copsanova {GFDsurv}

copSANOVA: concordance parameter survival analyis-of-variance

Description

The function copanova calculates the ANOVA-rank-type statistic for general factorial survival designs based on the (extended) concordance parameter. The respective p-value is obtained by a multiplier bootstrap approach.

Usage

copsanova(
  formula,
  event = "event",
  data = NULL,
  BSiter = 1999,
  weights = "pois",
  tau = NULL,
  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 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.

BSiter

The number of bootstrap iterations; the default is 1999.

weights

Character to specify the multiplier bootstrap approach. Either a wild bootstrap with centred Poisson ("pois", default) or standard normal ("norm") weights, or the weird bootstrap ("weird") can be chosen. Moreover, both wild bootstrap strategies can be selected with a correcting factor for liberality by "corrLibPois" and "corrLibNorm".

tau

The truncation time specifying the end of the relevant time window for the analysis. By default (NULL), the smallest 95%-quantile of the times per group is chosen.

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 copsanova function calculates the ANOVA-rank-type statistic for general factorial survival designs based on the (extended) concordance parameter. Crossed as well as hierachically nested designs are implemented. The p-value is determined by a multiplier bootstrap approach. Here, a wild bootstrap with/without correcting factors for liberal tests or the weird bootstrap of Andersen et al. (1993) can be chosen. The concrete analysis is done on the time window [0,tau], where tau need to be chosen equal to (default) or smaller than the smallest out of the largest possible censoring times per group.

The copsanova function returns the test statistic as well as a corresponding p-value based on a the specified multiplier procedure.

Value

An copsanova object containing the following components:

statistics

The value of the copsanova along with the p-value of the specified multiplier bootstrap.

Bsiter

The number of bootstrap iterations.

weights

The chosen multiplier bootstrap method.

tau

The chosen truncation time specifying the end of the relevant time window for the analysis.

References

Dobler, D. and Pauly, M. (2020). Factorial analyses of treatment effects under independent right-censoring. Statistical Methods in Medical Research 29(2), 325-343. doi:10.1177/0962280219831316.

Examples

library(condSURV)
data(colonCS)
out <- copsanova(formula ="Stime ~ rx*sex",event = "event",
                 data = colonCS, BSiter = 99)

##Detailed informations:
summary(out)