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

Source: R/mutze_test.R
mutze_test.Rd

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 class 'mutze_test'
print(x, ...)

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).

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.

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)
#> Mutze Test Results
#> ==================
#> 
#> Method:     Poisson Wald (fallback) 
#> Estimate:   -0.0933
#> SE:         0.4859
#> Z:          -0.1921
#> p-value:    0.4238
#> Rate Ratio: 0.9109
#> CI (95%):  [0.3515, 2.3608]
#> Dispersion: Inf
#> 
#> Group Summary:
#>     treatment subjects events exposure
#>  Experimental       20      8 21.02991
#>       Control       20      9 21.55013