Carry out log rank test

Compute log rank test statistic on an endpoint.

Refer to this vignette for more information and examples.

Usage

fitLogrank(formula, placebo, data, alternative, ..., tidy = TRUE)

Arguments

formula

An object of class formula that can be used with survival::survdiff. Must consist arm and endpoint in data. No covariate is allowed. Stratification variables are supported and can be added using strata(...).

placebo

character. String of placebo in data$arm.

data

data frame. Usually it is a locked data.

alternative

a character string specifying the alternative hypothesis, must be one of "greater" or "less", i.e., one-sided test is enforced. No default value. "greater" means superiority of treatment over placebo is established by an hazard ratio greater than 1.

...

subset condition that is compatible with dplyr::filter. The log rank test (survival::survdiff) is carried out on this subset only. This argument could be useful to create a subset of data for analysis when a trial consists of more than two arms. By default it is not specified, all data will be used to fit the model. More than one conditions can be specified in ..., e.g., fitLogrank(formula, data, arm %in% c('pbo', 'low dose'), x > 0.5), which is equivalent to fitLogrank(formula, data, arm %in% c('pbo', 'low dose') & x > 0.5). Note that if more than one treatment arm are present in the data after applying filter in ..., models are fitted for placebo verse each of the treatment arms.

tidy

logical. FALSE if more information are returned. Default TRUE.

Value

a data frame with columns:

arm

name of the treatment arm.

placebo

name of the placebo arm.

p

one-sided p-value for log-rank test (treated vs placebo).

info

the number of events of the endpoint in the subset.

z

the z statistic of the log rank test, with the sign of the log hazard ratio of treatment vs placebo.

If the statistic is undefined because its variance is zero (e.g., no informative event comparison in the subset), a simulation placeholder z = 0 with the corresponding p = 0.5 is returned with a warning rather than an error, so that a few such replicates in a large simulation do not require error handling in action functions. The placeholder carries no evidence either way and is not a standardized normal statistic.