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Plot Regional Consistency Probability for Single-Arm MRCT (Count Endpoint)

Source: R/plot_rcp1armCount.R
plot_rcp1armCount.Rd

Generate a faceted plot of Regional Consistency Probability (RCP) as a function of the regional allocation proportion \(f_1\) for count endpoints (negative binomial model). Formula and simulation results are shown for Method 1 (log-RR and linear-RR scales) and Method 2. Facet rows correspond to the two Method 1 scales (\(\log(RR)\) and \(1 - RR\)), and facet columns correspond to total sample sizes specified in N_vec.

Regional sample sizes are allocated as: \(N_{j1} = \lfloor N \times f_1 \rfloor\) and \(N_{j2} = \cdots = N_{jJ} = (N - N_{j1}) / (J - 1)\).

Usage

plot_rcp1armCount(
  lambda = 2,
  lambda0 = 3,
  dispersion = 1,
  PI = 0.5,
  N_vec = c(20, 40, 100),
  J = 3,
  f1_seq = seq(0.1, 0.9, by = 0.1),
  nsim = 10000,
  seed = 1,
  base_size = 28
)

Arguments

lambda

Numeric scalar. Expected count per patient under the alternative hypothesis. Must be positive. Default is 2.

lambda0

Numeric scalar. Expected count per patient under the historical control. Must be positive. Default is 3.

dispersion

Numeric scalar. Dispersion parameter of the negative binomial distribution. Must be positive. Default is 1.

PI

Numeric scalar. Effect retention threshold for Method 1. Must be in \([0, 1]\). Default is 0.5.

N_vec

Integer vector. Total sample sizes for each facet column. Default is c(20, 40, 100).

J

Positive integer (>= 2). Number of regions. Default is 3.

f1_seq

Numeric vector. Sequence of Region 1 allocation proportions. Each value must be in \((0, 1)\). Default is seq(0.1, 0.9, by = 0.1).

nsim

Positive integer. Number of Monte Carlo iterations for simulation. Default is 10000.

seed

Non-negative integer. Random seed for simulation. Default is 1.

base_size

Positive numeric. Base font size in points passed to theme. Use larger values (e.g., 28) for presentation slides and smaller values (e.g., 11) for vignettes or reports. Default is 28.

Value

A ggplot2 object.

Examples

p <- plot_rcp1armCount(
  lambda     = 2,
  lambda0    = 3,
  dispersion = 1,
  PI         = 0.5,
  N_vec      = c(20, 40, 100),
  J          = 3
)
print(p)