computeCvPoissonRegression: Compute critical values for Poisson regression data

In EmpiricalCalibration: Routines for Performing Empirical Calibration of Observational Study Estimates

Compute critical values for Poisson regression data

Description

Obtains critical values for the group and continuous sequential MaxSPRT test with Poisson regression data, using a Wald type upper boundary, which is flat with respect to the likelihood ratio function, and with a pre-specified upper limit on the sample size.

It is often not possible to select a critical value that corresponds to the exact alpha specified. Instead, this function will select the least conservative critical value having an alpha below the one specified, so the sequential analysis is conservative.

Usage

computeCvPoissonRegression(
  groupSizes,
  z,
  minimumEvents = 1,
  alpha = 0.05,
  sampleSize = 1e+06
)

Arguments

groupSizes	Vector containing the expected number of events under H0 for each test.
z	The control time divided by the time at risk.
minimumEvents	The minimum number of events needed before the null hypothesis can be rejected.
alpha	The significance level, or the type 1 error probability, which is the probability of rejecting the null hypothesis when it is true.
sampleSize	Sample size for the Monte-Carlo simulations.

Value

The computed critical value. The 'alpha' attribute of the result indicates the selected alpha.

Examples

groupSizes <- rep(1, 10)
computeCvPoissonRegression(groupSizes, z = 4)

EmpiricalCalibration documentation built on Sept. 30, 2024, 9:12 a.m.