gsDesignNB (version 0.2.6)

mutze_test: Wald test for treatment effect using negative binomial model (Mutze et al.)

Description

Fits a negative binomial (or Poisson) log-rate model to the aggregated subject-level data produced by cut_data_by_date(). The method matches the Wald test described by Mutze et al. (2019) for comparing treatment arms with recurrent event outcomes.

Usage

mutze_test(
  data,
  method = c("nb", "poisson"),
  conf_level = 0.95,
  sided = 1,
  poisson_threshold = 1000
)
# S3 method for mutze_test
print(x, ...)

Value

An object of class mutze_test containing the fitted model summary with elements:

• method: A string indicating the test method used.

• estimate: log rate ratio (experimental vs control).

• se: standard error for the log rate ratio.

• z: Wald statistic.

• p_value: one-sided or two-sided p-value.

• rate_ratio: estimated rate ratio and its confidence interval.

• dispersion: estimated dispersion (theta) when method = "nb".

• group_summary: observed subjects/events/exposure per treatment.

Invisibly returns the input object.

Arguments

data

A data frame with at least the columns treatment, events, and tte (follow-up time). Typically output from cut_data_by_date().

method

Type of model to fit: "nb" (default) uses a negative binomial GLM via MASS::glm.nb(), "poisson" fits a Poisson GLM.

conf_level

Confidence level for the rate ratio interval. Default 0.95.

sided

Number of sides for the test: 1 (default) or 2.

poisson_threshold

When method = "nb", the model falls back to Poisson regression if theta (the NB shape parameter) is outside the range [1/poisson_threshold, poisson_threshold]. Very large theta indicates near-Poisson data, while very small theta indicates extreme overdispersion with unstable estimates. Default is 1000.

x

An object of class mutze_test.

...

Additional arguments (currently ignored).

Methods (by generic)

• print(mutze_test): Print method for mutze_test objects.

Examples

enroll_rate <- data.frame(rate = 20 / (5 / 12), duration = 5 / 12)
fail_rate <- data.frame(treatment = c("Control", "Experimental"), rate = c(0.5, 0.3))
dropout_rate <- data.frame(
  treatment = c("Control", "Experimental"),
  rate = c(0.1, 0.05), duration = c(100, 100)
)
sim <- nb_sim(enroll_rate, fail_rate, dropout_rate, max_followup = 2, n = 40)
cut <- cut_data_by_date(sim, cut_date = 1.5)
mutze_test(cut)