Compute the minimum detectable relative risk

computeMdrr(
  object,
  exposureCovariateId,
  alpha = 0.05,
  power = 0.8,
  twoSided = TRUE,
  method = "SRL1"
)

Arguments

object: An object either of type SccsIntervalData as created using the createSccsIntervalData function, or an object of type SccsModel as created using the fitSccsModel() function.
exposureCovariateId: Covariate Id for the health exposure of interest.
alpha: Type I error.
power: 1 - beta, where beta is the type II error.
twoSided: Consider a two-sided test?
method: The type of sample size formula that will be used. Allowable values are "proportion", "binomial", "SRL1", "SRL2", or "ageEffects". Currently "ageEffects" is not supported.

Value

A data frame with the MDRR, number of events, time at risk, and total time.

Details

Compute the minimum detectable relative risk (MDRR) for a given study population, using the observed time at risk and total time in days and number of events. Five sample size formulas are implemented: sampling proportion, binomial proportion, 2 signed root likelihood ratio methods, and likelihood extension for age effects. The expressions by Musonda (2006) are used.

References

Musonda P, Farrington CP, Whitaker HJ (2006) Samples sizes for self-controlled case series studies, Statistics in Medicine, 15;25(15):2618-31