsanova {GFDsurv}

medSANOVA: Median survival analyis-of-variance

Description

The function medsanova calculates the Wald-type test statistic for inferring median survival differences in general factorial designs. Respective p-values are obtain by a \chi^2-approximation and a permutation approach.

Usage

medsanova(
  formula,
  event = "event",
  data = NULL,
  nperm = 1999,
  var_method = "twosided",
  var_level = 0.9,
  nested.levels.unique = FALSE
)

Arguments

formula

A model formula object. The left hand side contains the time variable and the right hand side contains the factor variables of interest. An interaction term must be specified.

event

The name of the censoring status indicator with values 0=censored and 1=uncensored. The default choice is "event"

data

A data.frame, list or environment containing the variables in formula and the censoring status indicator. Default option is NULL.

nperm

The number of permutations used for calculating the permuted p-value. The default option is 1999.

var_method

Method for the variance estimation of the sample medians. The default is the "one-sided" confidence interval approach. Additionally, the "two-sided" confidence interval approach can be used.

var_level

A number between 0 and 1 specifying the confidence level for the variance estimation method; the default value is 0.9.

nested.levels.unique

A logical specifying whether the levels of the nested factor(s) are labeled uniquely or not. Default is FALSE, i.e., the levels of the nested factor are the same for each level of the main factor.

Details

The medsanova function calculates the Wald-type statistic for median differences in general factorial survival designs. Crossed as well as hierachically nested designs are implemented. To estimate the sample medians' variances, a one-sided (resp. two-sided) confidence interval approach is used and the level of this confidence interval can be specified by var_level.

The medsanova function returns the test statistic as well as two corresponding p-values: the first is based on a \chi^2 approximation and the second one is based on a permutation procedure.

Value

An medsanova object containing the following components:

pvalues_stat

The p-values obtained by \chi^2-approximation

pvalues_per

The p-values of the permutation approach

statistics

The value of the Wald-type test statistic along with the degrees of freedom of the \chi^2-distribution and the respective p-value, as well as the p-value of the permutation procedure.

nperm

The number of permutations used for calculating the permuted p-value.

References

Ditzhaus, M., Dobler, D. and Pauly, M.(2020). Inferring median survival differences in general factorial designs via permutation tests. Statistical Methods in Medical Research. doi:10.1177/0962280220980784.

Examples

library("survival")
data(veteran)
out <- medsanova(formula ="time ~ trt*celltype",event = "status",
 data = veteran)

## Detailed informations:
summary(out)